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CA 02288990 1999-10-27
WO 98/48785 PCTNS98/08684
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CA 02288990 1999-10-27
WO 98148785 PCTIUS98I08684
Sixth, if the genetic variants involved in polygenic disorders are common they
must be fundamentally different than those causing single gene disorders, i.e.
they
must result in only moderate changes in gene expression, since if they caused
major
alterations in gene expression they would cause single gene disorders
(Comings,
1998b). Based on these observations the inventors have suggested that by
forming
variable lengths of Z-DNA the common dinucleotide repeat polymorphisms
themselves play a major role in polygenic inheritance (Comings, 1998b). These
repeats are plentiful, the alleles are common, and the Z-DNA formed exert a
modest
effect on gene expression. A meaningful phenotypic effect can only be produced
by
adding together the effect of variants at many different functionally related
genes.
The implication of this for the present study was that when available the
inventors
were particularly interested in using short tandem repeat polymorphisms, and
any
single base pair polymorphism was likely to be in linkage disequilibrium with
one or
more of the alleles of short tandem repeats associated with the gene. Because
of the
latter the inventors assumed that any single nucleotide polymorphism, even if
it was
not associated with exons or promoters, could provide valuable information for
the
MAA technique. Seventh, to examine the additive effect of two or more genes
affecting a given neurotransmitter or functional group, a dummy variable
called
polygenic (PG), was set up based on simply adding the scores for each
individual
gene. Examining the effect of various combinations of genes within a given
functional group on the percent of the variance allowed the identification of
the subset
of genes that has an additive effect on a quantitative score. This set of
genes was then
used to form a traps polygenic score (TPG) that examined the additive effect
of genes
across different functional groups. The higher the PG or TPG score the greater
the
number of phenotypicaily relevant gene variants an individual possessed.
' Eighth, as in the previous studies (Coming et al., 1996b; Comings et al.,
1998a; Comings et al., 1998b) linear regression analysis was used to examine
the
additive gene scores. This allowed the calculation of r' or the percent of the
variance
that the genes being examined contributed to the trait, provided F to estimate
the
magnitude of the effect, and p for the significance of the effect. The fact
that a gene
accounts for only about I percent of the variance of a trait does not mean the
effect is
of little importance. Depending on a number of factors, this can provide for
up to 10
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WO 98/48785 PCT/US98/08684
percent (r) of the predictability of a phenotypic effect (Rosenthai and Rubin,
1982;
Ozer, 1985). Nineth, since the various behavioral scores have different ranges
of
magnitude, the cumulative r' values rather than the behavioral scores were
used to
provide precise comparison of the effect of the 29 genes across different
phenotypes.
Tenth, the inventors have chosen to primarily examine the attention deficit
hyperactivity (ADHD) score based on the DSM-IV (Diagnostic and Statistical
lLlanaral of the American Psychiatric Assn. lv 1994) criteria. Rather than
emphasizing a dichotomous diagnosis the inventors have used the . approach of
utilizing the whole range of the score by comparing the means of the ADHD
score in
individuals with different sets of genotypes. Eleventh, there were two
potential
approaches to use for the regression analyses: univariate and multivariate.
Far the
univariate analysis the scores for each gene were added to produce a single
score and
the correlation between this score and the QTVs determined. This was an
extension
of the 0, 1, 2 , 3 scoring approach. The advantage of this approach was that
the r
1 S. values increased only when one or more of the genes had an additive
effect on the
QTV. When genes were added that had a subtractive effect, the r decreased.
This was
conservative for the determination of final r and r2 values. However, since
all the
gene scores were combined into a single variable, the degrees of freedom was
always
1 despite the number of genes added. This was non-conservative in regard to p
values.
For the second approach each individual gene score was evaluated in a
multivariate regression equation and correlated with the QTV. Because the
degrees of
freedom increased as each gene was added, this was conservative for p values.
However, since both additive and subtractive genes contributed to r, this
approach was
2S non-conservative for r and r~ values which increased whether the genes were
additive
or subtractive in their effect.
The inventors have chosen the univariate approach for two reasons. First, it
is
more conservative since both the r and p values decrease when genes that do
not
contribute to the QTV are added. Second, because the genes are represented by
a
single degree of freedom regardless of the number of genes examined it has the
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potential of simultaneously examining the effect of hundreds of genes without
loss a
power.
_ Twelfth, to examine the hypothesis that most of the disorders that are
comorbid with ADHD share genes in common, the inventors also examined the
additive effect of the same set of 29 genes on the quantitative scores for
oppositional
defiant disorder (ODD), conduct disorder (CD), tics, learning disorders (LD)
and
other QTVs. For subjects over 14 years of age the inventors also examined QTVs
for
alcohol abuse/dependence, drug abuse/dependence and smoking. Thirteenth, the
examination of these comorbid disorders also allowed the inventors to examine
the
hypothesis (Coming et al.. 1996b; Comings, 1996a) that in addition to sharing
genes,
different comorbid disorders utilize some genes and combinations of genes that
are
unique to specific phenotypes. Fourteenth, to explore whether a level of
significance
of p < OS for the association between an individual gene and a given phenotype
had
any bearing on whether that gene had an additive effect, the inventors
examined the
relationship between the rz and p values of the 145 gene-phenotype
associations with
regard to whether they had an additive or subtractive effect on the phenotype
score.
Finally, after an initial pass using all the candidate genes had identified
those with an
additive effect, each additive gene was progressively added to a new TPG score
and
correlated with the QTV in question. This provided an estimate of the total r2
that can
be obtained using only those genes identified as contributing to the QTV in
question.
Advantages of an Additive Score The use of an additive gene score taps into
a number of the most unique aspects of polygenic inheritance -- the additive
effect of
different genes, the subtractive effect of different genes, the role of
heterosis and
epistasis, and the fact that while a number of different genes may be
contributing to
- 25 the same phenotype in any given individual or group of individuals only a
subset of
those genes may be present. Most importantly, this approach can compensate for
the
presence of different sets of genes in different individuals. Thus, many times
one
association study is replicated by some but not all subsequent studies. This
is due to
the fact that the gene is contributing to only a small percent of the variance
and one
set of genes may be involved in one group of subjects while another set is
involved in
a different group of subjects. Rather than implying that one or the other
result is
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incorrect, an equally valid conclusion is that different sets of genes can
produce the
same phenotype in the different groups of individuals. Since the additive
score
measures the effect of the total set of genes involved, the MAA technique may
be
much more reproducible across different groups of subjects. To give a specific
example, while variants of the DRD2 (TagI AI allele) (Coming et al., 1996b;
Blum et
al., 1998), DRD=I (48 by 7 repeat) (Lahost et crl., 1995), DBH (TagI B 1
allele)
(Coming et al., I 996b), and DATl ( I 0 repeat allele)genes (Coming et al.,
1996b;
Cook et al., 1995; Gill et al., 1997; Waldmaqn et al., 1996) have all been
implicated
in the etiology of ADHD, each may have a significant effect in some groups of
subjects but not others. However, if the physiologic effect is similar for
each gene
(alteration in dopamine metabolism) an additive score for all four might prove
to be
consistently and significantly higher in all groups of ADHD subjects versus
controls,
indicating it is a global genetic defect in dopamine metabolism rather than
any single
gene that is etiologically important in ADHD. The same rationale holds for
additive
genes across different neurotransmitter groups. A final strength of the
additive score
is that by using different combinations of genes a maximally informative set
can be
optimized to the identification of a given phenotype. Unlike the minor effects
of
individual genes, such an optimized set may provide great predictive and
diagnostic
value.
Hypotheses The first hypothesis is that, since polygenic disorders involve the
additive effect of multiple genes, the MAA technique will provide much greater
power in the identification of the genes involved than examination of the
genes one-
at-a-time, and the inclusion of only those genes that had an additive effect
would
maximize the r2 and p values. To test this the inventors have examined the
effect of
29 genes in 336 Caucasian individuals consisting of 274 with Tourette syndrome
and
62 controls rated for the severity of ADHD. The second hypothesis is, if two
independent samples are examined the inventors hypothesized that since
different
samples may utilize different sets of genes many but not all genes that were
additive
in one sample would be additive in the re-test sample, many but not all genes
that
were subtractive in one sample would be subtractive in the re-test, some genes
would
be additive in one sample and subtractive in the re-test sample, and for both
samples
the additive effect of multiple genes would be significantly greater than the
effect of
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WO 98/48785 PCT/US98108684
any single gene. The inventors also expected that the r'' fluctuations would
be greater
with a smaller N. To test this the 336 subjects were randomly divided into two
independent sets of 168 subjects with equal numbers of TS and control subjects
and
" equal numbers of males and females in each group. The correlations between
the PG
and TPG scores for the ADHID score was examined for each set. The third
hypothesis
is that, when different phenotypes are examined the inventors hypothesized
that
comorbid disorders would share some ADHD genes while some genes and gene
combinations would be unique to comorbid disorders. To test this the inventors
examined the additive and subtractive effect of all 29 genes against different
QTVs
oppositional defiant disorder, conduct disorder, tics, learning and other
disorders.
Test Subjects. The study group consisted of 336 unrelated, non-Hispanic
Caucasian subjects. Of these 274 had a diagnosis of Tourette syndrome (TS),
and 62
were controls. The TS subjects came from the Tourette syndrome Clinic at the
City of
Hope Medical Center. All meet DSM-IV criteria for TS and all were personally
interviewed by the inventor. While these are predominately the same subjects
examined in previous studies (Comings, 1995a; Comings, 1990), some new TS
probands were added when the DNA samples of some of the subjects used in the
prior
studies were depleted. The inventors have previously divided the inventors' TS
subjects into those with mild (grade 1, chronic tics too mild to treat),
moderate (grade
2, severe enough to require treatment), and severe (grade 3, very significant
effect on
some aspect of their life) (Comings and Comings, 1987b). Among the TS subjects
30% were grade 3, 62% were grade 2, and 8% were grade 1. TS and ADHD are
similar disorders and the majority of TS subjects that come to clinics have
comorbid
ADHD (Comings and Comings, 1987b; Comings and Comings, 1984). Of the TS
subjects 54% met DSM-IV criteria for ADHD. The presence of controls, and TS
subjects with and without ADHD make this group particularly well suited to
examining the association between the alleles of different genes and ADHD as a
continuous variable. The age of the TS subjects averaged 18.0 years (S.D.
13.2).
While the majority were older children and adolescents, 29% were 21 years of
age or
older. The mean age of the controls was 46.3 years (S.D. I5.38). Both the TS
subjects and controls have been described elsewhere (Comings et al., 1996b;
Comings, 1995a; Comings, 1994b; Comings, 1994a; Comings, 1995b).
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Each control and TS proband was required to fill out a questionnaire based on
the Diagnostic Interview Schedule (Robins et al., 1981 ), DSM-1II-R
(Diagnostic and
Statistical rLlanual of Mental Disorders, 1987) and DSM-IV (Diagnostic and
Stutistical Manual of the American Psychiatric Assn. IV, 1994) criteria for a
range of
disorders. The questions asked if the DSM-1V ADHD symptoms during childhood
and adolescence were never or rarely present (score = 0), occasionally present
(score =
1 ) or always present (score = 2). The oppositional defiant disorder (ODD) and
conduct disorder (CD) scales were also based on summing the number of DSM-IV
criteria for these disorders. To assess the presence of problems with learning
disorders, subjects were asked three questions. 1. Have you ever been placed
in an
educationally handicapped (EH), learning handicapped (LH) or learning disorder
(LD)
special class? 2. Have you ever been placed in a resource class? 3. Have you
ever
been told you had a seaming disorder? Each question was scored no = 0 or yes =
1 and
added to form the LD score. In California, placement in any of the above
special
I 5 classes requires a thorough evaluation by one or more educational
psychologists, and
the assessment that the student is two years or more behind his peers. The
alcohol
score (Comings, 1994b) was based on the summation of DSM-IV symptoms of
alcohol abuse/dependence. The tic score was based on the summation of the
presence
of a range of motor and vocal tics (Comings, 1995a). The specific questions
used for
the behavioral scores have been described in detail elsewhere (Comings et al.,
1996b;
Comings et al., 1998a; Comings, 1995a; Comings, 1990; Comings, 1994a: Comings,
1995b; Robins et al., 1981; Comings, 1995c; Comings, 1994c). The
questionnaires
were reviewed with each subject to ensure their accuracy. The accuracy,
utility and
sensitivity of a questionnaire based approach to symptom evaluation has been
demonstrated by others (Gadow and Sprafkin, 1994; Giayson and Carlson, 1991 )
by
comparing the use of such an instrument to an interviewer administration of
the same
structured instrument.
To examine the possibility that even sets of randomly scored alleles would
show a significant effect when only the additive genes were used, each gene
was
assigned scores of 0 and 2 on the basis of a rounding off random numbers in an
algorithm that produced the same final allele frequencies as those reported in
Table
65.
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The inventors utilized the dstattl0.exe program
(http:I/odin.mdacc.tmc.edu/anonftp/) to provide precise p values for the large
F values
obtained in the linear regression analyses. Linear chi square analysis was
used to test
for significant differences in the number of genes in subjects without versus
those
with ADI-ID. The SPSS Statistical Software from SPSS, Inc (Chicago, IL) was
used.
Results. Table 65 lists the name of the 29 genes, the type of polymorphism, a
a reference to the polymorphism including the technique for genotyping, how
the
genotypes were scored, the percent of subjects scored as 1 or 2, whether there
has
been verification of the method of genotype scoring in an independent set of
subjects,
and when available, the reference for this. To test hypothesis #l, the r, r2,
F and p
value of the progressive PG and TPG gene scores against the ADHD variable were
calculated by linear regression analysis (Table 66). In presenting the results
the
inventors have divided the genes into groups based on the neurotransmitter or
function
they affect.
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ICAI02288990 1999-10-27
WO 98148785 PCT/US98/08684
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CA 02288990 1999-10-27
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CA 02288990 1999-10-27
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Dopamine Genes. All five of the dopamine receptor genes and the dopamine
transporter gene (DA TI) were examined. Of these, the DRD2 (Coming et al.,
1996b;
Comings et al., 1991), DRD=t (Lahoste et al., 1996; Swanson et al., 1998) and
DA TI
(Comings et al., 1996b; Gill et al., 1997) genes have already been implicated
in
ADHD. The DRD~, DRD2. DRD~ and DA TI genes had the greatest effect on the
ADHD score, with r2 values ranging from 0.0022 to 0.0088. The respective p
values
ranged from 0.388 to 0.093. While the DRD.~ gene played no role in ADl-1D in
this
study, to conform with the prior literature the inventors scored this gene to
emphasize
the longer 6 to 8 repeat alleles, despite the inventors' own studies
indicating that the 2
alleles may also be important (Comings et al., 1997a).
The PG scores showed that the DRDI and DRD2 genes were additive in their
effect such that the sum of their individual r2 values (0.0041 + 0.0084 =
0.0120 was
the same as the observed PG score for D 1 + D2 (0.0125). The inventors termed
these
additive genes'. By contrast when the DRD3 gene scores were added to those of
the
D 1 + D2 PG score, the resultant r2 was less (0.0092) than for the DRDl plus
the
DRD2 gene. Thus, in regards to the ADHD phenotype, the inventors termed these
the
DRD3 a subtractive gene. Since the r2 continued to decrease with the addition
of the
DRD-1 gene (0.0071 ) this was also considered a subtractive gene. Since the
r'' values
again increased following the addition of the DRD~ and the DA TI genes, these
were
considered additive genes. When only the additive genes were included in the
PG
score. the total r2 was 0.0208, compared to 0.0138 when both the additive and
subtractive genes were included in the PG score.
Based on these results the inventors formulated the rule that if adding a gene
increased r~, increased F and decreased p, it was playing an additive role in
the
phenotype, while if it reversed these values, it had a subtractive effect on
the
phenotype. In the remainder of these studies the inventors have chosen this,
rather
than an arbitrary p value, as the criteria for including the gene in the PG or
TPG
scores. Thus, the inventors chose DRDl, DRD2, DRD~, and DA TI as the set of
additive dopaminergic genes (D) for the TPG score. Together these four
dopaminergic genes accounted for =2.08 percent of the variance of the ADHD
score
(p ~= 10.0081 ).
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Serotonin Genes. The r'' values for the six serotonin genes ranged from
0.0106 for the serotonin transporter (HTT) to 0.0002 for the HTR2C gene. When
the
HTRIA gene was added to the HTT gene there was an increase in the r2 value
from
0.0106 to 0.0143. The r'' value dropped when the HTR1D~3 gene was added,
increased
following addition of the HTR2A gene, decreased with the HTR2C gene, and
increased with the TD02 gene. This suggested that the HTT, HTRIA, fITR2A and
TD02 genes would be the optimal set of additive serotonergic genes. The r' for
these
genes was 0.0161. However, since the r~ value for the HTR2A gene (.0016) was
the
lowest of the four, the inventors also determined the r2 for the HTT, HTRlA
and
TDO2. Since this was higher (0.0167) the HTR2A gene was left out of the
additive set
of serotonergic genes. Leaving out the gene with the next lowest r' value did
not
further increase the additive r2. Together these four genes accounted for
1.67% of the
variance of the ADHD score (p~= 0.018). When the additive dopaminergic and the
serotonergic genes were added they accounted for 3.7% of the variance of the
ADHD
score (pt= 0.0004).
Norepinephrine Genes. The r2 values for the four norepinephrine genes
ranged from 0.0066 to 0.0122. In this case all four genes had a progressively
additive
effect on the total r'' value. Together they accounted for 4.22% of the
variance of the
ADHD score (pt= 0.00015), indicating that the norepinephrine genes played a
greater
role in ADHD than the dopamine or serotonin genes. or the dopamine and
serotonin
genes combined. In fact, the two noradrenergic a,~ receptor genes themselves
accounted for almost as much of the variance of the ADHD score {3.44%) as the
dopamine and serotonin genes combined. Further details of the role of these
adrenergic genes in ADHD are presented elsewhere {Comings et al., 1998a). When
the effect of the dopaminergic plus the serotonergic plus the noradrenergic
genes
(DSN TPG score) were combined they accounted for 7.41 % of the variance of the
ADHD score (pfi= 0.0000040).
Catecholamine Degrading Genes. The r' values for the ADHD score for the
two catecholamine degrading genes ranged from 0.0244 for the MA OA gene and
0.0098 for the COMT gene. When added (C) they accounted for 3.33% of the
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variance of the ADHD score (p J~= 0.00077). When added to the DSN score they
accounted for 10.05% of the variance of the ADHD score (p = 2.8 x I 0 8).
GABA Receptor Genes. The r2 values for the ADHD score for the two
GABA receptor genes ranged from 0.0082 for the GABRA3 gene to 0.0066 for the
GABRB3 gene. The r' value for the two combined was 0.0143 (~y= 0.028). When
combined with the DSN and the C scores they accounted for 10.87% of the
variance
of the ADHD score {p~-= 5.9 x I 0 9 ).
Other Neurotransmitter Genes. The r2 values for the four additional
neurotransmitter genes were 0.0017 for the cannabinoid receptor gene CNRI,
O.OI43
for the nicotinic cholinergic a4 gene CHNRA~t, 0.0058 for the NMDA receptor
gene
NMDAR~, 0.0011 for the proenkephalin gene PENK. When each was added to the
previous genes the percent of the variance of the ADHD score progressively
increased
to 11.09%, 11.78%, 12.26%. and 12.34%. For the entire set of neurotransmitter
related genes (NT), pt= 3.4 x 10-l0.
Androgen Receptor Gene. Since all of the behaviors the inventors have
examined are more common in males, the inventors also examined the AR gene.
The
details of the role of this gene in ADHD, ODD and CD are presented elsewhere
(Comings et al., 1998b). The r' value for the ADHD score for the AR gene was
0.011 1. When added to the prior genes they accounted for 13.27% of the
variance of
the ADHD score (p =i 5.5 x 10-t ~}.
Immunity Genes. Because of the recent interest in the potential role of
immune factors in ADHD (Warren et al., 1995) and TS (Swedo et al., 1998) the
inventors examined two immunity genes, the interferon-y and the CD8A genes.
Both
proved to be subtractive genes and were not included in the additive set.
' 25 Other Genes. Two other genes, presenilin-1 (PSl) and the corticotrophin
releasing factor gene (CRF), were examined. Although the rz values for both
were
similar (.0051 and 0.0050), only the CRF gene was additive. When all of the
additive
genes were combined they accounted for 13.62% of the variance of the ADHD
score
(p = 2.8 x 10-t t ).
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ADHD Score. FIG. 4 illustrates the effect of increasing numbers of variant
additive genes on the ADHD score. It showed a progressive increasing trend
from 1.0
for those with only 4 or ~ variant genes, to 25.0 for those carrying 15
variant genes.
The p value for linear chi square test of a progressive increase in the ADHD
score was
<10g.
FIG. 5 illustrates the r' values for the progressive addition of all 29 genes
tested. The slope of the curve to the immediate left of the gene label
provided an
indication of the relative contribution of that gene on the ADHD score. When
the
slope increased the gene contributed to the ADHD score. The greater the angle
of the
slope the greater the contribution. When the slope decreased the gene was
subtractive
and in comparison to other genes, played little or no role in the ADHD score,
despite
that fact the initial gene scoring always showed a greater phenotypic effect
for the
genotypes scored as 1 or 2. It was determines that the DRDI, DRD2, DRD~ and
DA TI dopamine genes contributed to the ADHD score while the DRD3 and DRD-l
genes did not. Of the serotonergic genes, the HTT, HTRlA and TD02 genes
contributed to the ADHD score while the HTR2A and HTR2C genes did not. The
four
noradrenergic genes contributed more to the ADHD score than the genes for any
other
neurotransmitter group. When all 29 of the additive and the subtractive genes
were
included the final rz value was 0.113, pf= 2.7 x 10 9.
The inventors examined the possibility that if the same set of genes were
assigned alleles on a random basis, and only the genes that gave a positive
correlation
with the ADHD score were used, the resulting r2 would be significant. The
results are
shown at the bottom of FIG. 5. The progressively additive effect of the rZ
values is
shown by empty squares and the additive effect of using only the positive
correlations
are shown by squares containing an x. The final r2 using both the additive and
subtractive genes was 0.0001. The final r2 using only the additive random
genes was
0.0004. Neither was significant. In addition, although the commutative r2 was
as
high as 0.008 at the random PENK gene, this fell back to 0.0004 when the last
random
additive gene (CDBA) was added.
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To examine the potential confounding role of sex, the inventors examined
males and females separately. Both groups showed significant elevations of r2
values
that were primarily diminished by the decreased power of the smaller numbers
of
subjects in each group.
Thus, tests of hypothesis # 1 showed that the additive effect of 29 genes gave
significantly greater r2 and p values than for any single gene, and the r2 and
p values
were maximized when only the additive genes were used. The results of the test
of
hypothesis #2 are shown in FIG. 6. This shows that the observed results
matched the
hypothesized results. Fourteen genes were additive in both sets, 2 were
subtractive in
both sets, 13 were different in both sets. In general, the genes that were
most additive
in the total set, such as DRDS, DATl, HTT, HTRIA, all four adrenergic genes
(DBH,
ADRA2C, ADRA2C, NT). MA OA, COMT, and AR genes were the genes that were
additive in both sets, and the genes that were most subtractive in the total
set, DRD3,
DRD=~, were subtractive in both sets. The genes that were most strikingly
different in
the two sets were the CNRl, CNRA=l, NMDARl, and the PENK genes. For example,
the NMDARI gene was quite additive in group 2 but subtractive in group 1.
However,
in both sets there was a progressive increase in the r2 with final p values
for both of
<f 5.0 x 10 4 despite the smaller N. When only the additive genes were
included, both
were significant at p < 10 G. When only those genes that were additive for
both
groups were used in the total set, r'' = 0.108, F = 40.82, p7'= 5.6 x 10 9.
When those
genes that were additive in both or only one set were used in the total set,
r~ = 0.11, F
= 43.71, p = 1.5 x 10 9. These results further serve to illustrate the
frequent difficulty
in replication when single genes are examined. By contrast, the MAA technique
produced robust results under a range of conditions. FIG. 4, FIG. 5 and FIG. 6
illustrate the testing of hypothesis #3 that comorbid conditions share genes
in
common with ADHD and use some unique genes.
ODD and CD The effect of the 29 genes on the r2 values for the ODD and
CD scores were examined. When both the additive and subtractive genes were
included the maximum r'' for the ODD score was 0.0775, p7'= 3.1 x 10 6, and
for the
CD score was 0.0225, p = 0.0059. Again, inspection allows the genes that
contribute
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to the ODD and CD score to be easily identified. When only the additive genes
are
utilized the maximum r' for the ODD score was 0.10, p7'= 3.2 x I0 8 and for
the CD
score was 0.075, p f =-~3.7 x 10 6. The most important genes for ODD were
DRDI,
DRD2, DRD3, DA TI. MITT, HTRIA, HTR2A, HTR2C, DBH, ADRA2A, ADRA2C,
MA OA, GABRA3, GABRB3, CNRI, CHRNA4, N~LIDARI, PENK, AR and CDBA. The
most important genes for CD were DRDI, DRD2, HTT, HTRlA, HTR2A, IITR2C,
DBt~ ADR2C, CHRNA-~. AR and CDBA. Thus, 20 of the 29 genes played a role in
ADHD, 20 genes played a role in ODD, and 10 played a role in CD. The ODD genes
were both similar and different than the ADHD genes, while the all the CD
genes
were the same as for the ADHD score.
LD and Tics. The effect of the 29 genes on the r~ values for the LD and tic
scores were examined. When both the additive and subtractive genes were
included
the maximum rZ for the LD score was 0.011, p = 0.054, and for the tic score
was
0.014, p~= 0.029. When only the additive genes were included the maximum r2
for
the LD score was 0.043, p = 0.0005 and for the tic score was 0.061, p =
0.00005.
Again, inspection shows that the genes involved in the tics score were DRDI,
DRDS,
HTRIA, HTRlD(3, HTR2C, TD02, DBH, ADR2C, COMT, GABRA3. CNRI and
CNRNA-~. The genes involved in the LD score were DRDl, HTR2C, TD02, DBH,
ADRA2A, ADR2C, MA OA, CNR~ and CNRA4. Thus, 12 genes were involved in tics
and 9 were involved in learning disorders.
Alcohol or Drug Abuse/Dependence and Smoking. The results of testing
the 29 genes against an alcohol abuse/dependence score in 164 subjects over
age 14
were examined. When the N is smaller there is a wider fluctuation in the r2
values.
Thus, when adults only were examined the additive genes are easily
distinguished
2~ from the subtractive genes. For the alcohol abuse/dependence QTV the final
r' using
both sets of genes was 0.049, p/= 0.0044. However, when only the additive
genes
were used the r2 value was 0.14, p = i 7.5 x 10 b. As discussed below, many of
these
genes or neurotransmitter systems have been previously implicated as playing a
role
in alcoholism.
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The results with the drug abuse/dependence QTV (exclusive of smoking)
were similar to those for alcohol abuse/dependence. However, since drug abuse
was
less prevalent than alcohol abuse/dependence the magnitude of the line for the
r2
values was lower. The major differences were that the tITRID~3 and HTR2A,
ADRA2C genes were subtractive in drug abuse/dependence but additive in alcohol
' abuse/dependence, HTR2C and NT were additive in drug abuse but subtractive
in
alcohol abuse, and INFG was subtracting in alcohol abuse but neutral in drug
abuse.
Smoking was more common and these results were also examined. Here the QTV
was scored as ever smoked for a month more = 1, never smoked = 0. In this
study of
subjects the DRD2 gene played a major role producing a r2 value of 0.055 by
itself.
Other additive genes were NMDARI, PENN; AR and INFG. The final r2 = 0.10,
p = 0. 0003 8.
Other QTVs. The inventors also examined several other QTVs for behaviors
that are commonly comorbid with ADHD. The total r' values, p values and major
additive genes were as follows: mania -- r2f= 0.0721, p~= 5.8 x 10-6, DRDI,
DRD2,
DRD3, DATl, HTRID/3, HTR2C, TD02, ADR2AC, GABRB3, CNRI, CHRNA:~;
schizoid -- r'' = 0.058, p = 8.3 x 105, DRD2, DRD3. HTR1D(3, ATRIA, HTR2A,
HTR2C. DBH, CNRI, CHNRA.~; OCD --.046, 1.0 x 10~, DRDl, DRD2. HTRID(3,
GABRB3. CNRI, NMDARl; general anxiety -- r'' = 0.032, p = 0.001, DRDI, DA TI,
HTRID~3, CHRNA~, PENK; and major depression -- r' = 0.0271, p = 0.0025, DRDI,
DATl, HTT, HTRID(3, TD02. CNRl, CHRNA;I, NMDARI.
Number of Genes Involved. To examine the relative distribution of the
number of variant genes in subjects with and without a specific dichotomous
phenotype the inventors have plotted the number of additive variant genes in
subjects
with no DSM-IV criteria for ADHD versus those who fulfill DSM-IV criteria for
ADHD (FIG. 7). The mean number of variants was approximately 10 in those with
no ADHD symptoms and approximately 12 in those with a diagnosis of ADHD. In
those without ADHD symptoms the distribution ranged from 3 to I4 while for
those
with a diagnosis of ADHD the distribution ranged from 7 to 18.
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Additive Effects Versus p Values. From the present study the inventors
plotted 145 rz and p value results for all 29 genes versus 5 quantitative
scores (ADHD,
ODD, CD, LD and tics} and identified those which were additive versus those
which
were subtractive for the phenotype in question. This shows that when genes are
examined one-at-a-time a p value arbitrarily set at <0.05 has little relevance
to
whether a gene is additive and contributes to a phenotype, or subtractive and
does not
contribute. When examined individually, the majority of additive genes had p
values
of greater than 0.05 and many had p values between 0.10 and 0.40. While a p
value of
greater than 0.45 universally identified subtractive genes, in the range of p
values
between 0.12 and 0.45 some were additive and some were not.
Discussion. Concern has frequently been voiced about the difficulty of
identifying the genes involved in polygenic disorders. In the present study
the
inventors have attempted to turn the single major characteristic of polygenic
disorders,
the additive effect of many different genes. to an advantage by examining the
additive
effect of multiple candidate genes. Since the additive and subtractive effect
of
multiple association studies was used, the inventors have termed this a
Multiple
Additive Associations technique. When using linkage techniques to examine
complex
disorders, it has been suggested that quite stringent levels of significance
must be used
to avoid false positives (Larder and Krugyak, 1996). Because of this
recommendation, in Table 66 and FIG. ~ and FIG. b, the inventors have
presented full
rather than truncated p values. This shows that using the MAA technique, 29
genes
and only 344 subjects, p values of up to I 0 ~ ~ were readily attained.
Since this is a new technique it is reasonable to carefully scrutinize the
statistical approach that was used. It is clearly open to several potential
criticisms.
The first is that if the studies were only done in a single group of subjects,
that when
the gene scoring is set to maximize than effect on a given phenotype, simply
adding
those gene scores together would result in higher and higher r'' values.
Because of
this, the inventors were careful to validate the gene scoring in a separate
group of
subjects studied either by the inventors or by others in the literature. As
shown in
Table 6~, all of the genes fulfilled this criteria. For some genes there was
only one
viable scoring. For example. for genes with a two allele polymorphism. when
the
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frequency of the I allele is low and thus there are very few with a 11
genotype, there
is no alternative to a scoring of 22 = 0 and I 1 or 12 = 2.
A second potential criticism is that if enough genes are examined and if only
the additive genes are used, it would be possible to obtain significant
results on a
random basis. To examine this possibility, the inventors set up a second set
of scores
in which each 'dummy' gene for each individual was randomly assigned a score
with
- the frequency being set to match those found in the observed results. The
effect of
progressively adding both the positive and negative results is shown in FIG. 5
(open
squares). By the end of the 29th gene the positive versus the negative results
had
canceled each other and the final r2 was 0.001. More importantly, when only
the
random genes that gave positive correlations were used, the commutative r2
value
increased to a maximum of 0.006 for the PENK gene. However, after the final
two
positive random genes were added, the r'' value had dropped back to 0.004, p >
0.25.
This subtractive effect was presumably due to the fact that even though the
effect of
the CDBA and CRF random scores were positive the r2 values were in 0.0000 to
0.0003 range and produced a subtractive effect. While multiple iterations of
the same
process using only those with a positive r could undoubtedly have eventually
produced a significant result, the important point is that when candidate
genes are
used that have been independently verified in other studies to be associated
with a
phenotypic effect the final p values are dramatically higher (10 5 to 10 ~~}
than with
the random genes.
A third potential criticism is that even though the results are clearly
greater
than expected from random scoring, using univariate regression and including
only
the additive genes still inflates the resultant p values. There are two
approaches to this
quite reasonable criticism. The first is that the gold standard of all gene
studies is
replication or the examination of multiple independent sets. The inventors
suggest
that a reasonable standard for the MAA technique is that the additive set
should
include all the genes shown to be additive in any study with a reasonable N.
For
example, in the inventors' own test-retest study. the chosen set of additive
genes could
include those that were additive in either set. Since some of these would be
subtractive in some studies this would help to avoid the artificial inflation
of r' and p
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values. When this was done the final r2 was still a substantial, 0.11, and
highly
significant, p < 2.0 x 10 ~~.
Reasons for a Subtractive Effect. A moderate subtractive effect can be due
to the fact that the percent of the variance accounted for by the gene in
question is
relatively small. For example, if the 11 genotype of gene A had a very small
effect on
the ADHD score, another gene could be responsible for non-A11 subjects having
a
higher ADHD score than A11 subjects. As a result non-A1 1 subjects could still
have
significant symptoms, resulting in subtractive effect of the gene. A second
reason for
a subtractive effect can occur when a number of different phenotypes are
examined.
For example, the 11 genotype of a gene may be significantly associated with
ADHD
while the 22 genotype may be associated with a different phenotype such as
depression. As a result, when the scoring is such that the 1 1 genotype = 2,
this could
result in an additive effect for an ADHD score but a subtractive effect for a
depression
score.
1 S Implications for Genetics Studies of Complex Disorders. The inventors
believe these studies have a number of implications not only for psychiatric
genetics
but for the genetics of complex polygenic disorders in general.
1. The power or examining marltiple as' opposed to single genes. These
studies provide some insight into the problems with the reproducibility of
association
studies that examine genes one at a time. This is most likely due to the fact
that
multiple genes are involved, no single gene is critical, and each gene
contributes to
only a small percent of the variance. As a consequence, when genes are
examined
one-at-a-time, one gene will reach significance in one study but not another.
Rather
than being the source of endless frustration (Moldin, 1997), or even thoughts
chat
none of the studies are correct, this should be viewed as the expected outcome
in the
genetics of complex disorders (Comings, 1998x). The percent of the variance
and p
values for the results of 145 association studies using the genes and
behavioral traits
the inventors have reported here (except for alcohol abuse/dependence). This
shows
that the majority of the additive genes would have been rejected if the usual
criteria
for single gene significance of p < 0.05 was used. It also demonstrates why it
has
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been so difficult for two laboratories to agree when the p < 0.05 cut off is
used. If the
associations producing an r2 value of 0.0008 to 0.025 were actually
independent
studies from different laboratories, only 6 or 20% would have been considered
to be
positive and the remaining 24 would have been considered negative 'non-
replications.'
However, by the MAA technique all these associations are utilized.
Since the single most distinctive characteristic of polygenic inheritance is
the
- involvement of multiple genes, the inventors have suggested that it is
necessary to
take advantage of this characteristic to identify the genes involved in
complex
disorders (Comings et crl., 1996b; Comings, 1996a; Comings, 1998a; Comings,
1998b). The present studies validate this concept and indicate that the power
in
polygenic inheritance comes from the examination of the additive effect of
multiple
genes. The inventors also suggest that the proper criteria for determining
whether a
gene plays a role in a given disorder is whether it has an additive rather
than a
subtractive effect on the relevant quantitative score. This can be easily
identified by
presenting the results as shown in Table 66, or in FIG. S and FIG. 6.
2. Association studies as the optimal approach to polygenic inheritance.
Presently the most popular methods used to identify the genes in complex
disorders
consists of whole genome screening using lod score linkage, affected sib pair
techniques, or haplotype relative risk techniques. While this has great
inherent logic,
replication is often difficult and many of the above genes that the inventors
have
found to play a role in ADHD or TS based on the additive gene approach have
been
'excluded' on the basis of such studies (Pakstis et al., 1991 ). While
association studies
using unrelated controls and significantly affected probands uniquely
possesses the
power to detect the small effects characteristic of polygenic inheritance
(Risch and
Merikangas, 1996; Comings, 1998a), they are also plagued with problems of non-
replication. The MAA technique of examining the additive effect of multiple
genes
utilizes the power of association studies and potentially avoids the problems
of non-
replication. This is because it replaces the criteria of single gene
significance at
p < 0.05 with a less stringent parameter of having an additive effect on the
cumulative
r''. This approach is also more practical since the identification of single
unrelated
probands and unrelated controls, examined with the same instruments, is much
easier
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than the identification of affected sib-pairs. In an effort to identify the
genes involved
in a number of different disorders, DNA banks have been set up consisting
exclusively of affected and unaffected sib pairs. Both the speed of case
ascertainment
and the gene f nding power of these banks could be dramatically increased by
including single probands and a comparable number of unrelated controls
screened
with the same test instruments. The efficiency of this could be maximized by
allowing different banks to share the same, publicly available set of screened
controls.
As with the CEPH (Centre d'etude du Polymorphisme I-Iumain) (Dausset et crl.,
1990)
samples, it would be further enhanced by requiring that after a given period
of time,
all the genotyping results be made public so the results of multiple
investigators could
be collated.
The results provide an additional reason why association studies are more
powerful than family based linkage techniques. This shows that even when these
two
optimally disparate groups are used the total number of variant genes is only
modestly
different (mean of 10 versus 12). This suggests that when comparisons are made
within families, as occurs with lod score, sib pair, haplotype relative risk,
and TDT
analyses (Spielman and Ewens, 1996) which actually or implicitly compare
affected
to unaffected, the differences will be even less. When the problem is
compounded by
examining only one gene at a time, extremely large numbers of subjects are
required
to detect relevant genes even at borderline lod score or p values. As an
example, to
date, even with very large numbers of families and sib pairs, the majority of
the
linkage studies of behavioral disorders have identified no specific genes.
This
contrasts with the results in FIG. 5 and FIG. 6 where the use of less than 350
subjects
provisionally identified 20 specific genes for ADHD and 15 for alcohol
abuse/dependence and produced data to allow an estimation of the relative
importance
of each gene for each phenotype.
3. The problem of hidden ethnic stratification. One of the most
frequently voiced concerns about association studies is the potential problem
of
hidden ethnic stratification. The MAA technique minimizes that concern for the
following reason. While hidden : ethnic stratification could play a role when
examining single genes, it is unlikely that the frequencies of all the genes
will vary in
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the same direction. As the number of genes examined increases the potential
effect of
hidden ethnic stratif canon decreases.
4. The issue of examining many genes. Another one of the objections
often raised in gene searches and association studies is that if one looks at
enough
genes, some will be significant by chance alone. This is especially valid when
genes
are examined one at a time and the end point is a p value of < 0.05. However,
the
- additive multiple associations technique is independent of the p value of
individual
genes and asks instead whether a gene in the background of other genes
contributes to
the r' of the phenotype in an additive or a subtractive fashion. By
emphasizing this
characteristic of polygenic disorders the technique inherently and naturally
accommodates the examination of large numbers of candidate genes. This was
seen
in the up and down fluctuation of the summary r' values for the tic, LD and
alcohol
abuse/dependence scores. This suggest that hundreds of genes could have been
added
and the final r2 values would continue to fluctuate. However, the more genes
that are
examined the greater the number of additive genes that are identified and the
greater
the final r2 when this subset of genes is utilized. Thus, in contrast to the
one-gene-at-
a-time approach where power is lost with each additional gene examined, the
power
of the additive multiple associations technique increases as more genes are
examined.
5. Comorbid disorders utilize related sees of genes. Even though ADHD
was the primary phenotype the inventors examined, there was an additive effect
of
many of the ADHD genes on related phenotypes such as oppositional defiant
disorder,
conduct disorder, alcohol abuse/dependence, drug abuse/dependence, smoking,
OCD,
mania, schizoid behaviors and others representative of RDS behaviors.
6. Comorbid disorders utilize different sets of genes. In addition to using
similar sets of genes, comorbid disorders may have a different phenotype
because they
use a different set of the genes. Two observations suggest the latter. First,
even when
only the additive genes were used the final rz values were lower for the ODD,
CD,
LD, tic and other scores than for the ADHD scores. If the inventors assume
that the
total genetic contribution to each of these phenotypes is similar to that for
ADHD,
then genes other than the ones examined here would have to be involved to
bring the
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rZ values to comparable levels. This indicates that in addition to sharing
genes, these
comorbid disorders also use unique genes. Second, the slope of the lines for
the
cumulative r2 values using both the additive and subtractive genes show that
the
comorbid disorders use distinct sets of genes or distinct genotypes. For
example, the
MA DA gene was additive for the ADHD score but subtractive for the alcohol
abuse/dependence score. This suggests that the MA OA gene was less important
for
the alcohol abuse/dependence score than for the ADHD score.
7. Different phenotypes may use different genotypes. An alternative to
using different sets of genes is that different phenotypes may use different
genotypes.
Again using the MA OA gene as an example, instead of it being less important
in the
alcohol abuseldependence score, it may utilize different genotypes. As a
further
example, depression and aggression have often been linked to low CNS serotonin
levels (Brown et al., 1982; Coccaro et al.. 1989) whtle obsessme-cnmnnlcivP
behaviors have been linked to increased CNS serotonin or receptor sensitivity
(Inset et
al., 1985). Thus, it would not be surprising if a given genotype was
positively
associated with depression/aggression but negatively associated with obsessive
compulsive behaviors. These observations raise the question of whether gene
scoring
should be individualized for each phenotype. The inventors suspect that it
should, but
this would require that each such scoring be validated in an independent set
of
subjects.
8. Use of different polymorphisms. In this study the inventors used only
one polymorphism per gene. The use of other polymorphisms, or the combination
of
several polymorphisms into haplotypes, could potentially increase the rz
values.
Thus, the total rz values observed here may actually significantly
underestimate the
true contribution of these genes to the respective phenotypes.
9. Comparative effect of different individual genes in different
phenotypes. One of the most powerful aspects of the MAA technique is its
ability to
identify the relative importance of different genes. This is possible because
all genes
are examined in the same group of subjects. An example is the role of the AR
gene.
The increase in the slope of the r' curve immediately to the left of the AR
gene for the
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ADHD (FIG. 5), ODD and CD scores indicates the relative importance of this
gene in
all three conditions. By contrast, the AR gene was subtractive and played no
role for
the tic and LD scores and was neutral for the alcohol abuseldependence score.
10. Comparative effect of different groZrps of genes in different phenotypes.
Another aspect of the MAA technique is its ability to identify the important
role of
groups of genes affecting specific neurotransmitters for different phenotypes.
Thus,
the set of four adrenergic genes played a significant role in the ADHD score.
Together they accounted for 42% of the variance of the total r2 score based on
the
additive genes. By comparison the serotonergic genes accounted for only 9.5%
of the
I0 total. This is in agreement with the many studies strongly implicating
defects in
norepinephrine metabolism in ADHD (Halperin et al., 1997; Yliszka et al.,
1996;
Arnsten et al., 1996). By comparison, the opposite trend was seen for conduct
disorder. Here the serotonergic genes accounted for 30% of the variance of the
total r2
based on the additive genes, while the adrenergic genes accounted for only
13%. This
is in agreement with tile many studies indicating defects in central serotonin
metabolism in antisocial behaviors (Brown et al., 1982; Lidberg et al., 1985;
Coccaro
et al., 1997).
ll. ~Llultiple tiers ,for association studies and implications .for the
publicatioh of single gene studies. The results of the MAA technique suggest
that
association studies should be conducted on multiple tiers. In the first tier,
there is a
need for preliminary studies of one-gene-at-a-time to determine which
polymorphisms
may be useful and how they should be coded. However, the inventors suggest
that the
important finding for monogenic studies is how the genotypes of a polymorphism
of a
given gene should be scored. Depending upon the size of the sample, even with
p
values of > 0.05, this information can be of value. Since most single
association
studies that gave an r2 of > 0.005, or an r of > 0.07 were additive, when the
N is
adequate this could be the criteria for publication rather than p < 0.0~.
The second tier would be the use of the MAA technique utilizing aII
reasonable candidate genes. These results are represented by the line for
additive and
subtractive genes in FIG. 5 and represent the non selective inclusion of all
genes. The
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third tier would be the development of a 'new model' for a given phenotype
based on
using only the additive genes. This is represented by the line for the
additive genes in
FIG. 5. The final tier would be independent replication studies identifying
new
additive genes and continually testing and retesting the resultant 'new
models' based
on the accumulating set of additive genes.
12. Genes for alcoholism, drug abuse and smoking. While twin and
adoption studies clearly indicate that at least some forms of alcoholism, drug
abuse
and smoking are strongly genetic, the identification of the specific genes
involved,
based on one-gene-at-a-time approaches, has not been outstandingly
effectively. In
fact, the combination of replication and non-replication (Noble, 1993; Blum et
al.,
1995b) of the reported association between the Taq A1 allele of the DRD2 gene
and
alcoholism (Blum et al.. 1990b), has been one of the contributors to
skepticism about
the power of association studies. The results of using the MAA technique for
the
alcohol abuse/dependence score provides insight into many of the problems
concerning the molecular genetics of alcoholism. First, the steep slope of the
r'' curve
to the left of the DRD2 gene indicates that the DRD2 gene, using the Taq A l
/A2
polymorphisms does play a role in the alcohol score in this group of subjects.
However, it accounts for only 10% of the total r2 score and since the total
r'' score was
only 0.14, this gene accounted for only 1.4% of the total variance of the
alcohol
abuse/dependence score. This is fairly typical of the additive genes and is a
major
reason why replication using the one-gene-at-a-time approach is so difficult.
Second,
the dopaminergic, serotonergic and gabaergic sets of genes all played an
important
role in the alcohol score. Thirdly, the power of the MAA technique is
illustrated by
the highly significant final r' value (p~= 7.5 x 10 b). To the inventors'
knowledge, for
a comparable N, this far exceeds any results ever reported using the one-gene-
at-a-
time approach. Fourthly, while the focus of the study was on ADHD rather than
alcoholism, the numerous genes that were shared by ADHD and alcoholism (DRDI,
DRD2. DA TI, HTT, HTRIA. DBH, ADRA2C, GABRB3, CNRl, NMDA and CRF) is
consistent with the high frequency of ADHD in alcoholism and alcoholism in
ADHD
(Loney et al., 1981; Cantwell, 1972; Alterman et al., 1983; Tarter, 1988;
Biederman
et al., 1995).
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The results for smoking showed a strong effect of the DRD2 gene. However,
since these subjects had Tourette syndrome, which itself is associated with
the DRD2
gene (Comings et al., 1991 ), the association with smoking may be enhanced
over
what it would be for subjects who smokers only. Despite the primary focus on
genes
for ADHD the results for the alcohol abuse/dependence scores show that many of
the
genes identified for ADHD have been previously been implicated in the
iitcrature as
being candidate genes for alcoholism.
l3. Studying multiple phenotypes in a single group of subjects'. A common
strategy in genetic studies of behavior is to collect individuals with a
'pure' disorder,
with few or no comorbid conditions, and to score subjects dichotomously into
those
having or not having that diagnosis. Such studies may lose power for two
reasons.
First, individuals with many comorbid conditions are more likely to have
higher
overall levels of genetic loading, thus increasing the power for identifying
genes.
Second, screening for a wide range of behaviors allows a number of different
disorders to be examined in the same group of subjects. This is illustrated in
the
present study. Since TS is associated with many comorbid conditions (Comings
and
Comings, 1987x; Comings, 1995x; Miller et al., 1996) it was possible to test
for the
genes specifically associated with a number of disorders, all in one group of
subjects.
The results indicated it was still possible to identify unique combinations of
genes for
the different phenotypes. This suggests the use of structured interviews to
make
multiple DSM-IV diagnoses and allow the development of multiple quantitative
traits
in a single group of subjects with high rates of comorbidity, may be a much
more
efficient approach to finding genes for a range of disorders than accumulating
many
DNA data bases, each for a specific disorder.
l;t. Risk factors verszrs diagnosis and predictability. One of the common
assumptions about polygenic inheritance is that the genes involved act only as
risk
factors and, in contrast to single gene disorders, it will not be possible to
use genetic
tests in a diagnostic fashion. However, as the percent of the variance
explained by the
additive effect of genes increases, r also increases, and with it there is an
increase in
predictability. The final r of 0.37 for the ADHD score indicates that by using
this set
of genes the predictability of ADHD score was up to 37% over what it would
have
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been with no genetic information. With the addition of still more genes, r
values of
twice this magnitude could be obtained. With the availability of DNA chip
technology, the number of genetic tests that have to be performed is no longer
an
issue. As shown in FIG. 7 while the curves for the distribution of the number
of
relevant genes for individuals with no symptoms of ADHD versus those who meet
DSM-IV criteria for ADHD shows much overlap, the differences that are present
are
highly significant. Doubling or tripling the number of additive genes may
eventually
produce largely non-overlapping curves. The inventors contemplate that the
diagnostic power will increase with added genes.
l~. Replication. Just as replication is the gold standard in linkage and
association studies, it should also be the standard for replication of the MAA
technique. Variations in the number of subjects, composition of probands by
severity,
the ratio of controls versus subjects. the range of the quantitative scores,
and other
factors would ail be expected to alter the final r2 values, independent of the
effect of
I S specific genes themselves. Aside from these factors the inventors would
expect
varying levels of replication. The highest level is one in which the identical
sets of
genes are additive or subtractive in different groups of subjects and the
slopes of the
curves are similar. Since the ability of different genes to produce a similar
phenotypic
effect is one of the characteristics of polygenic disorders, the inventors
expect that this
level of replication is both unlikely and unexpected, and that different
studies will
show that different sets of genes and different slopes of the curves are
involved. This
was verified by the split sample tests (FIG. 6).
A second and more realistic standard of replication is one in which most but
not all of the additive genes for one group are also additive for the
replication group,
and in which the r' values and significance levels progressively increase as
more
genes are included, but the slopes of the curves for different genes may vary.
At this
level, it would also be expected that the relative importance of groups of
genes be
replicated. Thus, the present studies showing that adrenergic genes are of
relatively
greater importance in ADIJD while serotonergic genes are of relatively greater
importance in conduct disorder should be replicated. This was verified in the
split-
sample tests where the importance of the noradrenergic genes in ADHD was
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replicated in both sample. The ultimate level of replication of the MAA
technique
would occur if it accelerated the identification of genes involved in
polygenic
disorders.
1 G. Technical issues. While most of the important aspects of the additive
. 5 multiple associations technique have been covered. there are several
aspects that
deserve emphasis. The inventors feel a quantitative trait is preferable for
several
- reasons. It requires that the controls be evaluated for the same trait as
the subjects.
This is important because in the inventors' experience, both the controls and
subjects
may have a wide range of scores. In addition the power of the analysis is
greater
when the full range of the quantitative trait is examined. For maximum power,
the
number of controls should approximate the number of subjects, or the number of
subjects with low scores should approximate the number with high scores.
Although the final r and r2 values are independent of order, to clearly
identify
subtractive genes it may be necessary to change the order of entry of adding
genes
such that those with the greatest positive associations are entered first. If
the initial
genes do not produce an adequate increase in the r or r'' values, it would be
difficult to
identify subtractive genes.
~ 7. Therapeutic and pharmacologicul implications. A final aspect of the
MAA technique, based on its ability to both identify the genes involved and to
weigh
their relative importance, is its potential power to guide therapeutic and
pharmacological interventions. For example, the observation that four
adrenergic
genes accounted for 40% of the final r2 value for the ADHD score suggests that
drugs
acting on the adrenergic a,, receptors would be of value in the treatment of
ADHD.
Clinical studies in fact support the effectiveness of clonidine and
guanifacine,
adrenergic a, receptor agonists, in the treatment of ADHD (Hunt et al., 1985).
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EXAMPLE 14
CHROMIUM PICOLINATE AND CHROMIUM NICOTINATE DIETARY
SUPPLEMENTATION TO TREAT OBESITY
In this study, chromium picolinate was of primary interest, but it was thought
that preliminary data on chromium nicotinate, tested under the best conditions
{combined with exercise training), would be valuable as well. The present
study of
the effects of chromium supplementation on young, obese women had two
objectives.
First, to determine if chromium picolinate supplementation alone favorably
alters
body weight and composition, glucose tolerance, and plasma lipids, and whether
these
effects could be augmented with exercise training. Second, to provide data on
the
effectiveness of chromium nicotinate supplementation combined with exercise
training.
METHODS. Subjects were forty-three healthy, sedentary, obese females.
Various statistical cut-off definitions are used for obesity, but for the
purpose of this
investigation obese was defined as higher than the recommended body fat
percentage
for young women, recommended being 20-25% body fat (Blackburn et al., 1994).
The inventors recognize that a portion of the inventors' population was only
mildly
obese. Prior to acceptance, questionnaires were used to determine the health
status
and activity patterns of the subjects. None of the subjects documented any
health
problems, nor medication for such conditions. Age ranged from 18 to 35 yrs.
with a
mean of 24.4 ~ 0.70 yr. Initial weight ranged from 50.8 to 96.1 kg. with a
mean of
71.3 ~ 1.9 kg. Percent body fat as determined by hydrostatic weighing ranged
from
25.0% to 45.0%, with a mean of 33.0% ~ 0.91. Initial V02max values ranged from
1.53 to 3.30 L/min, with a mean value of 2.38 ~ 0.13 L/min. Each subject was
informed of the potential risks and benefits associated with participation
before
signing an informed consent document. The study was approved by the
Institutional
Review Board of The University of Texas.
Experimental design. Subjects were randomly assigned to one of four
treatment groups: chromium picolinate supplementation without exercise
training
(CP), exercise training with placebo (E/P), exercise training with chromium
picolinate
supplementation (E/CP), and exercise training with chromium nicotinate
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supplementation (E/CN). The treatment period was nine wk. The effects of stage
of
the menstrual cycle were not controlled. Although pre- and post-testing were
conducted at different stages of the presumed 28-day menstrual cycle, the
effects of
this, if any, were randomized among all study participants and thus should not
have
influenced the data.
Chromium supplements and placebo tablets were prepared by Shaklee, Inc.,
- USA (San Francisco, CA) and shipped to the investigator in coded bottles of
100
tablets each. Subjects were given 16 tablets each wk containing chromium
picolinate
(200 pg), chromium nicotinate (200 pg) or placebo (inert ingredients).
Subjects were
given verbal instructions to take one tablet each morning and evening
throughout the
treatment and post-testing period, resulting in a dosage of 400 ~g daily for
those
receiving chromium supplements. Subjects were asked to return unused tablets
each
wk. Compliance was measured by counting returned tablets. There were no
problems
with compliance.
Exercise training consisted of a cross-training program with several
components. The first component was step aerobics, which is a type of aerobic
dancing utilizing upbeat music, a bench of 12 to 24 inches in height, and an
instructor
to guide participants through a series of moves designed to provide a full-
body
aerobic workout. These one-hr classes were attended twice a wk by each
exercising
subject and were taught by certified instructors. Subjects were allowed to
pick their
own bench height. Instructors gradually increased exercise intensity as the
study
progressed.
The second component was cycling. Cycling exercise was conducted twice a
wk for 30 min at a target heart rate of 75% - 80% maximal heart rate
(determined
during the initial VOZmax test). Universal Aerobicycles (Universal Gym
Equipment,
Inc., Cedar Rapids, Iowa) were used. Target heart rate was programmed into the
ergometer at the beginning of each session and monitored during exercise with
an
onboard computer adjusting resistance to maintain target heart rate.
The third component was resistance training. Resistance training was
conducted twice a wk with The Powercise Fitness System (TruTrac Therapy
Products,
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Inc., Temecuia, California). Five separate machines were used in which
resistance is
set by an electronic braking device. This was a "double positive" system
eliciting
concentric-only contractions from agonist/antagonist muscle groups. The five
machines used exercised all major muscle groups of the upper and lower body.
Each
subject's maximum strength was determined initially by lifting progressively
heavier
weights with each repetition (10 lb. increments) until the load could no
longer be
lifted. Once maximal strength had been determined, workout weights were
automatically set at 50% maximal strength, and the subject was guided through
a
workout consisting of three sets of 15 repetitions on each machine. Subjects
followed
a pacing light which allowed approximately I.5 sec per concentric contraction.
Rest
time between sets was 25 to 28 sec. Time between machines was not tightly
controlled. Subjects were encouraged throughout the study to increase the
weight
lifted while still completing three sets of fifteen repetitions, and these
increases were
documented. Subjects were also instructed to do three sets of twenty
"abdominal
crunches" (sit-ups).
All training session were supervised by at least one investigator. Attendance,
heart rates during cycling exercise, and resistance training parameters
(weight lifted
and completion of repetitions and sets) were recorded. Subjects were asked not
to
alter their diet during the course of the study.
Experimental protocol. During the wk prior to initiation of treatment,
subjects were weighed on two separate occasions using a Health-o-Meter scale
(Continental Scale Corporation, Bridgeview, IL) with a sensitivity of t 200
g., and
subjected to body composition analysis via hydrostatic weighing (Behnke et
al.,
1974). VOZmax was determined on a treadmill using a modified Burke protocol,
with
2~ inspired volumes and expired gasses measured as previously described for
the
inventors' laboratory (Yaspelkis et crl., 1993). Fasting blood samples were
drawn on
two separate days, and an oral glucose tolerance test (OGTT) was conducted as
subsequently described.
During the nine wk treatment period, supplementation and exercise training
were administered as detailed above. In the ninth wk of treatment, all pre-
test
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measurements were repeated. Tests were administered in the same order both pre-
and post-treatment. Supplementation and exercise training continued through
the
post-testing period. All subjects involved in exercise training participated
in a step
aerobics session two days prior to the post-OGTT and rested on the day prior
to the
test, resulting in approximately 40 h between the last bout of exercise and
the OGTT.
Sample collection and analyses. Plasma for hormone and substrate analysis
was obtained between 7 and 9 A.M. after a 12 hr fast which included no
caffeine or
nicotine. Blood (10 ml) was collected via venipuncture of an antecubital vein.
Determination of glucose and insulin response to an oral glucose load was
conducted
on the same day as one of the pre-treatment blood samples. Subjects ingested a
room
temperature beverage containing 100 grams of dextrose (Tru-Glu 100
orange/carbonated, 10 oz. Fischer Scientific, Pittsburgh, PA), and blood
samples (3
ml) were obtained from a 21-gauge indwelling venous catheter (Baxter
Healthcare,
Deerfield, IL) in an antecubital vein prior to ingestion and 15, 30, 60, 90,
120 and 180
min post-ingestion.
All blood samples were anti-coagulated with 250 ml
ethylenediaminetetraacetic acid (EDTA) (Sigma Chemical Company, St. Louis,
MO).
Aliquots of whole blood (200 ml). from basal samples were used for
glycosylated
hemoglobin determination via an affinity resin column, colorimetric, endpoint
procedure {Sigma Diagnostics, St. Louis, Mo.). The remaining samples were
centrifuged at 1,000 x g for I S min; plasma was then removed and frozen at -
20oC for
subsequent analysis. Insulin concentration was determined by radioimmunoassay
(ICN Biomedicals, Inc., Costa Mesa, CA). Plasma samples were analyzed by
enzymatic assay for glucose, triglycerides and total cholesterol (Sigma
Diagnostics,
St. Louis, Mo.). HDL-C was determined enzymatically following the
precipitation of
LDL-C and VLDL-C (Sigma Diagnostics, St. Louis, MO). LDL-C was calculated
with the equation: LDL-C = (Total cholesterol) - (HDL-C) - (Triglycerides/5)
(Sigma
Diagnostics, St. Louis, MO). Standards were run with each assay to verify
consistency. All samples were run in duplicate, with each subject's samples
run
sequentially.
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Statistical analyses. A multivariant ANOVA was run for each variable on pre
and post values across all treatment groups. Pre and post values, as well as
time
points in the OGTT, were treated as repeated measures. If a significant F
value was
observed (p < 0.05), further analysis was done to determine where these
changes
occurred. An a priori significance (p < 0.05) was used as a criteria for
further tests.
Post-hoc tests were repeated measure ANOVAs or Fishers PSLD (p < 0.05),
depending on the nature of the data. These low stringency were used to protect
against the chance of committing a type II error because of the small sample
size and
therefore limited power of the statistical design. Analyses were conducted
using
Statistic Package for Social Sciences (SPSS) software, version 6.0 for
Macintosh.
RESULTS. Subjects participating in exercise training completed 90.0% (~2.2)
of the prescribed training sessions. There were no differences in compliance
between
treatment groups. Heart rates during cycling were consistently between 75 and
80%
of HR Heart rates during step aerobics were very high, ranging from 175 to 190
max'
beats per min (85 to 95% of HRmaX). VO~max was not significantly altered in
any
treatment group.
Following treatment, there was a significant increase in body weight in the CP
group, and a significant decrease in body weight in the E/CN group. There were
no
significant changes in body fat percentage, fat mass, or fat-free mass. Fat-
free mass in
the E/P group was significantly higher than in all other groups both pre- and
post-
treatment. It is noteworthy that the non-significant higher initial weight in
the E/P
group can be attributed almost entirely to greater fat-free mass.
There were no significant differences in pre- and post-basal plasma glucose or
insulin levels. Glycosylated hemoglobin was also unchanged following
treatment.
Pre- and post-treatment glucose tolerance and insulin response curves for each
treatment group for a nine wk treatment period were plotted from a three hr
oral
glucose tolerance test. Subjects received chromium supplementation (CP),
exercise
training with placebo (E/P), chromium picolinate {E/CP), or exercise training
with
chromium nicotinate (E/CN).
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Comparisons of glucose tolerance curves or area under the glucose curves
from a three hr oral glucose tolerance test, of a pre and post nine wk
treatment period
in subjects receiving chromium supplementation (CP), exercise training with
placebo
(E/P), exercise training with chromium picolinate (E/CP), or exercise training
with
~ chromium nicotinate (E/CN) indicated no significant treatment effect for any
group.
Likewise, there were no significant improvements in the insulin response
curves
following the CP (p = 0.433), E/P (p = 0.087), or E/CP (p = 0.110) treatments.
However, following the E/CN treatment there was a significant decline in the
in the
insulin response at 60, 90 and 120 min after the oral glucose load which
resulted in a
significant decline in the overall insulin response curve (p = 0.041 ). The
area under
the insulin response curve for the E/CN treatment was also found to be
significantly
reduced post training. No significant differences were found for
triglycerides, total
cholesterol, LDL-C, and HDL.-C between pre- and post-treatment samples.
DtsCUSStoIV. The inventors' study compared the effects of chromium
picolinate supplementation, exercise training, or both in young, obese women.
Data
was also gathered on the effects of chromium nicotinate supplementation
combined
with exercise training, as in vitro work suggests chromium nicotinate may be
effective
in altering these risk factors as well.
Body weight increased significantly in the CP group following treatment.
This is an important finding, as chromium picolinate is often promoted as an
aid to
weight reduction. The inventors' results indicate that without exercise, not
only may
chromium picoIinate supplementation be ineffective in causing weight loss, but
may
result in weight gain. Weight gain has not been seen with previous studies of
chromium supplementation (Kaats et al., 1991; Page et al., 1991; Riales,
1979),
possibly due to uncontrolled diet and activity patterns as well a differential
genotype
patterns of the subjects (i. e. carriers of DRD2 A 1 vs DRD2 A2 alleles. Also.
the
. inventors' subject population (young, obese females) has not previously been
studied;
this may account for the difference in findings. Similarly, the amount of
chromium
- administered in the present study (400 ug/d) was twice the amount previously
studied
with females. It is possible that at-this concentration chromium has a
facilitating
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effect on weight gain, whereas at lower concentrations it may have an
inhibitory
effect.
No significant changes in body weight were seen in the E/P or E/CP groups,
confirming previous research indicating weight loss is not often seen with
nine wk of
_5 exercise training (Stefanick, 1993). There was, however, a significant
decrease in
body weight in the E/CN group. To the inventors' knowledge, this is the first
study to
suggest that chromium nicotinate supplementation combined with exercise
training
may be an effective means of weight loss.
There were no significant changes in relative body fat, fat mass, or fat-free
mass following treatment. As with body weight, discrepancies between the
results of
this study and previous research may be due to differences in study
populations,
uncontrolled diet and activity patterns, and/or the amount of chromium
administered.
VOZmax levels did not increase significantly following the training period.
This may have been due to lack of specificity of testing: pre- and post max
tests were
conducted on a treadmill and training consisted of cycling and aerobics.
Additionally,
the resistance component of the training program may have masked or diminished
aerobic changes.
Basal glucose levels were not significantly altered in any treatment group.
This result is consistent with other studies of chromium supplementation
(Abraham et
ul., 1992; Wilson et al., 1995) and exercise training (Wallberg-Henriksson,
1992) in a
normo-glycemic population. The response of plasma glucose levels to an oral
glucose
load was significantly higher in the CP group both pre- and post-treatment
when
compared to the E/P group. Group means imply an inverse relationship between
fat-
free mass and glucose area under the curve, suggesting that a greater fat-free
mass
2~ allows for more rapid disposal of an absolute amount of glucose. Individual
data,
however, indicated a poor correlation between these two factors (r = -0.26).
No treatment effect was seen for glucose response to an oral glucose load. As
with basal glucose levels, subjects initially exhibited normal glucose levels
following
an oral glucose load. Glycosylated hemoglobin. an indication of plasma glucose
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levels over a six wk period, was unchanged following treatment. Basal insulin
levels,
initially within a normal range, were not significantly altered in any group
following
treatment.
Insulin response to an oral glucose load was significantly reduced in the E/CN
group following treatment. An improvement in insulin response has been shown
previously with hyperglycemic subjects (Djordjevic et al., 1995). No
significant
- change in insulin response was observed in the other exercise trained
groups; this was
unexpected as decreases have previously been documented with exercise training
(Heath et al., 1983; Mikines et al., 1989; Sharma, 1992; Wallberg-Henriksson,
1992).
However, this decrease has been found to be very short lived (Heath et al.,
1983;
Mikines et al., 1989), thus the inventors' inability to detect a decreased
insulin
response to a glucose challenge in these subjects may have been due to the
rapid
decay of improved insulin action. Whatever the cause for the lack of an
exercise
training effect on insulin action in the E/P and E/CP groups, the results do
suggest that
the combination of exercise training and chromium nicotinate may be
beneficial.
Basal plasma lipids did not change with treatment. It was interesting to note
that the few subjects with abnormal initial lipid levels moved towards
normalization
following treatment, although these changes were not significant.
In summary, high levels of chromium picolinate supplementation without
concurrent exercise training caused significant weight gain in young, obese
females,
while exercise training combined with chromium nicotinate supplementation
resulted
in several potentially beneficial changes, including significant weight loss
and a lower
insulin response to an oral glucose load. The inventors conclude that high
(400 ~g/d)
supplemental amounts of chromium picolinate are contraindicated for weight
loss in
young, obese women, while exercise training combined with chromium nicotinate
supplementation may be more beneficial than exercise training alone in
providing
some protection against CAD and NIDDM through risk factor modification.
Chromium supplementation may effect various risk factors for coronary artery
disease and non-insulin diabetes mellitus, including body weight and
composition,
basal plasma hormone and substrate levels, and response to an oral glucose
load. A
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study examined the effects of chromium at 400 pg daily, with or without
exercise
training, on these risk factors in young obese women (Grant et al., Med. and
Sci.
Sports and Exercise, 29:992-998, 1997). Chromium picolinate (CP)
supplementation
resulted in significant weight gain in this population, while exercise
training
combined with chromium nicotinate (CN) supplementation resulted in significant
weight loss and lowered the insulin response to an oral glucose load. The
inventors
conclude that high levels of chromium picolinate supplementation are
contraindicated
for weight loss in young, obese women. Moreover, the inventors' results
suggest that
exercise training combined with chromium nicotinate supplementation may be
more
beneficial than exercise training alone for modification of certain CAD and
NIDDM
risk factors.
After treatment there was a significant increase in body weight in the CP
group and a significant decrease in body weight in the exercised CN group (at
least
P = 0.05). There were no significant changes in body fat percentage, fat mass,
or fat-
free mass. Fat-free mass in the exercised-placebo group was significantly
higher than
in all other groups both pre- and post-treatment. Moreover, unlike the CP
group, the
exercised CN treatment resulted in a significant decline in the insulin
response at 60,
90 and 120 min. After the oral glucose load which resulted in a significant
decline in
the overall insulin response curve (P = 0.041 ).
The data from this study suggests that high levels of chromium picolinate
supplementation without concurrent exercise training caused significant weight
gain
in young, obese females, while exercise training combined with chromium
nictinate
supplementation resulted in several beneficial changes, including significant
weight
loss, and a lower insulin response to an oral load. This data suggests that
the use of
high (400 pg d-~) supplemental amounts of chromium picolinate are
contraindicated
for weight loss in young, obese females, while exercise training combined with
chromium nicotinate supplementation may be more beneficial in terms of weight
loss,
than exercise alone. The combined allelic frequencies of the OB/ gene and the
DRD2
gene account for as much as 22% of the variance of obesity in young obese
women
and this genotype may influence the effects of chromium salts on weight loss
specific
for this population bringing about an effect of chromium on weight loss in
humans
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(Comings. 1997). It is contemplated that supplementation of the composition
for
treating RDS related disorders, as described at Table 4, and specifically
those related
to obesity as described at Table 6, with chromium salts, .such as chromium
nicotinate
and/or chromium picolinate will aid in maintenance of weight loss.
EXAMPLE 15
CHROMIUM PICOLINATE INDUCES CHANGES IN BODY COMPOSITION
AS A FUNCTION OF TAQI DOPAMINE D, RECEPTOR A2 ALLELES
METHODS. In this study the inventors genotyped 100 subjects for both the
dopamine D~ receptor (DRD2) and the dopamine transporter gene (DATI )
utilizing
standard PCRTM techniques (Blum et al., 1997). The subjects were assessed for
scale
weight and for percent body fat using densitometry. The subjects were divided
into
placebo and chromium picolinate (CrP) groups (400 mg per day), according to
methods developed by Kaats, et al., 1998).
RESULTS. In literature controls the TaqI DRD2 A I allele was present in 26%
of 714 subjects (185/714) and was present in 3.3% of 30 {1/30) very well
assessed
super controls. Chi Square analysis revealed significant differences between
these two
control groups (p < 0.006).
The DRD2 A1 allele was present in 67.4% (58/86) of the obese subjects with
>_ 28% body fat; in 61.5% (8/13) of obese males with >_28% body fat and in
68.5%
(50/73) of obese females with >_28% body fat. The DRD2 AI allele was present
in
65.3% (49/75) of the obese subjects with ?34% body fat; in 62.5% (5/8) of
obese
males with ?34% body fat and in 65.7% (44/67) of obese females with >_34% body
fat. The DAT1 10/10 allele was present in 37.4% of 91 control subjects
(34/91),
47.7% (41/86) of obese subjects with >_28% body fat, 38.5% (5/13} of obese
males
with >_28% body fat, and 49.3% (36/73 } obese females with >_28% body fat. The
- DAT1 10/10 allele was present 46.7% (35/75) of obese subjects with >_34%
body fat,
37.5% (3/8) of obese males with >_ 34% body fat, and 47.8% (32/67) obese
females
with >_34% body fat. Chi Square analysis revealed a significant association
between
the TuqI DRD2 A 1 allele and morbid obesity when compared to either the
literature or
super controls. Table 67 shows that a significant association was found for
both
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males and females with _< 28% body fat compared with literature controls (Chi
Square = 62.b, df = 1, p =<.0001 ); and for super controls (Chi Square = 36.6,
df = 1,
p = < .000 I ). A significant association was found for both males and females
with
_< 34% body fat compared with literature controls (Chi Square = 50.6, df= l,
p = < .0001 ); and for super controls (Chi Square = 33.0, df = 1, p = < .0001
). The
effect for males with < 28% body fat compared with literature controls (Chi
Square = 8.31, df= 1, p = .004); and, for super controls (Chi Square =18.6, df
=l ,
p = < .0001 ); and for females with <_ 28% body fat compared with literature
controls
(Chi Square = 57.34, df = I , p =<.0001 ) and for super controls (Chi Square
=36. i 1, df
= 1, p = < .0001 ). The effect for males with <_ 34% body fat compared with
literature
controls (Chi Square = 5.46, df = I, p = .02); and, for super controls {Chi
Square
=16.6 , df =1, p = < .0001 ); and for females with _< 34% body fat compared
with
literature controls (Chi Square = 46.73, df= 1, p =<,0001 ) and for super
controls (Chi
Square =32.38 , df = I , p =<.0001 ) In contrast no association was found for
any
I S allelic combinations for the DAT1 gene including the 10/10 genotype in
this morbidly
obese population. For both males and females with <_ 28% body fat compared
with
literature controls (Chi Square =2.27, df = 1, p = .132) and for males with <_
28% body
fat compared with literature controls, (Chi Square =.02 , df =l, p = .89 ) and
for
females with <_ 28% body fat compared with literature controls (Chi Square
=2.73, df
=l, p = .098 ). For both males and females with <_ 34% body fat compared with
literature controls (Chi Square =1.75 , df= I, p =.185) and for males with _<
34% body
fat compared with literature controls (Chi Square =.01, df =1, p =.96 ) and
for females
with <_ 34% body fat compared with literature controls (Chi Square =2.02, df
=1, p =
I6 ).
TABLE 67
DOPAMINE TRANSPORTER GENE AND BODY FAT
PERCENT DATA(%) CHI-SQUAREb
BODY FAT 10/10 R-VALUE (Controls)
< 28 41 0.132
Total Population (47.7)
(N = 86)
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DOPAMINE TRANSPORTER GENE AND BODY FAT
PERCENT DATA(%) CHI-SQUAREb
BODY FAT 10/10 P-VALUE (Controls)
0.89
Mates (38.5}
(N=13)
<28 36 0.098
Females (49.3)
(N=73 )
<34 35 0.185
Total Population {46.7)
(N=75 )
<34 3 0.96
Males (37.5)
(N-8)
<34 32 0.16
Females (47.8)
(N=67)
a = parentheses indicate percentages
b = 34/91=37.4%
TABLE 68
DOPAMINE D2 RECEPTOR GENE AND BODY FAT
PERCENT DRDZ(%) CHI-SQUARE CHI-SQUARE'
BODY FAT 10/10 P VALUE P VALUE
(Literature (Super Controls)
Controls)
~ 28 58 0.0001 0.0001
Total Population(67.7)
(N = 86)
~ 28 8 0.004 0.0001
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DOPAMINE D2
RECEPTOR GENE
AND BODY FAT
PERCENT DRDz(%) CHI-SQUARE CHI-SQUARE'
BODY FAT 10/10 RVALUE P VALUE
(Literature (Super Controls)
Controls)
Males (61.5)
(N= I 3 )
<28 50 0.0001 0.0001
Females (68.5)
(N=?3)
<34 49 0.0001 0.0001
Total Population(65.3)
(N=75)
<34 5 0.02 0.0001
Males (62.5)
(N=8)
<34 44 0.0001 0.000 t
Females (65.7)
(N=67)
a = parentheses indicate percentages
b=
b = 1/30=3.3%
Comparing super controls and all cases with more than 34% body fat.
utilizing a statistical technique called logistic regression analysis, the
DRD2 A1 allele
accounts for 45.9% of the variance which is statistically significant (Chi
Square
=43.47, df =1, p < .0001). In contrast, when compared to literature controls
DAT1
(/0110 allele) accounts for 3% of the variance and is not a statistically
significant
contributor to the variance in this population under study.
In terms of the CrP data, the sample was separated into two independent
groups; those with either an A l IA l or A l /A2 allele and those with only
the A2/A2
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pattern. Each of these groups was tested separately for differences between
placebo
and treatment means far a variety of measures of weight change. These measures
consisted of calculations of the percent of fat weight change, the change in
fat weight,
the change in body weight. the change in fat free mass, the percent change in
fat
weight, the body composition index and body weight change in kilograms. Mean
differences between placebo and treatment groups were tested statistically
using an
independent groups t-test. Statistical significance was determined for these
comparisons by setting the alpha criterion at p = 0.05. Any p values less than
0.05 are
considered to indicate a statistically significant difference between the mean
of the
placebo and the treatment groups.
T test analysis revealed that carriers of the DRD2 A2 allele were more
responsive to the effects of CrP than were the DRD2 A 1 allele carriers. The
measures
of the change of fat weight (p < 0.032), change in body weight {p < 0.011 ),
the
percent change in weight {p < 0.035), and the body weight change in kilograms
(p < 0.012) were all significant, whereas no significance was found for any
parameter
for those subjects possessing a DRD2 A1 allele.
DISCUSS101V These results suggest that the dopaminergic system, specifically
the density of the D, receptors, confers a significant differential
therapeutic effect of
CrP in terms of weight loss and change in body fat. This takes on even greater
significance when one considers the relationship between overeating and the A
1 alleie
of the DRD2 gene (Blum et al., 1995). It is the inventors' contention that the
positive
responsiveness of the A2 DRD2 carriers retain the positive metabolic effects
of CrP,
but, in contrast, the A I DR.D2 carriers because increased carbohydrate
bingeing masks
the effects of CrP on weight loss and change of body fat. These data further
suggest
that combinations of CrP as other chromium salts with amino acid precursor
therapy
as even in A1 DRD2 carriers should result in reduced craving and significant
weight
loss (Bium et al., 1997). Moreover the inventors propose that mixed effects
now
observed with CrP administration in terms of body composition, may be resolved
by
genotyping the prospective patient prior to treatment. The reason that
Chromium
picolinate or nicotinate have the observed effects is that chromium salts
effect primary
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metabolic parameters while the amino acids affects hypodopamine deficiency
thereby
reducing craving for sweets.
EXAMPLE 16
AMINO ACID AND HERBAL COMPOSITIONS FOR ENHANCEMENT OF
ATTENTION PROCESSING: POTENTIAL EFFECTIVENESS IN
ATTENTION-DEFICIT HYPERACTIVITY DISORDER
The inventor has observed an association between polymorphisms of the
dopamine D~ receptor gene and brain electrophysiological abnormalities in
humans.
In this regard a weighted linear trend analysis revealed a significant
worsening effect
of event-related potentials (EPs) in the presence of the DRD2 A 1 allele
compared to
the DRD2 A2 genotype and comorbid Substance Use Disorder (SUD) (p < 0.0001 ].
Decreased amplitude and latency of the F300 wave of evoked related potentials
(ERPs) has long been associated with alcohol and drug dependence. The inventor
also found that a significant prolongation of P300 latency correlated with
three risk
factors: ( 1 ) parental SUD, (2 ) chemical dependency (i. e. cocaine
dependence), and
(3) carbohydrate bingeing. The inventor also found that two copies of the DRD2
A1
allele (A 1 /A 1 ) compared to the A2 form of the gene (A2/A2) significantly
correlated
with a prolonged latency of the P300 wave. Moreover, the P300 amplitude
correlated
with family history of alcoholism and SUD and S. Hill (University of
Pittsburgh)
found a significant association of P300 amplitude with the DRD2 A1 allele as
well.
These results suggest a role for the DRD2 A 1 allele in a non-behavioral
pathophysiological phenotype involving brain function including attentional
processing and potential RDS associated behaviors (i. e. SUD and ADD/ADHD). It
is
to be noted that P300 abnormalities are well documented but are not specific
to drug
abuse as once thought. They are also common in mixed ADD, schizophrenia,
delirium, obesity, and other psychiatric disorders.
A number of therapeutic interventions have been shown to improve evoked
potential abnormalities as well as spectral analysis, such as cholinergics,
cholinesterase inhibitors, dopaminergic and serotonergic agents, stimulants,
trace
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elements, diet {low refined carbohydrates), cranial stimulation, biofeedback,
as well as
others suggesting a different and non-specific approach.
Since dopaminergic function is linked to brain electrophysiological
abnormalities potentially through reduced D~ receptors, therapeutic measures
may
reside in technology developed to enhance brain D, receptor function. Gene
product
expression in terms of dopamine D2 receptor is significantly reduced with the
TaqI
AI, TaqI Bl, as well as other alleles which result in dopaminergic deficiency.
In
terms of gene repair, at the current time there is no known technique to
restore such
receptor deficiency in a permanent fashion, however certain parts of this
invention
address the potential to overcome this genetically-induced reduction in D~
B",aX. An
up-regulation of DZ dopamine receptors by agonists including bromocryptine,
and
N-n-propylnorapomorphine have been observed (Fitz et al., 1994). Moreover,
changes in mRNA did not appear to account for the increase in receptors after
agonist
treatment. Instead, studies with cycloheximide, a protein-synthesis inhibitor,
suggest
that increased protein synthesis (up-regulation of gene expression), and not
decreased
protein degradation, is responsible for up-regulation by dopaminergic
agonists.
Utilizing this logic, this invention proposes to couple the use of enhancement
of
synaptic dopamine release via enkephalinase inhibition (D-phenylalanine) to
promote
chronic occupancy of DZ receptors with potential D~ receptor proliferation or
up-
regulation as shown in transfected HEK-293 cells. This further forms the basis
of this
mvent~on.
EIectrophysiology and Neurotransmitter Function. Brain electrical activity
mapping, including QEEG and cortical evoked potentials, has revealed the
existence
of subtle neurological changes in a wide variety of subjects (Porjesz, et al.,
1987),
including schizophrenics (Braverman et al., 1990; Christian et al., 1994; BIum
et al.,
1995), criminals (Lovinger et al., 1995; Seiden et al., 1995), depressives
(Hudson,
1995), Alzheimer's (Scourfield, 1996; Lawford, 1995), AIDS (Meiswanger et al.,
1995), ADD/ADHD and their response to medication (Kokkevi et al., 1995; Nunes
et
al.. 1995; Yoshida et al., 1984), and SUD (Gilman et al., 1990; Morrow et al.,
1992;
Brown et al., 1994; Comings et al.; 1996; Neshinge et al., 1991 ). It is well
known
that drugs can induce neurotransmitter deficits in the deep limbic structures
(located in
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the temporal lobes) (Yoshida et al., 1984; (Gilman et al.. 1990), leading to
focal
electrophysiological abnormalities. Those topographical changes may be an
important marker or component which motivates an individual's desire to engage
in
substance use. It has been suggested that the kindling phenomenon of the
limbic
system may be a factor in both the craving and withdrawal of SUD subjects
(Ballenger et al., 1980). Furthermore, SUD like premorbid depression may
premorbidly predispose probands to subsequent Alzheimer's encephalopathy,
concomitant ADDIADHD, and other psychiatric diseases which may originate in
the
temporal lobes. Brain mapping temporal lobes abnormalities correlate to
hypometabolism on PET scan, which is similar to interictal temporal lobe
seizure
disorder patients who also have hypometabolism (Adams et ul., 1993). It has
been
reported that the DRD2 A1 allele associated with low glucose metabolism as
measured by PET scan in frontal lobes (Noble et al., 1997). This suggests that
hypometabolism is associated with low dopamine D~ receptors in the frontal
lobes of
the brain leading to dysfunction. A number of papers suggest that SUD promotes
kindling (Goldstein et al., 1994) or electrophysiological instability which
may induce
aberrant evoked potential and spectral analysis abnormalities. Moreover, both
cocaine
and ethanol induce a kindling response or electrophysiological instability and
on an
acute basis temporarily corrects evoked potential abnormalities most likely
via
dopamine release. Recovering substance abusers still had low P300 even after
substance use was discontinued. The P300 activity only partially recovers and
also
represents a genetic characteristic antedating substance use leading to
cocaine or
heroin abuse similar to that observed in alcoholics {Gilman et al., 1990).
The inventors theorizes that substance dependence significantly exacerbates a
potential premorbid state and strongly suggests a gene-environment
interaction. The
self medication of substances or behavioral acts which cause a release of
dopamine in
the nucleus accumbens (i. e. cocaine, alcohol, nicotine, sugar, sex, etc. ),
used to relieve
these electrophysiological disturbances especially with drugs, unfortunately
results in
worsening of brain dysfunction.
Additive Effect of Different Dopamine Genes. A summary of a recent study
by the inventor involving the three dopaminergic genes DRD2, D~3H and DAT1
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genes, in families with both ADHD/ADD and RDS genotyped up to the fourth
generation is presented herein. Together these results provide support at a
molecular
genetic level for the concept that ADHD, TD and other disorders are inherited
in a
polygenic fashion, part of a spectrum of related disorders (RDS), caused by
shared
genes, caused by alleles that are common in the population, caused by genes
that are
additive in their effect, caused by genes that upset the dopamine system
(among
others}.
Generational Association Studies of Dopaminergic Genes in Attention-
Deficit/Hyperactivity(ADHD) Probands and Multiple Family Members Up To
Four Generations. Polymorphisms of the dopamine D2 receptor gene are
associated
with the "Reward Deficiency Syndrome " (RDS) or a number of related impulsive
addictive-compulsive behaviors; the VNTR 10/10 genotype of the dopamine
transporter gene (DAT1) associated with ADHD and Tourette's Disorder (TD); and
the B 1 allele of dopamine-beta-hydroxylase gene (D(iH) also associated with
TD and
a number of RDS behavioral sub-traits.
The inventors genotyped 51 subjects up to four generations derived from two
multiply affected families. The DNA was extracted from buccal swabs according
to
the PCRTM-based methods (Blum et al., 1997) The two initial probands were
carefully diagnosed by a number of standard instruments to have ADHD.
Subsequently, the additional family members were also diagnosed for ADHD and
other related RDS behaviors. All subjects were genotyped for the three
dopaminergic
genes (DRD2, DATI, and D[iH). Eighty percent of all subjects {40/50) carried
the
DRD2 TagAl. When compared to "super" controls (1/30 or 3.3% carried the DRD2
A 1 allele) a significant association was observed (Chi Square = 41. I , df =
1,
p = 0.00000001, Yates corrected) with an odds ratio of 116 [95% confidence
limits
13.6-2,575]. The inventors present these data to point out the importance for
highly
screened controls. A similar but less robust finding was obtained when the
inventors
compared the data utilizing 714 non-alcoholic and non-drug abusing literature
controls ( 185/714 or 26 % carried the DRD2 A 1 allele). A significant
association was
found (Chi Square = b3.2, df = 1, p = 0.00000001, Yates corrected) with an
odds ratio
of 11.4 (95% confidence limits 5.38-24.93]. In 91 screened controls the
prevalence of
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the DAT1 10/10 allele was 34191 or 37.4%, as well as in 51 screened controls
where
the prevalence of the D~iH B 1 allele was 27/51 or 53%. A significant
association was
also found between the DATI (VNTR 10/10 genotype) in the ADHD-derived two-
family members {30150 or 60%) when compared to screened controls (Chi
Square = 7.51, df = 1, p = 0.0061 ) with an odds ratio of 2.64 [95% confidence
limits
1.31-5.38]. In contrast, non-significance was found with carriers of the D(3H
Bl
(32/50 or 64%) compared to screened controls (Chi Square = 1.27, df = 1, p =
0.259)
with an odds ratio of 0.63 [95% confidence limits 0.28-1.4]. The inventors
believe
that the high percent of the DRD2 Al allele in these subjects compared to 40-
50%
usually found with single addictive-impulsive-compulsive behavioral sub-
traits, is due
to the multiple behavioral sub-traits encompassing RDS. In one family, the
DRD2 A1
allele was present in 100% of subjects diagnosed as having ADHD.
When the data are complete linkage analysis will be performed, utilizing at
least one RDS behavior present in a family member as a co-variate. It is
noteworthy
that as the number of RDS behaviors increase in the subjects, the presence of
the
DRD2 A 1 allele also increases. At first glance it appears that the DRD2 A 1
allele,
relative to the other two dopaminergic genes, is more informative in
predicting both
ADHD and RDS behavior at least in this sample currently tested. The data are
currently being processed and additional outcomes will be presented and
discussed in
terms of the impact these findings have on the biogenetics of impulsive-
addictive-
compulsive behaviors (RDS) as well as one important sub-trait ADHD.
Cognition, Electrophysiology and Neurotransmitter Function. One
important aspect of this invention is that attentional processing is
influenced by
alterations in neurotransmitter function especially at the brain site (meso-
limbic)
responsible for "reward" and other related behaviors. Moreover, dysfunction in
the
"reward cascade" via genetic or environmental elements like drugs, sex, and
stress
may influence attentional processing. While a number of neurotransmitter
pathways
are ultimately involved in focusing, memory and cognition in general at least
four
major pathways are preferred in this invention to be involved: serotonergic,
opioidergic, GABAergic and dopaminergic. A brief review of the literature
concerning cognition and neurotransmitters will favor the positive
relationship
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between the dopaminergic system and attentional processing. This relationship
fosters the concept that compounds which activate the dopaminergic system and
promote agonist interaction at dopamine receptors or release natural dopamine
will
enhance attentional processing and focus in an individual.
Polymorphisms of the Dopamine D, Receptor Gene Associates with Brain
Electrophysiological Abnormalities in Humans. This is the first study known to
the inventors which provides evidence for the association between
polymorphisms of
the dopamine D2 receptor gene and brain electrophysiological abnormalities in
humans. In this regard a weighted linear trend analysis revealed a significant
worsening effect of event-related potentials (EPs) in the presence of the DRD2
A1
allele compared to the DRD2 A2 genotype and comorbid Substance Use Disorder
[SUD] (n < 0.0001). Duncan's Range Test showed SUD with or without DRD2 A1
allele significantly worsened the EPs compared to DRD2 A2 controls. Moreover,
the
inventors observed a significant association between severe substance use
disorder
(SUD) and the DRD2 A 1 allele relative to the inventors' "super controls"
(p < 0.0000033) and to a large number of literature controls (p < 0.0021).
Decreased
amplitude and latency of the P300 wave of evoked related potentials {ERP) has
long
been associated with alcohol and drug dependence. In this investigation the
inventors
found that a significant prolongation of P300 latency correlated with three
risk factors
(1) parental SUD, (2) chemical dependency (i.e. cocaine dependence), and (3)
carbohydrate bingeing (p < 0.03). In this population the inventors also found
that
decreased P300 amplitude correlated with family history alcoholism and SUD
(p < 0.049), but did not correlate with the DRD2 A 1 allele. These results
suggest a
role for the DRD2 A 1 allele in a non-behavioral pathophysiological phenotype
involving brain function and potential addiction liability.
The aim of this study was to determine whether the Dopamine D2 Receptor
gene (DRD2) TagI A1 allele associates with brain electrophysiological
abnormalities
with or without substance use disorder (SUD) in humans attending a private
outpatient clinic. Following the finding by the inventors' laboratories of a
strong
association between the A1 allele of the D2 dopamine receptor gene and
alcoholism
(Blum et al., 1990), several groups were unable to replicate the observation
(Gelernter
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et al., 1997). The inventors have suggested two possible reasons: f rst,
inadequate
screening of controls for alcohol, drug, and tobacco abuse as well as other
related
behaviors, and second, sampling errors in terms of characterization of
alcoholics for
chronicity and severity of the disease (Blum et al., 1996). However, review of
the
literature reveals a number of positive associations between the DRD2 gene,
with not
only alcoholism but also with a group of impulsive-addictive-compulsive
disorders
including polysubstance abuse, smoking, attention-deficit/hyperactivity
(ADHD),
carbohydrate bingeing, Tourette's Disorder, pathological gambling, post-
traumatic
stress disorder as well as schizoid/avoidant behavior that have been termed
"The
Reward Deficiency Syndrome" (RDS} (Blum et al., 1996). The variations in DRD2
alleles have been argued as representing variations in a very common latent
trait
associated with dopaminergic function of which alcoholism is but a single
manifestation (Hill and Neiswanger, 1997). Moreover, excluding "other
pathology"
from both controls and affecteds already has been accomplished in San Antonio,
Los
Angeles, Duarte, acid Pittsburgh (Hill et al., 1997; Bium et al., 1996). It is
the
inventors' contention then, that failure to find linkage or with-in-family
association
could be due to incomplete understanding of the appropriate phenotype for
analysis.
Therefore, in order to reduce spurious results, the inventors decided to
utilize a
non-behavioral-pathophysiologically based phenotype known as brain-
electrophysiological abnormalities, as a marker for subsequent association
studies
with the DRD2 TaqI A1 allele. Other human association studies with the DRD2
TaqI
A 1 allele suggested this approach (Noble et al., 1994; Blum et al., 1994). To
date,
correlations exist between abnormalities in both spectral and evoked
potentials for a
number of behavioral disorders including SUD, ADHD, conduct disorder (CD),
pathological violent behavior, Alzheimer's, among other disruptive behavioral
disorders (American Psychiatric Association Task Force, 1991).
Since the inventors found significant evoked potential abnormalities with SUD
and obesity, the inventors decided to systematically assess the possibility of
a direct
correlation of abnormal brain electrical activity and the DRD2 A1 allele in
patients
attending a neuropsychiatric and medical clinic in Princeton, New Jersey.
Positive
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correlations could provide important and relevant clinical information to
diagnose
premorbid genetically based brain dysfunction.
The relationship between the DRD2 allelic variance, SUD, and spectral
analysis was examined. A significant probability map (SPM) of the visual
evoked
response (VER) in a typical normal subject having a DRD2 TagI A2/A2 allele was
determined. Standard deviations {SD) maximum (0.34) and minimum (-1.00) were
calculated as SPM, and the inventors' control group is not significantly
different from
the standardized BEAMTM (brain electrical activity mapping) control. A
characteristic
brain map of the VER in a subject with the DRD2 TaqI A1/A2 allele without SUD,
with a light frontal temporal excess negativity to 2.92 SD as visualize as
bright white-
blue. The right frontal temporal abnormality exhibited by the light white blue
area is
typical of individuals with mood swings, palpitations, anxiety and stress,
with or
without SUD. A characteristic brain electrical activity map of the VER in a
SUD
patient with a DRD2 TagI A 1 /A2 genotype, with left and right frontal
temporal excess
negative to 6.13 SD was visualized by a bright white light.
The inventors also found a significant prolongation of P300 latency correlated
with three risk factors (a) parental SUD, (b) chemical dependency (i.e.
cocaine
dependence), and (c) carbohydrate bingeing (p < 0.03). Also the inventors
found that
decreased P300 amplitude correlated with family history alcoholism and SUD
(p < 0.049), but did not correlate with the DRD2 A1 allele.
Most importantly, weighted linear trend revealed a significant worsening
effect of event-related potentials in the presence of the DRD2 A 1 allele
compared to
the DRD2 A2 genotype and comorbid SUD (p < 0.0001 ). Duncan's Range Test
showed SUD with or without DRD2 A1 allele significantly worsened the EPs
compared to the DRD2 A2 controls.
It is noteworthy, that in this study, 52% of the severe SUD subjects (N = 29)
carried the DRD2 TagI A 1 allele. The percent prevalence is significantly
different
when compared with the inventors' "super normal" controls (excluded for
alcoholism,
SUD, smoking behavior, ADD/ADHD, carbohydrate bingeing, pathological
gambling, schizoid/avoidant personality disorder behavior, violent behavior,
and
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family history positive for alcoholism, SUD, and obesity) with a DRD2 A1
Allelic
prevalence of 3.3% (1/30) [Chi Square = 17.47, df= 1, p < 0.0000033].
Moreover, of
714 non-alcoholic, non-SUD (except tobacco), non-Hispanic Caucasian controls,
25.9% carried the DRD2 A 1 allele. When compared to the non-Hispanic Caucasian
SUD probands a very strong association was found (Chi Square = 9.44, df = I ,
p < 0.0021 ).
To the inventors' knowledge, this is the first study to observe a significant
association between the DRD2 A 1 allele with increasing number of brain
electrophysiological abnormalities in both VER and auditory evoked responses
(AER)
in patients attending a neuropsychiatric setting. This genetic evidence along
with
other studies of electrophysiological disturbances in individuals that may be
prone to
psychostimulant abuse/dependence further suggest the relationship of
dopaminergic-
genes and proclivity for stimulant abuse. It has been reported that SUD
exacerbates
brain mapping parameters (Braverman et al., 1996; Blum et al., 1995; Lovinger
et al.,
I S 1995; Seiden et al., 1995; Hudson, J., 1995) and when abstinence occurs,
there
appears to be persistence of some drug-induced brain electrophysiological
damage in
most cases (Scourfield et al., 1996; Lawford et al., 1995; Neiswanger et al.,
1995).
The inventors theorize that comorbid SUD in psychiatric probands significantly
exacerbates a potential premorbid state and strongly suggests a gene-
environment
interaction (Kokkevi et al., 1995; Nunes et al., 1995). The self medication of
illegal
drugs, like cocaine for example, used to relieve these electrophysiological
disturbances, unfortunately results in the worsening of brain dysfunction,
especially in
the bi-temporal lobes of the brain with chronic repeated use. Furthermore, a
decrease
in the amplitude and prolongation of the P300 has been associated with
alcoholism
and drug addiction (Yoshida et al., 1984; Gilman et al., 1990). This is
independent of
the acute effects of the alcohol and drugs since findings have been in the
sons of
alcoholics who do not use alcohol themselves (Morrow et al., 1992; Brown et
al.,
1994). This speaks for the presence of a gene or genes that are involved in
decreasing
amplitude and prolonging latency of the P300 wave and are associated with an
increased risk of SUD.
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In a global sense the inventors believe these observations will have important
neurophysiological relevance supporting the further the role of dopamine in
brain
function (i.e. reward). The linking DRD2 polymorphisms have been linked to
dopamine receptor function (Pohjalainen et ul., 1996). In 33 Finnish male
volunteers
the Tagl A l allele of the DRD2 gene associated with a statistically
significant
- reduction in the adjusted Bma~ compared to A2/A2 subjects. The Kd was not
significant between the groups. This is evidence that while the number of
receptors
go down with the Al/A2 subjects, there seems to be no change in receptor
function,
indicating a regulatory role of 3' mediated polymorphisms in receptor
synthesis.
Additionally, DRD2 knock out mice had a significant reduction in D., receptor
Bmax
without any change in Kd (Grandy et al., 1989). Thus the regulatory gene
element
may be in linkage disequilibrium with the TugI A polymorphism. Therefore,
demonstration of specific molecular polymorphisms identified with brain
electrophysiological abnormalities such as the DRD2 A I allele, should have
profound
clinical usefulness. Logically, from these studies it appears that
polymorphisms of the
dopaminergic system are tied to specific brain electrophysiological
dysfunction (i.e.
VER and AER) which seem to mediate abnormal behaviors {sub-traits of RDS).
If these associations continue to be defined, the inventors will be able to
provide better prevention strategies, especially in high risk groups.
Additionally,
more specific targeted treatment modalities ultimately will derive from these
investigations which, in the inventors' opinion, significantly impact human
behavioral
pathology.
Subjects A total of two-hundred ninety-four subjects were utilized in this
study. All of the subjects were non-Hispanic Caucasians who signed an informed
consent; the study was approved by the Path Research Foundation Institutional
Review Board. The patients were randomly selected for study from approximately
5,000 visits from 800 patients attending the PATH out-patient private clinical
practice
over a one-year period.
All subjects in the SUD groups were clinically established to have early full
(DSM II~ remission of SUD (Brown et al., 1994). The demographic breakdown of
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the inventors' one-hundred seventy-three sample base is described in Table 69.
Gender and psychiatric diagnoses were not significantly different between all
groups
tested. The mean age between all groups was assessed and did not vary
significantly
(p < 0.00001 }. For this investigation of the sexual selection included 53.1 %
percent
males and 46.9% percent females this age difference was found in psychiatric
diagnosis and not in gender.
Electrophysioiogieal and Genotyping Methods. For selection criteria and
assessment instruments to determine SUD phenotype (cocaine abuse, DSM IV Code
No. 305.60; cocaine dependence, DSM IV Code No. 304.20; alcoho! abuse, DSM IV
Code No. 305.00; and alcohol dependence, DSM IV Code No. 303.90) the inventors
utilized the same methodology as the inventors previously reported (Braverman
et al.,
1996). The subjects were genotyped for the DRD2 allelic variance (DRD2 TaqI A1
and A2) in accord with Comings et al. (1996). A Nicolette BEAMTM was used to
assess; total brain abnormalities, total spectral abnormalities, evoked
potentials (EP,
AER, VER) and P300. For a detailed description of this methodology to assess
brain
eiectrophysiological abnormalities (Braverman and Blum, 1996). The inventors
also
included in this study a non-genotyped P300 control group for matched
comparison
purposes. The P300 control group included 15 male volunteer subjects who were
drug, alcohol, and food addiction free, and free of psychiatric disease.
Statistical analysis. For statistical analysis all brain map data were
classified
as abnormal or normal. Specifically, EEG was dichotomized as normal or
abnormal,
spectral analysis was dichotomized at 2.5 SD from standardized BEAMTM controls
with recurrence of deficits (at the same loci) following three independent
runs. P300
voltage was dichotomized at an established normal voltage at IOdv (Neshinge et
al.,
1991 ), P300 latency was dichotomized at 350ms. This value was based on an
estimate of 300ms. plus mean age of the control group, which is a criterion
developed
by Lexicor, Inc., Boulder, Colorado, and an approximate 1.25ms. per year
increase in
P300 after age 18. A complete description of statistical methodology has been
previously published (Braverman et al., 1996). Additionally, the inventors
employed
a Duncan's Range Test for paired comparison of means. The alpha level was set
at
0.05 for significance.
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Enhancement of Attention Processing in Healthy Humans by KantrollTM~:
A Cocaine Surrogate with Enkephafinase Inhibitory Properties. This is the
first
report of the effects of daily ingestion of a specific amino acid mixture,
KantrollTM, in
humans on cognitive event-related potentials (ERPs) associated with
performance.
Cognitive ERPs were generated by responses to two computerized visual
attention
tasks, the Spatial Orientation Task (SOT) and Contingent Continuous
Performance
Task (CCPT), in normal young adult volunteers, where each subject acted as his
own
control for testing before, and after, 28-30 days of amino acid ingestion. A
statistically significant amplitude enhancement of the P300 component of the
ERPs
was seen after KantrollT~' for both tasks. The changes observed in this study,
on
normal controls. strongly suggested that enhancement of neurophysiologic
function
may be the basis for the facilitation of recovery of Reward Deficiency
Syndrome
behaviors (i.e. ADD/ADHD, Substance Use Disorders, Carbohydrate Bingeing,
Nicotine Abuse, etc.) following the ingestion of the amino acid supplement,
KantrollTM.
One of the most intriguing discoveries in neurobiology was that many
neurotransmitters (e.g., dopamine, norepinephrine, epinephrine, serotonin,
melatonin
and glycine), which play vital roles in brain functioning and in mood
regulation, can
be dramatically influenced by the circulating levels of their precursor amino
acid
nutrients (e.g., Wurtman, 1983). The respective precursor amino acids are L-
tyrosine
(or L-pheny1alanlne), L-trygtophan and L-threonine. All these neurotransmitter
synthesis systems exhibit two crucial features. First, the amino acids from
which they
are synthesized are among the nine essential amino acids: histidine,
isoleucine,
leucine, lysine, methionine, pheny1alanlne, threonine, tryptophan and valaime
{tyrosine
is synthesized from pheny1alanlne). Second, the primary step in the synthetic
pathway
utilizes an enzyme that is not saturated. The consequence of these two
characteristics
is that dietary intake of these amino acids can drive synthesis of specific
neurotransmitters. While this is not necessarily a linear relationship because
of
numerous regulatory feedback mechanisms; it has been shown to be a highly
significant modulatory route (Hernandez-Rodriques and Chagoya, 1986; Wurtman
and Fernstrom, 1976; Wurtman et al., 1981 ).
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Sophisticated measurements of brain chemical turnover in animals, including
microdialysis measurements, have demonstrated changes in neurotransmitter
output
following precursor amino acid loading {Hernandez et al., 1988). Complementary
behavioral changes have been demonstrated in animals following systemic and
direct
central nervous system delivery of precursor amino acids (Blum et al., 1972).
While
certain L-amino acids are neurotransmitter and neuromodulator precursors,
their
racemates, the D-amino acids also have biological activity. In particular,
D-pheny1alanlne and D-leucine decrease the degradation of opioid peptides
which are
central to regulation of mood and behavior (Blum et al., 1987; Carenzie et
al.. 1980;
Della Bella et ul., 1979; Ehrenpreis et al., 1979).
Neurotransmitter actions form the neurochemical basis of behavior, and their
perturbation may be central to a variety of psychiatric and behavioral
disorders. Their
contribution to addictive disorders also has been the focus of considerable
comment
(Koob and Bloom, 1988; Wise and Bozart, 1985; Blum et al., 1990; Blum and
Kozlowski, 1990; Amit and Brown, 1982). Specifically, dopamine, serotonin,
norepinephrine, gamma-aminobutyric acid (GABA), glutamine and the opioid
peptides are thought to play crucial roles in addictive disorders,
particularly regarding
alcohol, heroin, and cocaine abuse (Blum et al., 1977; Geller et al., 1972).
Consequently, these observations have led to the idea that ingestion of
selected
nutrients could affect mood and therefore behavior in humans.
While nutritional strategies have been employed several times in the past
(e.~., Williams, 1959), unequivocal quantitative demonstration has been
decidedly
limited. Recent clinical data, however, suggest a substantive effect of amino
acid
precursors and enkephalinase inhibitors on recovery from alcohol, cocaine and
food
addictions (Blum et al., 1988; Halikas et al., 1989}. A relevant study (Blum
et al.,
1988) examined the use of the amino acid supplement, KantrollTM, in a 30-day
inpatient treatment program for cocaine addicts. The only difference between
the
experimental and control groups was the use of this supplement. Two primary
measures were used: leaving the program before completion or against medical
advice
(AMA) and drug hunger. Drug hunger was a measure of vivid cocaine-related
dreams, somatic complaints, requests for medication, drug-related
confrontation
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responses, program compliance, agitation and violence or threats of leaving
AMA.
The amino acid supplement group fared significantly better on both measures
than did
the control group. AMA rates were reduced nine-fold and measured drug hunger
was
significantly reduced. In addition, staff reported a decided decrease in
agitation,
outside focus and craving. However, the measures are complex, frequently
qualitative, and dependent on numerous input factors (Brown et al., 1990).
_ The present study sought to close part of the gap between clinical
experience
and basic understanding of the neurobiological consequences of KantrollTM.
Here. the
approach was to evaluate quantitative neurophysiological changes associated
with the
treatment with KantrollT~'. The electrophysiological portion focused on the
P300
component of the cognitive event-related potential (ERP), evoked by two visual
attention tasks. The advantage of this electrophysiological approach over more
conventional EEG analyses is that, by virtue of being anchored to performance
tasks,
specific systems important to attentional processing are activated. These
attention
I S probes generate a family of components of the ERPs, each representing a
stage of
information processing. However, the ERP analysis here focused on cognitive
ERPs,
specifically on the P300 component. Quantitative ERP changes have recently
been
shown to vary predictably over a range of clinical disorders; e.g.,
attentional disorders
(Buschbaum et al., 1973; Halliday et al., 1976 Prichep et al., 1976),
schizophrenia
(Braverman, 1990), depression (DeFrance et ul., 1995a; Vasile et al., 1992),
dementia
{Duffy et al., 1984), and substance use disorder (Braverman et al., 1990, and
Braverman and Blum. 1996).
The focus in this preliminary report was on attentional processing, because in
part, alternations in attentionlor concentration both precede and accompany
sustained
substance abuse (Begleiter and Projesz, 1988; Whipple et al., 1988; Parsons,
1990),
and because many transmitter substrates thought to be important in attention
are
a targeted for manipulation through the administration of KantrollTM. Another
goal in
the present study was to address the question of whether amino acids, acting
as
precursors or modulators of neurotransmitters, affect the central nervous
system. This
study, then, addressed the issue of the electrophysiological and performance
correlates
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of chronic KantrollT" administration on normal subjects, especially as indexed
by
changes in the P300 component of the cognitive ERP.
Subjects. This study involved 20 normal volunteer subjects; free of
psychological, neurological, or psychiatric conditions as determined by DSM IV
criteria by a board certified psychiatrist. All subjects signed an informed
consent and
each was compensated for participation in the study. Each subject performed
the test
battery twice as a test-retest paradigm thus acting as their own control.
Initial testing
was done on day zero (pre-test) and then again after 28-30 days (post-test).
The
subjects consumed six KantroIlTM capsules daily for 28-30 days. The
composition is
shown in Table 6. The data from two subjects were not included because of poor
quality of either the pre-test or the post-test recordings.
Performance Tasks. Two performance paradigms were used as behavioral
probes for the electrophysiological studies. The f rst probe was spatial
orientation.
This is a reaction time based task (Posner et al. (1988)} where priming cues
are
1 S presented in different portions of the visual field. Reaction times are
compared for the
right and left visual fields when the priming cues are, and are not,
available. Through
a comparison of reaction times, it allows for an assessment of the
individual's ability
to smoothly switch attention. The instructions were to focus on a cross in the
center
of the monitor screen and push the right mouse button when the '*' appears in
the right
box, and the left button when the '*' appears in the left box. The boxes
alternate with
respect to which one is the brightest. Response times were recorded, and ERPs
were
constructed with respect to four categories: ( 1 ) facilitated for the right
visual field, (2)
non facilitated for the right visual field, (3) facilitated for the left
visual field, and (4)
non facilitated for the left visual field. The presentation was randomized,
but with an
equal probability for the four conditions. The facilitated box was brightened
500
msec prior to the presentation of the target. Accuracy scores and reaction
times for
both the left and right visual field were recorded, as well as the respective
A' {a
standard signal detection parameter) values. This paradigm samples the
orientation to
stimuli, fluidity of attention, along with cognitive processing speeds.
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The second probe was contingent continuous performance. This task is a
variant of a classical theme (Rosvold et al., 1956). The individual was asked
to
respond, by pressing the left mouse button, to a specific letter order: e.g.,
'T' if it was
preceded by another 'T'. This version of the task had a constant interstimulus
interval
(ISI) of 0.8 sec and included 500 trials. The probability of a non-'T' was set
at 50%,
the probability of the warning 'T' was set at 30%, and the probability of the
target'T'
was set at 20%. Averaged ERPs were constructed according to one of three
conditions: distractor (any letter other than a 'T'), warning signal (the
first 'T' in a
pair), and target (the second 'T' in a pair). The principal performance
measure was the
Inconsistency Index (Ringholz, 1989), which is a measure of consistency of
performance. It is noteworthy that the prefrontal cortex appears to be most
heavily
involved in sustaining attention and effort, leading to good performance on
this sort of
task (Cohen, et al., 1987; Corbetta, et al., 1991 ), and these tasks are very
sensitive to
stimulant medication (Sostek et al., 1980).
Recording Scheme. The EEG was recorded from 28 active recording sites
referenced to linked earlobes (AI-A2). The montage was based on the
international
10-20 system, with additional electrodes placed in the fronto-temporal (FTC1,
FTC2),
centro-parietal (CP 1, CP2), temporo-parietal (TCP 1, TCP2), and parieto-
occipital
{POI, P02) regions. Electrode impedances were kept at less than 5 KOhm.
Additional electrodes were used to monitor extraocular artifacts. Vertical eye
movements and blinks (i. e. VEOG) were recorded via electrodes placed
immediately
above and below the orbit of the left eye. Horizontal eye movements (i.e. I-
iEOG)
were monitored with an additional pair of electrodes at the external canthi.
The EEG and EOG were amplified with a 32-channel Neuroscience Brain
Imager (0.1-40 Hz, 6 dB/octave lowpass, 36 dB/octave highpass). The raw EEG
was
sampled for 2.56 sec by 16-bit analogue-to-digital converters (TECMAR
Labmaster
DMA) under the control of the SCAN EP/EEG acquisition and analysis system
(NeuroScan, Inc.). To construct the event-related potentials (ERPs), waveform
averages were constructed from 256 points spaced over an 800 msec interval.
Sampling began I 00 msec prior to . the stimulus presentation to establish a
pre-stimulus baseline, which was used as a part of the correction process. The
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single-sweep data was baseline corrected by subtracting the mean. The number
of
sweeps recorded varied according to the behavioral probe. A total of 200
sweeps was
recorded for the Spatial Orientation and 500 sweeps were recorded to the
Contingent
Continuous Performance Test. Each segment of the behavioral paradigm. There
were
three parameters examined. each of which may vary according to the efficiency
of an
individual's attentional processing. These parameters were: latency,
amplitude, and
symmetry (spatial distribution) of components of the ERPs.
Data Analysis. First, T-test statistical maps were generated for all 28
electrode sites, at each point in time. This was a variation on the
Statistical
Probability Mapping of Duffy et al. (1981). Though exploratory, this procedure
did
result in an easily visualized map, indicating which electrode sites warranted
further
analysis. Since the P300 was the component of interest, Pz was selected as the
site for
the statistical analysis, since from the maps, this is where the focus of the
component
appeared to reside for these visual tasks. Then, the peak latency and peak
amplitude
(within a 275-325 msec interval) was determined. The second stage of
statistical
analysis employed a paired T test model (Statgraphics, 1988), comparing the
Baseline
and Treatment conditions. Performance data was also analyzed with the same
model.
A schema of the electrode array, with ERPs from the CCPT task superimposed
for the 28 scalp recording sites for illustration was made. Included also are
recordings
of Vertical Extra-oculogram (VEOG) and Horizontal Extra-oculogram (HEOG) that
were used for artifact correction and rejection.
The results from the SOT will be discussed first. Again; this particular
paradigm generates a number of interesting components (DeFrance et al., 1993),
including the N2 negativity that developed within the 100-200 msec interval.
The N2
component is a processing negativity, which is an early stage of the orienting
response
(DeFrance et al., 1993). This component was found enhanced subsequent to
treatment, but the amplitude change did not pass the inventors' pre-
established
threshold for significance [F( 1,17) = 2.30, p = 0.0259]. Nevertheless, late
vertex
positivity (i. e. P300 component) in the post-test condition was found
markedly larger
in amplitude for both the left [F(1,17) = 8.531, p = 0.0095] and right
[F(I,17) = 16.31
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p = 0.009] facilitated conditions. Hence, the group averaged P300 component
after
KantrolITM administration was found to be significantly enhanced when
orienting both
to the right and left visual fields.
Topographical maps were examined for the Baseline and Treatment conditions
. 5 with respect to the facilitated (i.e. 'primed') condition for the left
visual field. The
patterns for the right visual field mirror those of the left so they will not
be presented.
In the 100-200 msec interval, the N2 negativity is indicated over the right
temporo-
parietal region. Again, the effects of KantrollTM on this component approached
statistical significance, but greater changes were associated with the P300
component
and can be readily appreciated by comparing the 300-400 msec intervals.
Essentially,
the topographical features of the P300 component remained the same, but the
peak
amplitudes were enhanced by the KantrolITM treatment. With respect to the
performance data, the combined reaction times for the left and right visual
field
stimuli were faster after treatment 232 ~ 0.03 msec versus 238 ~ 0.03msec
[F(1,17) = 8.62, p = 0.001]. In sum, there was a marked effect on the P300
component, with a borderline enhancement on the N2 component. A marked effect
was also seen on the P300 component associated with a vigilance task, the
CCPT.
The various components of the cognitive ERPs associated with the Contingent
Continuous Performance Task (COPT) have many similarities to those generated
by
other continuous performance tasks, featuring a prominent P300 component (e.
g.,
DeFrance et al., 19956; Hillyard et al., 1973). Three sets of waveforms were
constructed for analysis - distractor, warning signal, and target - but only
the target
waveform need to be discussed. Comparison of the pre-test to the post-test
conditions
found a significant [F(1,16) = 7.422, p = .015] enhancement of the P300
amplitude.
To determine the effect, the averaged target waveform at Pz for the Baseline
and
Treatment conditions were compared. The large positive-going potential,
peaking
around 300 msec is the classic P300 component. Again, the target waveforms
were
those taken when the subject responded to the second 'T' in a pair - as per
the
- instructions. The topographical maps for the 300-400 msec interval for the
target
conditions before (Baseline) and after (Treatment) KantrollTM was determined,
and the
topographical features of the P300 component remains the same, but that the
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amplitude shows the enhancement when comparing the pre-test to the post-test
condition. That is, in both the Baseline and Treatment conditions, the P300
. component occupied a posteriocentrai locus and was symmetric as was the case
for
the SOT. Nevertheless, the amplitude of the P300 component was enhanced in the
Treatment condition. While there were electrophysiological differences after
approximately four wk of treatment, there was no significant difference with
respect
the main performance variable for this behavioral probe - the Inconsistency
Index.
The likely reason for this is that these normal subjects were already near
their
optimum performance with respect to their accuracy. It might be expected,
however,
that performance differences would emerge in clinical populations.
KantrollTM was designed as a potential treatment for cocaine abuse, with the
recognition that one manifestation of cocaine abuse is altered attentional
processing
(Robledo et al., 1993). Moreover, human attentional processing and the P300
component of the cognitive ERP are often linked (e.g., Hillyard et al., 1973).
At this
point, then, is worthwhile to revisit the neurology of the P300 component
because it
provides the context for the importance of the findings. As widely known, the
P300 is
one of several endogenous cognitive ERPs components, whose latency,
morphology,
and spatial distribution are highly dependent upon the psychological context
in which
the stimulus is embedded (Sutton et al., 1965). Consequently, the P300
component
has been the subject of much study due to its putative role in attention and
memory.
Evidence from depth recordings in humans suggests that anterior (e.g.,
amygdaloid
complex) and medial temporal (e.g., hippocampal formation), along with frontal
lobe
structures may be involved in the regulation of the P300 component, and may
actually
contribute to its display over the temporal regions (Halgren and Smith, 1987;
Wood et
al., 1980). It is not likely, however, that the deep temporal structures are
the sole
generators of the P300 component, as believed from earlier studies, since
temporal
lobectomy does not obliterate the component (Johnson and Fedio, 1986; Smith et
al.,
1985). Nonetheless, the deep portions of the temporal lobe appear to have
clear
modulatory responsibilities over the P300 component (Halgren and Smith, 1987),
as
may prefrontal zones (Simon et al., 1977).
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As the characteristics of any cognitive ERP are very much anchored to the
eliciting behavioral paradigm, it is important to keep in mind that the
performance
probe used in this study has elements of both a sustained and selective
attention task.
So certain components (e.g., P300) of the ERP should be referable to both
aspects of
attention. Interestingly, recent PET studies (Corbetta et al., 1991 ) have
indicated that
w the orbitofrontal cortex is heavily involved in the selective aspects of
attention,
whereas sustained attention appears to be more the purview of the medial
portions of
prefrontal cortex (Cohen et al., 1987), with the right hemispheric portions
playing a
particularly important role (Pardo et al., 1991; Wilkins et al., 1987). Since
the
orbitofrontal cortex regulates the activity in the anterior temporal region
via the
uncinate fascieulus, it should not be surprising then that amygdaloid damage
should
also affect selective attention. It has been suggested that amygdaloid damage
does
impair selective attention, perhaps because of a failure to assign sufficient
emotional
value to the stimuli (LeDoux, 1993). It is also known that attentional
performance is
I S influenced by the salience and the distinctiveness of the incoming
information. There
is considerable evidence that it is precisely this role that the hippocampus
plays in the
overall process of selective attention (e. g., Salzmann et al., 1993; White,
1993).
Therefore, the orbitofrontal-amygdaloid complex-hippocampal formation axis
appears
to be important in the regulation of selective attention, where the amygdaloid
complex
likely assigns salience value to a stimulus complex and the hippocampal
formation
compares salience value among stimuli. Within this framework, then, the P300
behaves as a modality-independent byproduct of the selective attention process
- a
necessary foundation to subsequent emotional, memory, and cognitive
processing.
These performance probes, then, challenge the functionality of pathways along
the
frontal-temporal axis. It is precisely these forebrain regions implicated in
the brain's
response to cocaine.
Attentional processing also has been shown to be dependent on biogenic
amine regulation (Stanzione e1 al., 1990; Scatton e~ al., 1982). Since the
precursors
for synthesizing the amines are dependent upon dietary intake, it is possible
that
dietary supplements can alter available biogenic amine stores in the brain.
This has
lead to various clinical strategies that target nutritional improvement of the
brain's
chemistry for the treatment of specific disorders (Blum. 1989c). This approach
makes
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use of normal cellular control mechanisms and can result in decided
improvement in
psychological outlook, behavioral performance, and relapse prevention. Such
nutritional improvement rests on the administration of amino acid precursors
of key
neurotransmitters in conjunction with vitamins and minerals central to
synthesis of
these neurotransmitters. It is noteworthy that all of these chemicals
(transmitters,
vitamins and minerals) have been found deficient not only in active alcohol
and drug
abusers but often remain in deficit well into recovery (Blum, 1991a).
This study, then, demonstrates that nutritional supplementation can enhance
neurophysiologic function in normal controls, and this may have important
ramifications for the utilization of amino acid supplementation in the cocaine
recovery
process (Blum et al., 1988; Trachtenberg and Blum, 1988). Future projects will
investigate how attention and memory functions are affected in recovering
cocaine
abusers. Since the various neurotransmitters, important in attention are
dependent
upon nutritional sources for their precursors, it may be possible to minimize
the
attentional defects, and/or speed recovery, by selective precursor
enhancement. The
implication of the Reward Cascade model is that each of these
neurotransmitters,
which can be shown to be functionally altered as a consequence of drug use
and/or
genetic anomalies (Bium et al., 1990; Noble et al., 1991a; Noble et al.,
1991b; Blum
et al., 1991a; Blum et al., 1991b; Blum et al., 1992; Noble et al., 1992; Blum
et al.,
1994a; Blum et al., 1994b; Blum et al., 1995b; Blum et ul., 1995a; Noble et
al.,
1995), should be manipulated to facilitate improved brain functioning and thus
potentially improve feelings, mood, and behavior. This common disease, first
termed
by Blum et al. (1995a; 1996a) "The Reward Deficiency Syndrome," is a
malfunction
of the "Reward Cascade." The inventors' results suggest that treatment of the
"Reward
Deficiency Syndrome" could be accomplished - at least in part - by amino acid
loading techniques with enkephalinase inhibitory properties.
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TABLE 69
CLASSIFYING COCAINE ABUSERS
TYPE A TYPE B
Cause of Abuse Problem more more genetic
environmental
Gender equal more male
male/female
Personality low impulsively high impulsively,
sensation
and sensation seeking
seeking, high
harm avoidance
Childhood Factors fewer early risk conduct disorder
factors
Age of Onset later earlier
Substance Abuse less severe, more more chronic and severe;
Severity episodic polydrug
Psychopathology lower severity, higher severity, more
more affective antisocial
After years of studies, researchers are able to identify factors that classify
alcoholics as Type A or Type B. Recent NIDA-funded studies show that, in
general, the same multiple criteria are valid in classifying cocaine abusers.
Results may prove useful in explaining different causes of abuse and in
designing specific prevention and treatment interventions.
EXAMPLE 17
MAA TECHNIQUE FOR OTHER POLYMORPHIC TRAITS -
CHOLESTEROL AND LOW-DENSITY LIPOPROTEIN LEVELS.
In the inventors' studies of the role of various genes in cardiovascular
diseases
the inventors have identified four genes that were associated with cholesterol
and
lipoprotein metabolism. These were the serotonin transporter (HTT), the
oxytocin
IO receptor (OXYR), the dopamine DRD2 receptor (DRD2) and the presenilin gene
(PSl)
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genes. The respective polymorphisms were the promoter insertion deletion at
the
HTT gene (Collier et al., 1996), a dinucleotide polymorphisms of the OXYR gene
(Mecheiini et al., 1995), a promoter insertion/deletion polymorphism of the
DRD2
gene (Arinami et al., 1997), and RFLP at the PSI gene (I-Iiguchi et al.,
1996). Based
on studies of a separate group of subjects the scoring was as follows: HTT
gene - 0 =
SS, LL, 2 = SL; OXYR gene 278/278 = 0, 278/267 = 1, 276/276 = 2; DRD2 11 = 0,
I2 = 1, 22 = 2; and the PSl gene 22 = 0, 12 = 1, 11 = 2.
FIG. 8 shows the use of the MAA technique in assessing the role of these four
genes in cholesterol and LDL metabolism.
These results demonstrate that the MAA technique can be generalized to any
polygenic disorder or trait. These four genes accounted for 16.2 and 11.5% of
the
variance of cholesterol and LDL levels respectively. The p values for
cholesterol
were 0.0002, and for LDL were 0.002.
EXAMPLE 18
Dopaminergic Genes, Violence and Schizoid/Avoidant Behaviors. With
regard to "pathological violence". the inventors genotyped eleven adolescents
between
the ages of 12-19, who were in a residential treatment program in San Marcos,
Texas,
for both the DRD2 and DAT1 gene variants. These subjects were selected on the
basis of a one-h structured interview diagnosed to have impulsive-aggressive
violent
behavior. Each subject selected for study had a 2.5 SD abnormal brain
electrical
activity map measured by Nicolett(TM) interpreted by a board certified
neurologist.
While 6 out of 11 subjects had the D2A1 allele (56%), I1 out of 1 I subjects
carried
the DAT1 (VNTR 10 allele}. When the D2A1 allele in the subjects were compared
to
"super-controls (1/30 or 3.3%), significant association was observed (X2 =
14.9,
df= 1, p < 0.0001). Similarly a significant association was found for the DAT1
10
allele when compared to literature controls (34/91 or 37.4%, X2 = 7.6, df = 1,
p < 0.006), but not for the 10/10 genotype (p = 0.093).
The present invention describes an association between various dopaminergic
genes and SAB. For the SAB data set the inventors genotyped a total of 109
subjects
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attending an outpatient clinic in Princeton, NJ for the three dopaminergic
genes
(DRD2, DATI, D(3H) as well as 172 screened controls. With chi square, the
inventors found that the D2A1 allele significantly associated with patients
identified
by the Millon Clinical Multi-Axial Inventory computerized test to have SAB
(score >
84} compared to D2A2 allele (X2 = 7.6, df = 1, p = 0.006). Carriers of the D2A
1
allele in this study was found in 11/22 or 50% of schizoid and 12/27 or 44% of
avoidant subjects a significant association when compared to super controls
(X2 = 16.75, df = 1, p = 0.000044). While chi square analysis failed to reveal
association of D(3HB 1 allele and DAT110/10 allele with SAB utilizing that
approach,
a significant association was found between the DATI 480bp VNTR 10/10 allele
in
those individuals diagnosed with SAB (18/28 or 68%) when compared to controls
(X2 = 6.3, df = 1., p = 0.012). A similar trend was found in the D(3HB 1
allele ( 17/23
or 76%) compared to screened controls (but not super controls) (X2 = 2.9, df =
1,
p < 0.09). Linear trend analysis showed increasing frequency of the D2A1
allele with
increasing SAB severity (A 1 /A 1 = 83%; A 1 /A2 = 41 %; and A2/A2 = 23%;
p = 0Ø005). Utilizing multiple variable associations, both D2A1 allele and
sex were
significant predictors of SAB severity. With D2A 1 allele the inventors found
an odds
ratio of 2.79 (p = 0.018) and with gender 3.6 (p = 0.007). The Hosmer-Lemeshow
goodness of fit at p = 0.778 and the combined contribution to the variance was
17.9%.
In SAB it appears that the DRD2 gene is more important than the DATI gene.
EXAMPLE 19
EXAMPLES OF SPECIFIC ASSAYS FOR GENETIC POLYMORPIiISMS
The following are examples of specific detection methods for the various
genes proposed in the present invention used to diagnose a predisposition of
RDS,
related behaviors, and other polygenic traits. Many of the specific assays are
the same
as found in the literature, and exemplify the use of such assays for
polymorphisms in
the MAA technique. One of skill in the art will recognize that modifications
can be
made to such and assay to detect a specific allelic polymorphism, and that
additional
genes and assays can be used in the MAA technique other than the ones
described
herein.
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DRD1. To examine the DRD1 gene the method involves the utilization of the
Ddel polymorphism consisting of an A to G change in the 5'UTR, treated by the
PCRTM procedure described by Cichon et al. (1994).
DRD2. The detection for this gene comprises obtaining DNA of a subject,
subjecting said DNA to digestion by TaqI restriction enzyme, separating
resultant
DNA fragments, hybridizing said separated DNA fragments to a labeled
recombinant
phage-hD2G1(ATCC#61354 and 61355) or a fragment thereof specifically binding a
6.6kbA 1 allele of the human dopamine D2 receptor, and determining the
presence of
said A1 allele of the human D2 receptor. In particular, the fragment of
recombinant
phage--hD2G1 (ATCC#61354 and 61355) may be a BamHI fragment having an about
1.7kb size. An alternative method of detection involves a PCRT"' technique
(Noble et
al., 1994).
DRD4. DNA is extracted and PCRTM amplified using VENT polymerase and
a high denaturing temperature (98°C for 1 min) with a combined
annealing and
extension reaction for 5 min at 70°C (Sommer et al., 1993). The primers
employed
are (Nanko et al., 1993), 5'-AGG TGG CAC GTC GCG CCA AGG TGC A-3' (SEQ
ID N0:23) and D4 = 42: 5'-TCT GCG GTG GAG TCT GGG GTC GGA G-3' (SEQ
ID N0:24).
DAT1. Determination of the DAT1 repeat polymorphism involves VNTR
genotyping. Genomic DNA is extracted from and amplified by PCRTM of the DAT1
40-by VNTR. The alleles at the 3' UTR are determined by PCRTM using the
oligmers
and PCRTM conditions reported by Vandenbergh et al., 1992a. Following PCRTM
amplification the products are electrophoresed in an 8% acrylamide gel with a
set of
size markers.
D~3H. D'Amato et al. (1989) reported the presence of two Taq D(3H
polymorphisms entitled A and B. A D(3H cDNA clone All (Lamouroux et al., 1987)
is used which consists of a 2.7kb insert at the EcoRI site. To improve
labeling the
vestor is digested with BamHI and SaII to produce five bands. A 3.5 kb
fragment was
labeled for testing the B polymorphism. Digestion with TagI restriction
endonuclease, electrophoresis in agarose, Southern transfer to a nylon filter,
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hybridization with 32P labeled probe, and autoradiography demonstrates
fragments of
2.8kb (B 1 ), and 1.4 kb (B2).
MAOA. The MA OA VNTR polymorphism is utilized (Hinds et al., 1992).
This complex polymorphism consists of a GT microsatellite directly adjacent to
an
. 5 imperfectly duplicate novel 23-by VNTR mofit, with alleles differing in
both the
number of dinucleotide repeats and VNTR repeats. DNA is extracted by standard
methods and then amplified by PCRT"' and each primer is labeled with
fluorescent
HEX or FAM Amidite(Applied Biosystems, Foster City, CA) primers [ < 320; 320-
333; 334; >_335]. A two ~l of the 10 fold diluted PCRTM product is added to
2.51
deionized formamide and o.5 ~.l of ROX 500 standard and denatured foe 2 min at
92C
and loaded on 6% polyacryiamide gel in an Applied Biosystems 373 DNA
sequences.
The gel is electrophoresed for S h at 1100 volts and constant 30 W. The gel is
then
laser scanned and analyzed using the internal ROX 500 standards. The peaks are
recognized by Genotypes (version I . I ) [Applied BiosystemsJ based on color
fragments sized by base pair length.
Tryptophan 2,3-dioxygenase gene PCRTM amplification of the Mutant
Region of Intron 6. The PCRT'" reaction to amplify the target sequence is as
follows:
IOmM Tris HCL, pH 8.3, 50mM Kcl, I.SmM, l.SmM MgCl2, 0.05% Tween 20,
0.05% NP-40, 100~.M each dATP, dCTP, DTTP, dGTP, 0.1 p,M primers. The
primers are:#116 GACACTTCTGGAATTAGTGGAGG (SEQ ID N0:25), and # 117
GAAGTTAAATCCATGTGGCTC (SEQ ID N0:26). The following is added to
20pL: O.SU AmpliTAq (Perkin-Elmer, Foster City, CA),1~1 (250ng) genomic DNA.
The reactions are run on a PE-9600 thermal cycles (Perkin Elmer) or a PTC-100
programmable thermal controller (MJ Research, Inc., Watertown, MA) using the
following protocol: 94 C 5 min, then 30 cycles of 94 C, 30 sec., 60 C 30 sec.,
72 C I
min., then 72 C for 5 min. To determine if amplification occurred, 101 of the
reaction mixture will be electrophoresed on a 1.5% agarose gel in TBF buffer.
From the above PCRT"' reaction a 10 p.l aliquot is digested using 1,5 units of
restriction enzyme BsII and final 1 x buffer (supplied by New England BioLabs,
Beverly, MA) and incubated at S5 C overnight. A 10 ~1 aliquot of the digested
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product was subjected to electrophoresis in a 4% metaphor agarose (F.M.C.
Products,
Rockland, ME) gel for I hr. At 100VV in IxTBE. The geI is stained in ethidium
bromide. Three sizes of the fragments are expected. When the polymorphic site
is
GIG, the DNA is completely digested giving 673 by and 359 by fragments. When
the
polymorphic site is AIA the I032 by fragment is undigested. G/A heterozygotes
had
three fragments, 1032 bp, 673bp, and 359bp.
The sequence immediately 3' to the G->T mutation is GATA. GATC is the
recognition site for the DpnII restriction. endonuclease. The 3' 23 by oligmer
is
designed to match the ATC sequence immediately 3' to the G->T mutation is as
follows (oligomers underlined and the two g sites for the mutations double
underlined);
5' TCATTAATCCTCTGGGTATTGTAAATGTGGATTTAGGTTAATATATTAT
ATATAATGCCAAATAATGGCATAGATAAGGAATAGGGAGAAAA.AGGGAATTA-3'
(SEQ ID N0:27)
TAGTCTTATATCCCTCTTTTTCTTA (SEQ ID N0:28)
This mismatch in the third position rarely compromises its effectiveness as a
PCRTM primer.
The 5' primer was chosen to provide a product of 29 base pairs. When the G-
>T mutation is G, the GATC site is cut to produce 22 and a 70bp fragments.
When
the G->T mutation is A, only a 92 by fragment is present. The conditions for
PCRTM
reaction is as follows: l uM each primer, 0.2 mM each dNTP, SOmM KCL, 1 OmM
Tris
HCL, 1.5 mMMgCl2, O.OOi%(w/v) gelatin, 2.5 U/100pL. AmpliTAq(R) DNA
polymerase, 80 ng genomic DNA. The PCRTM cycles are 94 C 4 min: 30 cycles of
94
C 30 sec., 52 C 90 sec., 72 C 120 sec.; followed by 72 C for 5 min. The
conditions for
the DpnII digestion is I Op.L of PCRTM product, 0.05 p.L of 10 U/p,L of DpnII;
1.5 p.L
of: 1M NACL, 0.5 M Bis HCL, 0.1 M MgCl2, IOmM dithiothreitol, pH 7.9: 3.Sp.L
H20, at 37 C overnight. The products are electrophoresed in 4% Metaphor
agarose.
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HTR1A repeat Polymorphism. The method used to determine this complex
polymorphism has been described by Bolos et al., 1993. To label the PCRT"'
products
0.1 pM each of fluorescence labeled primers are used in the reactions. The
fluorescent dye is FAM amidite (Applied Biosystems, Foster City, CA). Two pl
of
the 10 fold diluted PCRTM product is added to 2.5p1 deionized formamide and
0.5 pl
of ROX 500 labeled standards, denatured for 2 min. At 92°C, and loaded
on 6%
polyacrylamide gel in an Applied Biasystems 373 DNA sequences. The gel is
electrophoresed for 5 h at 1100 volts and constant 30 W, laser scanned and
analyzed
using the internal ROX 500 labeled standards. The presence of internal
standards in
each lane allows very accurate length determinations. The peaks are analyzed
by
Genotypes (version 1.1, Applied Biosystems) and sized by base pair length. if
the
computer detected two peaks of similar height, the shorter peak is always
placed in
the column of a alleles, and the longer peak in the column of h alleles. If
the
computer concluded there is a single peak, the subject is assumed to be
homozygous
for the a allele.
HTR2A Gene. The HTR2A is assessed using the single base pair
polymorphism described by Williams et al., 1996. The procedures using the
Applied
Biosystems DNA sequences are as described above.
OB gene. The dinucleotide repeats present on the YAC contig containing the
human Ob gene has been described by Green et al., 1995: D7S1873, D7S1875,
D7S514, and D7S780. Of these, D7S1875 is closest to the OB gene. Comings
refers
to this as OB1875.
Cannabinoid Receptor Gene. DNA samples are amplified using the
following primers as described by Dawson 1995,
5'-GCTGCTTCTGTTAACCCTGC-3' (SEQ ID N0:29) and 5'-
TACATCTCCGTGTGATGTTCC-3' (SEQ ID N0:30). This identified alleles of a
(AAT) n triplet repeat. Standard methods are employed to label the PCRTM
products
and to determine resultant polymorphisms, (see above)
GABRB3 Gene. DNA samples are amplified using the following primers
described by Mutirangura et al., 1992. CA
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strand:5'-CTCTTGTTCCTGTTGCTTTCAATACAC-3' (SEQ ID N0:31 ) and GT
strand: 5'-CACTGTGCTAGTTTAGATTCAGCTC-3' (SEO ID NW~~I Th;~
identified 11 alleles of a (CA)n repeat varying in length from 181-201bp.
Standard
methods are employed to label the PCRT"' products and to determine resultant
polymorphisms. (see above).
Neuronal Nitric Oxide Synthase Gene. The nitric oxide synthase gene has
recently been implicated in aggressive behavior in mice (Nelson et al.. 1995).
Utilizing the methods of Hall et al. (1994). The inventors examined the
relevance of a
dinucleotide repeat polymorphism of the neuronal nitric oxide synthase gene
(nNosl a). The procedures using the Applied Biosystems DNA sequencer are as
described above.
COMT Gene. For analysis of the COMT gene the single base pair
polymorphism described by Lachman et al., 1996. This polymorphism has been
shown to be associated with different levels of activity of COMT (see Daniels
et al.,
1995).
Apolipoprotein-D Gene. DNA is extracted and digested with the restriction
enzyme TaqI. After agarose gel electrophoresed it is transferred on to a Nylon
membrane(Hybrid N. Amersham, UK) and hybridized with 32P-labeled APO-D probe
using standard methods. Two alleles, 2.2 and 2.7kb has been identified.
Human Chromosome-2. A microsatellite polymorphism, D2S 1788 which
maps to 2p21 (approximately 74cM from the tip of the short arm) has been
identified
(Comuzzie et al., 1997). DNA is prepared from lymphocytes and used for PCRTM
with fluorescently labeled primers from the MapParis 6a Linkage Screening Set
(Research Genetics) containing 169 highly polymorphic microsatelIite markers
spaced
at approximately 20cM.
UCP-2 Gene. This gene maps to regions of human chromosome I l and
mouse chromosome 7 that have been linked to obesity and hyperinsulinaemia. For
this gene the forward primer sequence is 5'-CATCTCCTGGGACGTAGC-3' (SEQ ID
N0:33) and the reverse primer sequence is 5'-AGAGAAGGGAAGGAGGGAAG-3'
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(SEQ ID N0:34). GenBank accession for the human UCP2 coding sequence is
U76367.
EXAMPLE 20
EXAMPLES THE MAA TECHNIQUE FOR GENETIC POLYMORPHISMS
This example presents number of polygenic traits that are independent of
psychiatric disorders and illustrate that the MAA technique can be generalized
to all
polygenic disorders and all polygenic traits. The following teaches how a
variety of
examples of different polygenic disorders could be studied using the MAA
technique.
Osteoarthritis
Step 1. Identify the polygenic disorder or trait to be studied. Generalized
osteoarthritis (GOA) is the most common from of joint disease. It is a major
cause of
disability and ranks as one of the top three health care problems in the
developed
world. GOA results in pain and loss of function in 10 to 15% of men and women
over
age 4~ and up to 70% of those over age 60. The etiology of GOA is complex,
I S involving environmental and genetic factors. A twin study in women
estimated that
additive genes accounted for 54% of GOA (Kaprio et al., 1996). Sporadic GOA is
inherited as a polygenic disorder.
Step 2. Set up a scale that measures the severity of the polygenic disorder.
The severity of OA is determined on the basis of x-rays using the Kellgren
score
(Kellgren and Lawrence, 1957).
Step 3. Identify the candidate genes to be tested. OA is very likely to be due
to presence of a threshold number of variant genes that play a role in the
synthesis or
degradation of cartilage. This concept and some of the potential candidate
genes are
Table 70. Candidate genes in OA based on their role in regulating the
synthesis or degradation of cartilage.
Step 4. Identify one or more polymorphisms associated with each gene. The
following is a list of some of the polymorphisms that can be used in the MAA
technique for OA.
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TABLE ~a
Gene Chrom. Type poly. Reference
Collagen
genes
COL2A 1 12q 12 VNTR-1 Wu et al. , 1990
COL2A1 12q12 VNTR-2 Priestley et al., 1990
COL2A1 12q12 Mae II RFLP Loughlin et al., 1995
COL9A1 6q12 DN-1 Warmen et ul., 1993
COL9A 1 6q 12 DN-2 - Warmen et al. , 1993
Aggrecan te proteoglycan
(chondroitin I )
sulfa
AGCI 15q26 RFLP Finkelstein et al.,1989
Insulin-like
growth factors
and receptors
IGF1 12q22 DN-1 Weber et crl. (Weber and
May, 1989)
IGFI I2q22 DN-2 Polymeropoulos et ul., 1991
IGF1R 15q25 TN Meloni et al., 1992
IGF1R 15q25 insertion Poduslo et al., 1991
IGF2 11p15 DN Rainer et crl., 1993
IGF2R 6q25 TN Ogawa et al., 1993
Transformingrowth r Beta
G Facto
TGFBI 19q13.2 Leu->Pro Cambien et al., 1996
RFLP
TGFB2 1q41 DN Westson et al., 1991
Interleukin
1
IL 1 A 2q 13 TN Zulini and Hobbs, 1990
IL1B 2q13 C->T RFLP diGiovine et ul., 1992
IL1R1 2q12 DN GDB
IL1RN 2q14 VNTR GDB
Metalloproteinases
MMP9 20p11.2 DN St Jean et al., 1995
MMP tissue MP l
inhibitors
of M
TIMP1 Xp11.3 RFLP-1 Allred and Wright, 1991
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Gene Chrom. Type poly. Reference
TIMPI Xp11.3 RFLP-2 Allred and Wright, 1991
Vitamin D3 12q Uitterman RFLP et al., 1997
VNTR = variable tandem repeat.
DN = dinucleotide repeat.
TN = trinucleotide repeat.
RFLP = restriction fragment length polymorphism.
Step 6. Set up a dummy polygenic or PG variable. The scores for the
different candidate genes are added to produce the PG scores.
Step 7. Perform univariate regression analysis of PG versus QT or DV.
Univariate regression analysis using any statistical program is performed
against PG
with the scores for the first OA (oa) candidate gene (cg) (PG + PG oacg 1 ),
then with
the addition of the second candidate gene (PG + oacg2). This is continued
until "n"
OA candidate genes are added (PG + oaegn), where n is the number of genes
added.
Step 8. PIot the results. The results are then graphed as shown for additive +
subtractive genes for the ADHD score in FIG. 4.
I 5 Step 9. Repeat the procedure using only the additive genes. Only the
additive
genes are plotted as shown for the additive genes for the ADHD score in FIG.
5.
Cholesterol Levels
Step 1. Identify the polygenic disorder or trait to be studied. Years of
research have shown a correlation between the elevated levels of blood
cholesterol
and coronary artery disease and stroke. Since genetic factors as well as diet
play a
role in cholesterol levels, the identification of the genes involved can be
important in
. identifying individuals at risk, and in the development of new drugs for
lowering
cholesterol levels.
Step 2. Set up a scale that measures the severity of the polygenic disorder.
The blood cholesterol level based on a fasting sample would provide the best
severity
scale.
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Step 3. Identify the candidate genes to be tested. A number of genes directly
involved in cholesterol synthesis pathways have been proposed as playing a
role in
cholesterol metabolism. To illustrate the power of the MAA technique the
inventors
used it to assess the role of several genes outside the cholesterol pathway
per se, to
identify their possible role in the regulation of cholesterol levels. The
inventors
identified four genes that were associated with cholesterol and lipoprotein
metabolism. These were the serotonin transporter (HTT), the oxytocin receptor
(OXYR), the dopamine DRD2 receptor (DRD2) and the presenilin gene (PS 1 )
genes.
Step 4 Identify one or more polymorphisms associated with each gene. The
respective polymorphisms were the promoter insertion deletion at the HTT gene
(Collier et ul., 1996), a dinucleotide polymorphisms of the OXYR
gene(Mechelini et
al., 1995}, a promoter insertion/deletion polymorphism of the DRD2 gene
(Arinami et
al., 1997}, and RFLP at the PS 1 gene (Higuchi et crl., 1996).
Step 5. Assign a score to the genotypes. Based on studies of a separate group
of subjects the scoring was as follows: HTT gene - 0 = SS, LL, 2 = SL; OXYR
gene
278/278 = 0, 278/267 = 1, 2761276 = 2; DRD2 11 = 0, 12 = 1, 22 = 2; and the PS
I
gene 22 = 0, 12 = 1, 11 = 2.
Step 6. Set up a dummy polygenic or PG variable. The scores for the
different candidate genes are added to produce the PG scores.
Step 7. Perform univariate regression analysis of PG versus QT or DV.
Univariate regression analysis using any statistical program is performed
against PG
with the scores for the first cholesterol (c) candidate gene (PG + ccgl), then
with the
addition of the second candidate gene (PG + ccg2). This is continued until all
the
cholesterol candidate genes are added (PG + ccgn).
Step 8. Plot the results. The results are then graphed as shown for the ADHD
score in FIG. 5. FIG. 8 shows the use of the MAA technique in assessing the
role of
these four genes in cholesterol and LDL metabolism.
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Step 9. Repeat the procedure using only the additive genes. Only the additive
genes are plotted as shown for the additive genes for the ADHD score in FIG.
5. In
this case all four genes were additive. .
Longevity
-- 5 Step 1. Identify the polygenic disorder or trait to be studied. Aging is
a
subject of increased interest as the average age of survival increases and as
the number
of individuals over the age of 65 in the population increases. The
identification of a
number of genes that can predict low long a person will live has value in
identifying
individuals at risk to die early. Interventions directed toward the effects of
certain
critical genes, could be of considerable benefit in prolonging quality of life
as well as
life itself.
Step 2. Set up a scale that measures the severity of the polygenic disorder. A
natural severity scale for aging is age in years. However, in this case, the
higher the
score (older the age) the better the phenotype.
i5 Step 3. Identify the candidate genes to be tested. While certain genes,
such as
superoxide dysmutase, a free radical scavenger, have tong been known to be
involved
in animal models of aging, as with cholesterol metabolism, the MAA technique
has
the potential of identifying a number of new genes previously unsuspected
genes, that
play role in longevity. The inventors chose to examine two of the genes that
played a
major role in cholesterol levels, since cholesterol levels correlate with
death from
cardiovascular disease. The inventors also included the APOE gene.
Step 4. Identify one or more polymorphisms associated with each gene.
These genes were scored in the following fashion. The frequency of the
different
genotypes of the candidate genes was compared by chi square analysis across
groups
of different aged subjects. If certain genotypes progressively increased in
frequency
across these age groups, this genotype was scored as = 2. The remaining
genotypes
were scored as = 0 or = I depending upon their relative frequency in these
different
aged subjects.
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Step ~. Assign a score to the genotypes. The scores for the different
candidate genes were added to produce the PG scores. Each gene is
progressively
added to the polygenic score.
Step 6. Sct up a dummy polygenic or PG variable. The scores for the
different candidate genes are added to produce the PG scores.
Step 7 Perform univariate regression analysis of PG versus QT or DV.
Univariate regression analysis using any statistical program is performed
against PG
with the scores for the first longevity (1) candidate gene (PG + lcgl), then
with the
addition of the second candidate gene (PG + lcg2). This is continued until all
I 0 longevity candidate genes are added (PG + lcgn).
Step 8. Plot the results. The results are then graphed as shown for the ADHD
score in FIG. 5. When plotting the data for genes vs. longevity, the MAA
technique
identified three genes which when combined in the study of 208 subjects, gave
highly
significant results with p = 1.5 x 10-7.
I S Step 9. Repeat the procedure using only the additive genes. Only the
additive
genes are plotted as shown for the additive genes for the ADHD score in FIG.
5.
These are just three examples of how the MAA technique can be generalized
to any polygenic disorder or trait, using any human gene that is relevant to
the
disorder or trait to be studied. As the genome project nears its conclusion in
the next
20 decade, it is expected polymorphism will have been identified for every
human gene,
allowing a through testing for every polygenic disorder or trait. The
inventors
contemplate that the MAA technique is also applicable to polygenic traits in
other
animals, and plants. and the use of this technique in other species is
encompassed by
the invention.
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EXAMPLE 21
EXAMPLE OF ANTI-CRAVING COMPOSITION AND ENHANCEMENT OF
COMPLIANCE TO TREXAN° FOLLOWING RAPID DETOXIFICATION
FROM OPIATES IN HARDCORE ADDICTS
Introduction. Over the last decade, a new rapid method to detoxify either
methadone or heroin addicts utilizing the narcotic naltrexone (Trexan°,
Dupont,
Delaware) sparked interest in many treatment centers throughout the United
States,
Canada, as well as many other countries on a worldwide basis. The dropout rate
among hardcore opiate addicts even today approaches 90 percent. The basic
concept
in this rapid method is to provide the patient with a pure narcotic antagonist
to block
the opiate-induced euphoriant effects. Utilizing this approach most patients
do not
comply and the recidivism rate is over 99% (S. Hall, San Antonio Methadone
Clinic).
It is the inventors contention that the major reason for non-compliance is due
to the
fact that while the narcotic antagonist (Trexan'~'} blocks the opiate or
alcohol-induced
euphoria (O'Malley et al., 1992; Volpicelli et ul., 1992), the drug has little
effect on
craving behavior. Since the inventors found that amino-acid therapy reduces
craving
behavior for a number of euphoriants, they decided to test whether the
combination of
both Trexari and amino-acid therapy prolongs compliance to Trexan° in
hardcore
addicts, who have used euphoriants up to 30 years.
Method. This study was accomplished at the San Antonio Methadone Clinic.
The criteria for entry into the study included both male and female patients
who were
considered hardcore addicts as diagnosed utilizing DSM III for heroin/opiate
dependence. Each patient was pre-evaluated by first receiving an injection of
0.4-0.8 mg. Narcan and their withdrawal was assessed (if it was too severe,
they were
not allowed entry into the study). If they passed this first test, they were
then
administered an oral dose of 12.5 mg. of Dupont's Trexan'~' and then evaluated
for
withdrawal symptoms over one-and one-half h. If the patient passed this test,
they
were given 50 mg, of Trexan~ per day. In this study, a total of 12 patients
were
evaluated. For this study, each patient besides receiving the narcotic
antagonist
according to the above regiment, also received the following amino-acids
daily:
DL-phenylalanine (2,700 mg.), L-tryptophan (450 mg.), L-tyrosine (300 mg.),
L-glutamine (150 mg.), Chromium (as picolinate-200 meg.), and
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pyridoxal-5-phosphate (30 mg.). The number of days without a relapse or self
report
of refusal to take either the Trexan~ alone or in combination with the amino-
acid
formula was counted. Each patient was evaluated (with some degree of failure
to
make contact) on a daily basis via phone or personal contact.
Results. The results were dramatic in terms of significantly enhancing
compliance to continue utilizing Trexan'~'. The average number of days of
compliance
that the San Antonio Methadone Clinic calculated on over hundreds of their
patients,
without amino-acid therapy, confronted with this rapid detoxification approach
is 37
days. In striking contrast, the 12 subjects in this study receiving both the
'rrexan'~' and
amino-acid therapy was relapse-free or reported taking the combination for an
average
of 262 days (p at least p < 0.05).
Conclusion It is suggested that the addition of the anti-craving formula
significantly reduced the craving for opiates and, therefore, seems to be
important in
assisting those hardcore opiate addicts in preventing relapse -- especially in
conjunction with the narcotic antagonist Trexan«.
EXAMPLE 22
THE D2 DOPAMINE RECEPTOR GENE AS A DETERMINANT
OF REWARD DEFICIENCY SYNDROME
Summary. The dopaminergic system, and in particular the dopamine DZ
receptor, has been profoundly implicated in reward mechanisms in the brain.
Dysfunction of the D2 dopamine receptors leads to aberrant substance seeking
behavior (alcohol, drug, tobacco, and food) and other related behaviors
(pathological
gambling, Tourette's syndrome, and attention deficit hyperactivity disorder).
The
W ventors propose that variants of the Dz dopamine receptor gene are important
common genetic determinants of the 'reward deficiency syndrome'.
The inventors have used Bayes (Rosner, 1986) theorem as a mathematical
method to evaluate the predictive value of the A~ allele of the DRD, gene in
impulsive-addictive-compulsive disorders.
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Bayes' theorem is widely used in medicine to predict the likelihood that a
particular event (defect) will result in an another event (disease) here, for
example,
that possession of the Ai allele of DRDZ will cause abnormal drug and alcohol
seeking
behavior (Table 72).
_. 5 When a screening test is evaluated, sensitivity is the probability that
the test
will be positive in a person with the disease in question; and specificity is
the
probability that the test will be negative in a person who does not have the
disease.
For Bayes' theorem The inventors used the following formula:
Predictive value = (Prevalence) (sensitivity)
(Prevalence)(sensitivity) + ( 1 - prevalence)( 1- specificity)
TABLE 71
SUMMARY OF DOPAMINE D2 RECEPTOR GENE VARIANTS AND
SUBSTANCE ABUSE/DEPENDENCE
Prevalence
Substance Abuse Allele Abusers Controls P value < References
Alcoholism DRD~ A 69 20 0.001 Blum et
1 al.,
19901
Alcoholism DRD~ A 30 19 NS Nakajima,
1
19891
Alcoholism (less severe) DRD2 B~ 17 13 NS Blum et
al.,
1993 i
Alcoholism (less severe) DRD2 C~ 57 33 0.002 Suarez
et
al. , 19941
Severe alcoholism DRDZ A, 47 17 0.001 Blum et
al.,
1991 i
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Prevalence
Substance Abuse Allele Abusers Controls P value References
<
Severe alcoholism DRDZ
B!
Severe alcoholism DRD,~nb-~"~39 16 0.02 Zhang
et
haplotype al.,
1 19941
Cocaine dependence DRDZ 51 18 0.0001 Noble
A, et
al.,
19931
Cocaine dependence DRDz 39 13 0.01 Noble
B~ et
crl.,
19931
Polysubstance abuse DRDZ 44 28 0.25 Comings
A i et
al.,
19941
Polysubstance abuse DRD~ 33 20 0.001 Smith
B, et
al.,
I992i
*C, allele denoted only regard to homozygote
with genotype. Alcoholics
(47182); controls (29/87):= 9.8, df = 1,
(x'' P = 0.002)
T ABLE 72
THE DOPAMINE D, RECEPTOR
GENE AS A PREDICTOR
OF COMPULSIVE
DISEASE
Risk Behavior Predictive Vaiue (%)
Alcoholism (severe) I4.3
Cocaine dependence (severe) I2_3
Polysubstance abuse I2.8
Chemical dependency 28.3
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Risk Behavior Predictive Vaiue (%)
Overeating (severe) I8.6
Ingestive behavior 35.0
ADHD 16.0
Smoking 41.5
Pathological gambling 4.G
Tourette's syndrome 5.5
Total impulsive-addictive-compulsive behavior 74.4
The assumptions supporting the data are explained in Blum et al., 1995
To calculate the specificity, the inventors used well-characterized controls,
screened for alcohol, drug, and tobacco use in some samples (Table 71 ). No
previous
study has used rigid exclusion criteria for controls (Blum et al., 1995a), and
such
efforts are essential because alcoholism per se is not the true phenotype
associated
with DRD2 gene polymorphisms (Blum et al., 1995b; Neiswagner et al., 1995;
Comings et al., 1991 ). More-over, to calculate the sensitivity of genotyping
the
inventors took data from studies where the probands were characterized for
chronicity
or severity of disease (Table 72).
The positive predictive value (PV+) of a test is the percentage of positive
results that are true positives when the test is applied to a population
containing both
healthy and diseased individuals (Galen et al., 1975). With the Taql A,
genotype,
PV+ was 0.744 or 74%; in other words, positive predictive value was high; but
PV-
was only 0.548 or 54.8%. The inventors would expect better negative predictive
value in studies where individuals with related impulsive-addictive--
compulsive
behaviors are excluded from the control groups. Pooled data on patients with
these
disorders point to a strong positive correlation with the DRD2 gene variant
(Yates
x2=b8.38, df=1, P < 10-').
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EXAMPLE 23
ATTENTION DEFICIT - HYPERACTIVITY DISORDER
SYMPTOMS ASSESSMENT SCALE {ADHD SAS)
Background. Recent literature (over the past fifteen years) indicates the
diagnostic criteria for Attention Deficit Disorder and Hyperactivity Disorder
are quite
variable. The nature of Attention Deficit Disorder (ADD) remains a
controversial
issue in research on childhood psychopathology (McGee, et. al. 1989).
While parents, teachers, youngsters, pediatricians, family medicine
practitioners, psychologists, school counselors, etc., know and have observed
Attention Deficit Disorder symptoms and Hyperactivity Disorder symptoms in
youngsters, we, in North America, have experienced difficulty in describing
the
phenomenon. Many studies in the literature indicate clinicians continue to
have
difficulty deciding if hyperactivity and attention deficit disorders actually
exist as
diagnostic conditions, if they are coexisting conditions, or if they are
separate
1 S diagnostic entities.
Attention deficit disorder with hyperactivity (ADHD) and without
hyperactivity (ADD) is a widespread. It afflicts between three million and
four
million children in the United States, as well as a larger number of adults.
People with ADHD suffer from overload; that is, they have heightened
awareness of incoming stimuli, particularly sight, sound and touch. They are
so
bombarded by the normal stimuli in their environment that they cannot filter
out the
background "noise" and concentrate on the task before them. They have trouble
focusing on a problem or a task. With a short attention span, they forget
appointments,
forget to pay bills, miss deadlines, and have frequent legal difficulties
because they do
not take care of problems as they arise. Always in a hurry, they have trouble
settling
on a goal or objective.
People with ADHD tend to be disorganized. Children have messy rooms;
adults have cluttered desks; daily activities tend to be chaotic. At all ages
they have
trouble making plans and even more trouble in carrying out plans in an orderly
fashion. Because of their inability to focus, those with ADHD have trouble
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completing what they start. They leave tasks unfinished, plans unrealized.
Attics and
basements are likely to be filled with partly completed sewing projects,
woodworking
projects, repairs, notebooks; desk drawers are likely to be cluttered with
unfinished
letters, outlines and project plans.
ADHD has nothing to do with intelligence. Many people with the disorder are
highly intelligent, but they tend to be underachievers because they can not
concentrate
- or sustain interest. As a result, family, friends, teachers and coworkers
become
impatient and expect them to fail.
People with ADHD have trouble adapting to change. Their life is so full of
tumult that even a minor additional change in their routine can be upsetting.
A parent
goes away on a trip, a new teacher takes aver a class, the family moves to a
new city,
a pet dies - any such change can create a crisis for a person with ADHD.
ADHD afflicted people live under stress so severe they can not tolerate
frustration; and when they are frustrated, they are likely to become angry.
The anger
tends to come suddenly and explosively slamming doors, harsh words, tantrums.
Children get into fights, adults blow up and lose jobs and alienate friends.
Afterwards
they are sorry, but the damage is done.
With their high level of frustration, people with ADHD are impatient. They
hate to wait in line, and delays of any kind make them frantic. Whatever is
going on -
a trip, a movie, a class, a discussion - they want it to go quickly and be
finished.
Their impatience makes people with ADHD impulsive. As children, they leap
into action without thinking of consequences. As adults, they drive too fast,
use
power tools carelessly, and plunge into activities without thinking of the
danger. The
result is they often hurt themselves or others.
People with ADHD have trouble with their orientation to time and space.
They may have to stop and think which is their right hand and which is their
left; they
- may have difficulty following a set of instructions, reading a map or
telling time.
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Many ADHD afflicted individuals are hyperactive. As babies or children they
constantly are on the move, squirming, twisting, and getting into everything.
As
adults, they are restless, easily bored, rebellious when asked to follow a
routine and
always on the move.
Another difference in those with ADHD is that they have abnormal brain wave
patterns. Their Beta waves - brain waves associated with concentration - are
low, and
their Theta waves - associated with relaxation - are high. which has been
associated
with daydreaming and drowsiness. It is not surprising, therefore, that
activities
associated with Beta waves, watchful anticipation and problem solving, are
difficult
for individuals with ADHD to sustain. They like activities that permit them to
stay in
a Theta state with a minimum of outside stimulation (Lubar et al., 1991 ).
If you look at these symptoms together, a picture emerges: an individual
suffering from overload, trying to adjust to a world that is too bright, too
loud, too
abrasive and too rapidly changing for comfort.
Early speculation about the causes of ADHD focused on such factors as
marital disorder, poor parenting, brain damage, psychiatric illness, or
alcoholism or
drug abuse in the family. Associated behaviors included conduct disorder and
anti-
social personality. Later these behaviors were shown to be linked hereditarily
to
Substance Use Disorders (SUDs). Most recently, research has begun to show a
significant association between these behavioral disorders, ADHD. and specific
genetic anomalies.
What is the cause or basis of ADHD? It is a compulsive disorder, genetic in
origin, that results from imbalances of neurotransmitters. It strikes in
childhood and
continues into adulthood. Its effects can be eased by treatment and
counseling. The
biological basis for this disorder has been established by a number of
investigators
(Biederman et al., 1992).
In very simplistic terms, the immediate cause is that those with ADHD are
afflicted with a defective filtering system. In other words, their brain stem
reticular
formation does not block out irrelevant stimuli. These people are aware of
every
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sound, every object, every touch, and they all mer;=a in a disorganized,
unbearable
bedlam. Non-essential stimuli get the same attention as these essential to
work or
relating to other people.
At a deeper level, ADHD is a problem of communication among brain cells, or
S neurons, possibly involving the neurotransmitters that carry interneural
messages in
ADHD people. These brain messengers are in short supply. If the messengers
that
_ inhibit incoming stimuli are deficient, too many signals get through and
create
confusion.
At a still deeper level, the problem lies in the genes that lay down the
blueprint
for manufacturing neurotransmitters. People with ADHD have at least ane
defective
gene; the DRDZ gene that makes it difficult for neurons to respond to
dopamine, the
neurotransmitter that is involved in feelings of pleasure and the regulation
of
attention. Other studies on genetic anomalies have implicated other
dopaminergic
genes such as the DRD4 receptor gene, the dopamine beta hydroxylase (D(3H)
gene,
1 S and the dopamine transporter genes as causative factors in ADHD {Cook et
al.. 1995;
Waldman, et al.. 1996).
Genetic Testing. Due to the genetic complexity of the etiology of ADD and
ADHD, the inventors contemplate to assess the presence of ADD and/or ADHD
through the use of the Multi-Plex DNA Based Reward Deficiency Gene Test. This
genetic test measures for the presence of the polymorphisms present in the
genetic
structure of the person being tested. The Multi-Plex DNA Based Reward
Deficiency
Gene Test will indicate if the polymorphisms are present and if so which ones
and
whether they are homozygotic or heterozygotic. Since there is a relationship
between
the severity of the ADfID impairment and symptoms and the particular alleles
of the
2S genes mentioned above. The Muiti-Plex DNA Based Reward Deficiency Test will
be
able to predict the severity of the ADHD symptoms and behavior in a person.
The Multi-Plex DNA Based Reward Deficiency Test is a very valuable tool
- for the treatment professional. Although the defect cannot be corrected at
this time,
the compounds disclosed herein as = Anti-Craving Agents have proven clinically
effective in curbing craving by stimulating: the brain's production of
Dopamine, and
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the activity level of the Dopamine receptor sites; and. they have been shown
to be
effective in increasing a person's ability to focus attention more sharply, to
facilitate
focus shifting, to increase "on task" behaviors, and to increase attention
span.
While the measurement of the Reward Deficiency Syndrome behaviors
(ADHD is one genetically based disorder which falls with this syndrome) are
quite
highly reliable and valid for RDS, differential diagnoses are more
challenging. The
Mufti-Plex DNA Based Reward Deficiency Test will give outstanding confirmatory
data and generalized differential diagnoses; however, the inventors currently
are
outlining a comprehensive (and rather rapidly completed) research study with
the
University of North Texas Health Sciences Center DNA Identity Laboratory and
the
University of Tennessee. This study will be a linkage study rather than an
association
study. The inventors believe that the results of this current study will allow
the
inventors to: make a confirmed differential diagnosis, reduce false positives
in the
diagnosis of ADHD {currently, many diagnoses with the current state of the art
of
1 S psychometric testing and interview techniques are false positives),
enhance true
positives, reduce denial in the patient and in his/her families, reduce
erroneous
diagnoses (mistaking anxiety for ADHD, etc. ) and thereby change faulty
prescriptions
for Ritalin, enhance treatment plan strategies, and make true differential
diagnoses
between ADD and AHIHD.
The Multi-Plex DNA Based Reward Deficiency Test Kit has been engineered
to detect the dopaminergic genetic defect. The Test is 99.9% accurate and is
non-
invasive. It requires the use of a buccal swab; therefore, no special training
is required
to administer the test and the test does not require blood to be drawn (as
other DNA
tests require). The swab is then forwarded by mail or courier to a certified
DNA
laboratory for processing. The inventors have an exclusive contract for this
particular
DNA testing with the University of North Texas Health Sciences Center DNA
Identity Laboratory. Results are processed within 72 to 96 hr (if a "scat"
assessment is
requested, the time will be on the order of 24 to 48 hr).
In the second edition of the Diagnostic and Statistical Manual of Mental
Disorders (DSM II) published by the American Psychiatric Association (1968)
the
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diagnostic category was "Hyperkinetic Reaction of Childhood." In the third
edition of
the Diagnostic and Statistical Manual of Mental Disorders, (DSM lll),
published in
1980, this category was broken into two separate diagnoses, "Attention Deficit
Disorder with Hyperactivity" (ADD-H) and "Attention Deficit Disorder without
S Hyperactivity" (ADD).
The revised, third edition of the Diagnostic and Statistical Manual of Mental
_ Disorders (DSM lll-R) was published in 1987. In this edition the American
Psychiatric Association chose again to merge these two diagnostic categories.
The
diagnostic category at that time was "Attention Deficit - Hyperactivity
Disorder"
(ADHD).
The current edition of the Diagnostic and Statistical Manual of Mental
Disorders (DSM IV), published in 1994, lists three diagnostic formulations:
"Attention - Deficit I Hyperactivity Disorder, Combined Type;" "Attention -
Deficit I
Hyperactivity Disorder, Predominately Inattentive Type;" "Attention - Deficit
/
Hyperactivity Disorder; Predominately Hyperactive - Impulsive Type."
There was a significant difference in the "goodness of the fit" between the
diagnostic criteria for DSM Ill's "Attention Deficit Disorder with
Hyperactivity" and
DSM lll-R's Attention-Deficit Hyperactivity Disorder." (Newcorn, et. al.,
1989). It
would seem that, with the continuing changes in the diagnostic criteria of
this
condition and with the lack of significant agreement between groups of
diagnostic
criteria, assessment scales constructed to meet the diagnostic criteria of any
of the
Diagnostic and Statistical Manual of Mental Disorders editions or revisions
would be
of limited applicability. This point was made by others "structured diagnostic
instruments, such as the "Diagnostic Interview Schedule for Children (DISC)"
(Costello, 1983) and the "Diagnostic Interview for Children and Adolescents
(DICA)"
(Herjanic and Campbell, 1977), were based on DSM III criteria and cannot be
considered adequate for subject selection in the new diagnostic system."
(Newcorn, et
al., 1989).
Attention deficit disorder is the most widely diagnosed childhood disorder.
On the surface, it is rather curious that the diagnostic descriptions of this
condition are
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undergoing so many changes; however, the symptoms of this condition are not
manifested constantly and in all surroundings (Brown, 1986}. Only 20 percent
of
Attention Deficit - Hyperactivity Disorder youngsters demonstrated ADHD
symptoms
during a pediatric examination (Sleator and Ullman, 1981). The frequently many
youngsters referred for a psychological evaluation due to hyperactivity were
reported
by the consulting psychologists to be quiet and cooperative in the actual
testing
environment (Tobiessen and Karowe, 1969). The status of ADD as a disorder
would
be more assured if there were a unique pattern of attentional or cognitive
correlates
which discriminated ADD from other disorders (McGee, et. al., 1989).
Due to this variability in the display of symptoms, the ADHD SAS is designed
for responses primarily from parents, teachers, or others who are familiar
with the
behaviors of the youngster. Therefore, those individuals who interact with the
youngster most intensely and most frequently are in the best position to
evaluate and
report the behavior of the youngster. Reports in the literature (Schachar et
al., 1986;
Atkins et al., I985} indicate teacher ratings of childhood and adolescent
behavior is
highly correlated with clinician ratings, neuropsychological assessments, and
direct
classroom observation data.
The ADHD SAS was developed in order to provide a quick assessment of the
Ievel of Attention Deficit - Hyperactivity Disorder symptoms being
demonstrated by a
youngster over a period of time. This assessment scale is not meant to be the
definitive diagnostic tool for use in diagnosing the hyperactive or
hyperkinetic
youngster. Making an accurate, definitive diagnosis of the extent, expression,
and
limitations of Attention Deficit - Hyperactivity Disorder requires many in-
depth
testing sessions with practitioners from several professional areas including
developmental pediatrics, pediatric neurology, clinical psychology,
neuropsychology,
speech and language, special education, and occupational therapy. Also this
scale was
not developed to replace an in-depth diagnostic work-up. This type of work-up
requires the collaborative efforts of the wide variety of professional
practitioners who
represent the fields listed above. So this scale is not a psychological
assessment,
neurological assessment, psychiatric =assessment, neuropsychological
assessment,
pediatric assessment, etc.
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Many professionals who frequently work with children and adolescents
require a scale which is rapidly administered, scored, and interpreted which
indicates
the presence and level of Attention Deficit - Hyperactivity symptoms. Part of
this
need is evident due to the incidence figures which have been reported. The
incidence
of this condition range from a low of less than one percent of all school aged
children
up to over 20 percent of all school aged children (August and Garfinkel,
1989).
According to the DSM IV, the prevalence of Attention Deficit/Hyperactive
Disorder
is estimated at 3% to 5% of the school-aged children. Data on prevalence in
adolescence and adulthood are limited.
IO In many instances parents and teachers note the behaviors characteristic of
Attention Deficit Hyperactivity Disorder which might be interpreted as
laziness,
stubbornness, lack of interest, anger, immaturity, etc. At times teachers,
counselors,
primary care physicians, and others in the health care and educational fields
interpret
the ADHD youngster's behaviors as a lack of discipline or the result of faulty
15 discipline practices on the part of the parents. And, since ADHD youngsters
do not
demonstrate their symptoms consistently, the descriptions of the youngster's
behavior
by a concerned parent may be interpreted as coming from a parent who is over-
loaded
with the responsibility of parenthood rather than dysfunction in the child
(since the
child is so quiet and well behaved in the doctor's office). When parents or
teachers act
20 on these assumptions or interpretations, they exacerbate the youngster's
reaction to his
underlying problems. A significant value of the ADHD SAS is its ready and
economical availability to counselors, teachers, family medicine
practitioners,
pediatricians, neurologists, and other professional practitioners to quickly
and easily
determine the level of attention deficit and/or hyperactivity symptoms present
in a
25 youngster. Therefore, the professional practitioner is able to validate or
refute the
assumptions or interpretations the parents or others are making relative to a
youngster's behaviors.
The ADHD SAS is a forty-three item scale which can be answered by either
parents or teachers, scored, analyzed, and interpreted in approximately I S
min. The
30 scale is an objectively scored instrument which asks parents or others
familiar with
the youngster's behavior to respond regarding the presence and frequency of
behavior,
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attitudes, or feelings. The scale requests parents to respond in an objective
fashion on
a four point Likert scale. The options on the scale range from "None or a
Little of the
Time" up through "Most or All of the Time."
The ADHD SAS is an efficient, cost-effective screening device. It is easy to
administer and score, and can be used by trained technicians or
paraprofessionals
under the supervision of a qualified professional. However, the ADHD SAS does
not
obviate the need for a thorough, skilled clinical assessment of youngsters.
Since the
format of this assessment device is a parental report form, it is particularly
susceptible
to conscious and unconscious distortions. For this reason and because of other
specific limitations of the instrument detailed below, the ADHD SAS should be
used
only as an indication of the presence and level of attention deficit
hyperactivity
symptoms: it should not be the solely used instrument upon which one would
plan a
treatment intervention or a course of treatment. The screening device is
designed to
be a supplement to skilled clinical judgments, not a replacement for them.
1 S Individuals with Attention Deficit Disorder with and without hyperactivity
may differ in their core attention deficits (Lahey, et. al., 1985). Both were
rated by
teachers as exhibiting similar attention deficits compared to controls on
items
referring globally to attention span, forgetfulness, difficulty following
directions, and
immaturity. However, compared to both the Attention Deficit Disorder without
hyperactivity and the control groups in their study, the Attention Deficit
Disorder -
Hyperactivity group was described as irresponsible, distractable, impulsive,
answering
without thinking, and sloppy. When considering their findings with the
(DeLamater
and Lahey, 1983; Cahey 1994), there emerges a picture of two rather different
groups
of children with problems of attention.
The ADHD SAS is constructed in such a manner as to assess both of the
prevalent types of Attention Deficit - Hyperactivity Disorders (August and
Garfinkel,
1989), as well as the overall level of ADHD disorder symptoms. The results of
the
ADHD SAS reflect the presence and quantified level of: Overall Attention
Deficit-Hyperactivity symptoms (or ADHD, Combined Type), Attention Deficit
symptoms, and Hyperactivity symptoms.
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When the scale detects Attention Deficit - Hyperactivity Disorder symptoms,
the results also indicate whether the levels of these symptoms are minimal,
mild,
moderate, severe, or extreme.
The ADHD SAS is intended for use by parents to describe behaviors of
children and adolescents from age 4 yr up. The scale is designed to be used in
a
physician's office, or during a psychotherapist's clinical interview with
parents, or
during a parent-teacher-counselor conference, etc. There are numerous settings
in
which this screening instrument will be of value.
However, it can not be used when a parent is non-cooperative, hostile,
uncommunicative, prone to distortions, or so disorganized in his or her
thinking that
responses do not accurately reflect parental perceptions. Additionally,
persons with
low verbal ability due to lack of a fourth grade reading skills (the reading
difficulty of
the scale is approximately at a fourth-grade level). a bilingual background
with
limited English as a second language, a neuropsychological impairment, or
moderate
to severe mental retardation will nave difficulty completing the scale.
The ADHD SAS can be administered and scored easily by a trained
paraprofessional or technician. However, the ultimate responsibility for the
use and
interpretation of the ADHD SAS should be assumed by a professional with
advanced
clinical training and experience. Prior to administering the AD>'-iD SAS,
potential
users should become thoroughly familiar with the scale's theoretical
rationale, method
of construction, psychometric properties, and specific limitations, as
detailed in this
manual. In addition users should be prepared to make clinical judgments about
the
validity of the scale results in the setting in which it is used by
supplementing test data
with information concerning the youngster's medical status, behaviors at
school and
behaviors with peers.
To help ensure the appropriate use of the ADHD SAS, potential users also
should become familiar with and conform to the standards for the use of tests
prescribed by the American Psychological Association ( 1994) or review a basic
text in
the field of testing and assessment. Users who lack clinical training in the
assessment
and case management of attention deficit hyperactive disordered youth should
review
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the relevant literature in this area before using the scale. Several excellent
texts exist
on issues relating to attention deficit hyperactive disordered youth (e.g.,
Rourke,
1985; Rourke, 1989; Rourke, et al., 1983; Rourke et al., 1986).
Use of the ADHD SAS in both clinical and research settings should conform
to the professional and ethical guidelines presented by the American
Psychological
Association ( 1981 ). As with any assessment procedure focusing on children,
the
ADHD SAS should not be used without the informed consent of one of the
youngster's parents. In addition, users should take the necessary precautions
to
safeguard the confidentiality of the results and restrict their use to those
with a
IO professional "need to know." Communication of the scale results to
individual
youngsters or the youngster's parents should focus on the qualitative aspects
of the
attention deficit hyperactivity disorder, rather than focusing or reporting on
a specific
analysis of item responses. Whenever possible the person interpreting the
scale
results should enlist the aid of the parent in understanding and amplifying
the scale
results. taking into account the individual's awareness of attention deficit -
hyperactivity disorders and his or her current emotional state.
Identif cation of attention deficit - hyperactivity disorders is a complex
task
requiring clinical sensitivity and a thorough knowledge of the clinical and
research
literature on neuropsychology and learning disabilities. The ADHD SAS is
intended
solely as a screening instrument. It should not be used in isolation. Other
diagnostic
methods such as such as pediatric evaluations, pediatric neurological
evaluations,
electroencephalographic evaluations, computer assisted E.E.G. or brain
electrical
activity mapping, neuropsychological evaluations, speech and language
evaluations,
psychiatric evaluations should be used to supplement, corroborate. and
investigate the
test results.
The ADHD SAS also has a number of specific limitations which should be
kept in mind when interpreting the test results. First, the intent of the
scale is not
particularly disguised. Thus, the scores are subject to conscious and
unconscious
distortions by individuals completing the scale. Second, the scale assesses a
parent's
reported assessment of his or her child's behaviors at one point in time.
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The symptoms of Attention Deficit - Hyperactivity Disorder are not
manifested constantly and in all surroundings; therefore, the ADHD SAS may not
accurately predict temporal changes in the level of ADHD symptoms {Brown,
1986).
Administration of the ADHD SAS. All that is required to administer the
- ~ ADHD SAS is a pen or pencil and the rating form. The Rating Form is to be
filled
out by one or both of the youngster's parents. It asks for identifying
information,
- including selected sociodemographic factors. The 43 test items are contained
on the
front and back of this sheet along with instructions and spaces for responding
to each
Item.
After ensuring that the demographic information is completely entered at the
top of the ADHD SAS Rating Form, the examiner should give the following
directions: "Here are statements which will help me to better understand your
youngster's behavior. I want you to read each statement and indicate, by
marking an
"X" in the appropriate column, the amount of time each statement is true. For
I S example, consider the statement, "Is 'fidgety' with hands and feet." Does
this
statement apply to your youngster "None or a little of the time," "Some of the
time,"
"A good pan of the time," or "Most or all of the time?"
While presenting these directions aloud, the examiner points to the item and
each of the four answer columns. After the youngster's parent has responded,
the
examiner marks an "X" in the appropriate box and says: "Now finish the rest of
the
items. If you have questions, be sure to let me know."
Occasionally an individual taking the test will not understand a word or
concept. If a parent has difficulty understanding a particular item, the
examiner should
explain the item as neutrally as possible. For example, if the parent does not
understand the word "fidgety" in the item "Is 'fidgety' with hands and feet."
the
. examiner might say, "This item is asking how often you think your youngster
has
difficulty holding his hands and feet still."
The development and maintenance of rapport with the individual completing
the scale is very important. From a psychometric perspective, the
establishment of
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adequate rapport is essential to minimize the amount of intentional
distortions of
responses, especially denial of difficulties in a parent's child. Any comments
the
respondent makes may be helpful in evaluating the attitude with which the
scale was
filled out, and whether or not it is a valid representation of that person's
interpretation
of his or her child's actions.
Scoring the ADHD SAS. Only the Rating Form is required to score and
interpret the ADHD SAS. To score the ADHD SAS, first notice that each item on
the
ADHD SAS Rating Form has an item number along with a code indicating whether
the item measures Attention Deficit or Hyperactivity. For example HOl is the
first
item measuring Hyperactivity; D04 is the forth item measuring Attention
Deficit;
Also note that on approximately 20% of the items, the coded indication is
preceded by
an "*." The scoring pattern of the items is counterbalanced in order to
prevent the
respondent from getting into a scoring pattern. Al! items, except those marked
with
the "*" are scored in the following manner, responses marked: "None or a
Little of the
1 S Time" are scored " 1 ", "Some of the Time" are scored "2", "A Good Part of
the Time"
are scored "3", "Most or All of the Time" are scored "4". Those items marked
with
the "*" are scored in the opposite direction; that is, responses marked: "None
or a
Little of the Time" are scored "4", "Some of the Time" are scored "3", "A Good
Part
of the Time" are scored "2", "Most or All of the Time" are scored "1 ". Next
add all of
the items coded "D" to determine the total score for the Attention Deficit
Disorder
SubScale. Then enter that raw score in the blank after the statement "TOTAL
ATTENTION DEFICIT DISORDER SCALE RAW SCORE."
Add all of the items coded "H" to determine the total score for the
Hyperactivity Disorder SubScale. Then enter that raw score in the blank after
the
statement "TOTAL ATTENTION HYPERACTIVITY DISORDER SCALE RAW
SCORE." Under the profile: LEVEL OF ADHD SYMPTOMS, plot the raw scores
which you recorded. By plotting the raw scores there is an automatic
conversion of
the raw scores to converted scores allowing you to read the level of Attention
Deficit
Symptoms, Hyperactivity Symptoms, and Attention Deficit - Hyperactivity
Symptoms directly. In the COMMENTS AND RECOMMENDATIONS Section you
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might wish to record your impressions and directions for actions with this
particular
patient or family.
Rationale and Theoretical Background of the ADHD SAS. Some of the
attention deficit assessment scales which are have been published and are
available
have merely incorporated the diagnostic criteria of the most recent edition of
the
Diagnostic and Statistical Manaral of Mental Disorders published by the
American
Psychiatric Association. However, this approach is of limited applicability in
that it is
so time bound. As will be noted not only does the interpretation of the
primary
symptoms change significantly over time, there actually is question if the
condition
exists, if it exists does it exist as attention deficit alone, or is attention
deficit
sometimes associated with hyperactivity. As will be noted from the literature
review
above, the current state of understanding is in somewhat of a flux. The
inventors
believe in the validity of the construct and believe it is a common, widely
spread,
somewhat prevalent condition of youngsters. Here is a preferred method of ADHD
diagnosis. ,
Over the past 30 years there has been considerable discussion relative to the
various symptoms and the importance of various symptoms of Attention Deficit -
Hyperactivity Disorder. The individual who ties his diagnosis and
understanding of
Attention Def cit - Hyperactivity Disorder to the Diagnostic and Statistical
Manual of
Mental Disorders published by the American Psychiatric Association is somewhat
vulnerable due to the changeability of the concept. This is particularly true
for the test
constructor. According to (Newcorn et. al., 1989) assessment scales
constructed to
meet the diagnostic criteria of any of the Diagnostic and Statistical Manual
of Mental
Disorders editions or revisions would be of limited applicability. Regarding
the
various assessment devices which were based upon the diagnostic criteria of
the latest
Diagnostic and Statistical Manual of Mental Disorders, (Newcorn et. al.,
1989), made
the point that "structured diagnostic instruments, such as the 'Diagnostic
Interview
Schedule for Children (DIDC)' (Costello, 1983) and the 'Diagnostic Interview
for
Children and Adolescents (DICA)' (Herjanic and Campbell, 1977), were based on
DSM III criteria and cannot be considered adequate for subject selection in
the new
diagnostic system."
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Item Selection and Validity Considerations of the ADHD SAS. Items on
the ADHD SAS were derived from descriptive principles found in the
professional
literature during the past twenty years. After reviewing the professional
literature, all
supported and agreed upon principles and theoretical constructs which
described or
which explained the basis of attention deficit or hyperactivity were converted
to
behavior statements. For example, the distractibility of these youngsters is
universally
mentioned as a predominate factor; therefore there are items related to
distractibility,
such as: "Is quickly and easily distracted," "Finishes what is begun."
Standardization of the ADHD SAS. Traditional standardization of the scale
determining the correlation of the ADHD SAS with some other scale, or using
teachers', or psychologists', or physicians', or parents' judgments as a
comparative
criterion by which to measure the ADHD SAS was felt to be unnecessary. The
inventors did not find good criterion measures with which to measure the
validity of
the ADHD SAS. The method of scale construction builds in the validity of the
scale in
that the scale is measuring the operational definitions of the constructs as
put forth in
the professional literature over the past twenty plus years.
The ADHD SAS is designed to measure the constructs which have gone into
defining Attention Disorder. The scoring is designed to measure the
relationship
between amount of symptomatology being demonstrated by an individual and the
?0 total amount available to be demonstrated by the upper limits of the scale.
Individuals are evaluated according to the following scale. If an individual
has
less that 40 percent of the total capacity of Attention Deficit symptoms the
Scale
measures then the interpretation is "No Symptoms of Attention Deficit Disorder
are
Indicated." If he demonstrates between 40 to 51 % of the total capacity of
Attention
Deficit symptoms the Scale measures then the interpretation is "Minimal Level
of
Attention Deficit Symptoms axe Indicated." If he demonstrates between 52 to
64% of
the total capacity of Attention Deficit symptoms the Scale measures then the
interpretation is "Mild Level of Attention Deficit Symptoms are Indicated." if
he
demonstrates between 65 to 77% of the total capacity of Attention Deficit
symptoms
the Scale measures then the interpretation is "Moderate Level of Attention
Deficit
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Symptoms are Indicated." If he demonstrates between 78 to 90% of the total
capacity
of Attention Deficit symptoms the Scale measures then the interpretation is
"Severe
Level of Attention Deficit Symptoms are Indicated." If he demonstrates between
91
to 100% of the total capacity of Attention Deficit symptoms the Scale measures
then
the interpretation is "Extreme Level of Attention Deficit Symptoms are
Indicated."
Individuals are also evaluated according to the following scale. If an
- individual has less that 40 percent of the total capacity of Hyperactivity
Disorder
symptoms the Scale measures then the interpretation is "No Symptoms of
Hyperactivity Disorder are Indicated." If he demonstrates between 40 to 51 %
of the
total capacity of Hyperactivity Disorder symptoms the Scale measures then the
interpretation is "Minimal Symptoms of Hyperactivity Disorder are Indicated."
If he
demonstrates between 52 to 64% of the total capacity of Hyperactivity Disorder
symptoms the Scale measures then the interpretation is "Mild Symptoms of
Hyperactivity Disorder are Indicated." If he demonstrates between 65 to 77% of
the
total capacity of Hyperactivity Disorder symptoms the Scale measures then the
interpretation is "Moderate Symptoms of Hyperactivity Disorder are Indicated."
If he
demonstrates between 78 to 90% of the total capacity of Hyperactivity Disorder
symptoms the Scale measures then the interpretation is "Severe Symptoms of
Hyperactivity Disorder are Indicated." If he demonstrates between 91 to 100%
of the
total capacity of Hyperactivity Disorder symptoms the Scale measures then the
interpretation is "Extreme Symptoms of Hyperactivity Disorder are Indicated."
Individuals are finally evaluated according to the following scale. If an
individual has less that 40 percent of the total capacity of Attention Deficit
-
Hyperactivity Disorder symptoms the Scale measures then the interpretation is
"No
Symptoms of Attention Deficit - Hyperactivity Disorder Symptoms are
Indicated." If
he demonstrates between 40 to 51 % of the total capacity of Attention Deficit -
' Hyperactivity Disorder symptoms the Scale measures then the interpretation
is
"Minimal Symptoms of Hyperactivity Disorder are Indicated." If he demonstrates
- between 52 to 64% of the total capacity of Attention Deficit - Hyperactivity
Disorder
symptoms the Scale measures then the interpretation is "Mild Symptoms of
Hyperactivity Disorder are Indicated." If he demonstrates between 65 to 77% of
the
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total capacity of Attention Deficit - Hyperactivity Disorder symptoms the
Scale
measures then the interpretation is "Moderate Symptoms of Hyperactivity
Disorder
are Indicated." If he demonstrates between 78 to 90%. of the total capacity of
Attention Deficit - Hyperactivity Disorder symptoms the Scale measures then
the
interpretation is "Severe Symptoms of Hyperactivity Disorder are Indicated."
If he
demonstrates between 91 to 100% of the total capacity of Attention Deficit -
Hyperactivity Disorder symptoms the Scale measures then the interpretation is
"Extreme Symptoms of Hyperactivity Disorder are Indicated."
INTERPRETATION AND CLINICAL USE OF THE
ATTENTION DEFICIT - HYPERACTIVITY DISORDER
SYMPTOMS ASSESSMENT SCALE
Interpretation of the ADHD SAS Results
Case Studies
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CA 02288990 1999-10-27
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RDS ASSESSMENT SCALE
I. ATTENTION DEFICIT DISORDER
PREDOMINATELY INATTENTIVE TYPE
TOTAL SCORE IOF HIGHEST SIX)
REQUIRED SCORE FOR DIAGNOSIS (6 X 2.51 15
LEVEL OF RDS: 15 - 20 MODERATE; 21 - 24 SEVERE
Predominately Hyperactive Type
TOTALSCORE
REQUIRED SCORE FOR DIAGNOSIS (6 X 2.5) 15
LEVEL OF RDS: 15 - 20 MODERATE; 21 - 24 SEVERE
PREDOMINATELY IMPULSIVE TYPE
TOTALSCORE
REQUIRED SCORE fOR DIAGNOSIS (3 X 2.5) 7
LEVEL OF RDS : 7 - 9 MODERATE; 10 - 12 SEVERE
1 I. TOURETTE'S DISORDER
TOTALSCORE
REQUIRED SCORE FOR DIAGNOSIS (3 X 2.51 7
LEVEL OF RDS : 7 - 9 MODERATE; 10 - 12 SEVERE
I I I. CONDUCT DISORDER
TOTALSCORE
REQUIRED SCORE FOR DIAGNOSIS (3 X 2.5) g
LEVEL OF RDS : 9 - 10 MODERATE; 17 - 12 SEVERE
I V. OPPOSITIONAI DEFIANT DISORDER
TOTALSCORE
I
REQUIRED SCORE FOR DIAGNOSIS 14 X 2.5) 10 ~i
LEVEL OF RDS : 10 -13 MODERATE; 14-16 SEVERE
V. INTERMITTENTENT EXPLOSIVE DISORDER
-484-
CA 02288990 1999-10-27
WO 98/48785 PCT/US98/08684
TOTAL SCORE "~''
REQUIRED SCORE FOR DIAGNOSIS (3 X 2.5) 7
LEVEL OF RDS : 7 - 9 MODERATE; 10 -12 SEVERE
V I. SCH1101DIAUOIDANT PERSONALITY DISORDER
TOTAL SCORE (OF HIGHEST FOUR IN EACH SECTION)
REQUIRED SCORE FOR DIAGNOSIS 18 X 2.5) 20
LEVEL OF RDS: (Schizoid + Avoidant) 20-26 MODERATE;
27-32 SEVERE
V 1 I. SUBSTANCE USE DISORDER
SUBSTANCE DEPENDENCE
TOTAL SCORE (OF HIGHEST THREE)
REQUIRED SCORE FOR DIAGNOSIS (3 X 2.5) 7
LEVEL OF ROS : 7 - 9 MODERATE; 10 - 12 SEVERE
TYPES OF SUBSTANCE DEPENDENCE CHECKED: ALCOHOL
G AMPHETAMINE RELATED G
CARBOHYDRATES G CRACKICOCAINE G HEROIN G MARIJUANA
G NICOTINE G
SUBSTANCE ABUSE
TOTAL SCORE (ONLY HIGHEST OF FOUR)
REQUIRED SCORE FOR DIAGNOSIS (1 X 3) 3
LEVEL OF RDS: 3 MODERATE; 4 SEVERE
TYPES OF SUBSTANCE ABUSE CHECKED: ALCOHOL G
AMPHETAMINE RELATED G
CAR80HYDRATES G CRACKICOCAINE G HEROIN G MARIJUANA
G NICOTINE G
V 1 I I. PATHOLOGICAL GAMBLING
TOTAL SCORE (OF HIGHEST FIVE)
REQUIRED SCORE FOR DIAGNOSIS (5 X 2.5) 12
LEVEL OF RDS: 12-16 MODERATE; 17-20 SEVERE
I X. POSTTRAUMATIC STRESS DISORDER
TOTAL SCORE (HIGHEST IN 1 ST SECTION, 3 IN THE
2ND, AND 2 IN THE
REQUIRED SCORE FOR DIAGNOSIS (1X3) + (3X2.5) 13
+ (2x2.5)
LEVEL OF RDS: 13-18 MODERATE; 19-24 SEVERE
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o N - o - o cv a o .-
a a~ :. ~ w- as . o
,.'.' O c C i=-- O O o Q~
c_w = O7 ~ .a vi
E ~ o N ~ a
o .a ~. o ; E c c E E~c
ao c o eo C/~ a, o~ N ~ ~ o
cv 'o N N
H ~' I~ ~ ~ c -- f.'~
fC c~ N N
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CA 02288990 1999-10-27
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a~
a~ a
a a
i a,
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o '
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a
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as
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o >. a > a ~u >. a ~ ~ >. ~ > >
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EXAMPLE 24
ASSOCIATION OF A TRINUCLEOTIDE (GGC) REPEAT
POLYMORPHISM OF THE ANDROGEN RECEPTOR GENE (AR) WITH
ADHD, CONDUCT AND OPPOSITIONAL DEFIANT DISORDER IN
~ 5 TOURETTE SYNDROME
Many of the behavioral and cognitive disorders including ADHD, CD, ODD,
. antisocial personality disorder, dyslexia and other teaming disorders, and
autism, are
three to five times more common in males than in females {DSM-IV, 1994). While
generally assumed to be due to hormonal and environmental factors, genetic
factors
could be involved. The role of specific genes would be easiest to understand
if the
gene was X-linked. Thus, depending upon the frequency of the relevant alleles
and
the percent of the variance attributed to the gene, and assuming a
predominantly
recessive mode of inheritance, an X-linked gene could account for a portion of
the
excess in males. If the gene also played an important role in the response to
testosterone, it would be unusually well suited to playing a role in the
maie/female
ratios. The androgen receptor gene {AR) is such a gene. It is located on the X-
chromosome at Xql l-12 (Migeon et al., 1981; Brown et al., 1989).
The inventors hypothesize that the shorter of the normal alleles of the AR CAG
and GGC repeats, associated with increased levels of androgen receptor and
sensitivity to testosterone, might be associated with one or more of the
disruptive
behavioral disorders of childhood, especially conduct disorder and oppostional
defiant
disorder, and might explain a portion of the male predominance of these
disorders. To
test this the inventors have examined the GGC (Sieddens et al., 1.992,1993),
trinucleotide repeat polymorphism in exon 1 of the AR gene in a series of 326
individuals consisting of 267 with Tourette syndrome and 59 controls. Because
of the
frequent association of dopamine (Valzelli, 1981 ) and serotonin (Brown et
al., 1982;
Lindberg et al., 1984; van Praag, 1991; Coccaro, 1989) with aggressive
behaviors, the
effect of the AR gene on conduct disorder and ODD was compared to that of the
dopamine D2 receptor gene (DRD2) and the serotonin transporter gene (HTT~.
This
study was conducted to determine if -the shorter alleles of the GGC
trinucleotide
repeat polymorphism of the human androgen receptor gene (AR) are associated
with
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the symptoms of conduct disorder (CD), oppositional defiant disorder (ODD} or
attention deficit hyperactivity disorder (ADHD).
Methods. The frequency of the alleles was determined in 326 subjects
consisting of 267 individuals with Tourette syndrome and 59 controls, 237
males and
89 females, all non-Hispanic Caucasians. Eight quantitative behavioral
measures
were examined simultaneously by MANOVA for correlation with the presence of
hemizygosity for the shorter alleles in males or homozygosity for the shorter
alleles in
females. Linear regression analysis was used to determine the percent of the
variance
that was due the AR gene. This was compared to the percent of the variance due
to the
dopamine D2 receptor gene (DRD2), and the serotonin transporter gene (HTT),
individually and in combination.
The subjects tested consisted of 59 controls and 267 individuals with Tourette
syndrome. The details concerning the subjects and controls, and the behavioral
assessments, are presented herein. In those studies the inventors examined the
following quantitative traits: inattention, impulsivity, hyperactivity, CD,
ODD,
learning disorders (LD), and grade school academic performance (GSAP). In the
present study and one of the companion studies the inventors have added a
quantitative tic score (Comings et al., 1996).
The GGC repeat polymorphism of the AR gene was amplified by polymerase
chain reaction by the technique of Sleddens et al., 1992, 1993. Irvine et al.,
1995
found the 16 repeat was the most common allele, .57, followed by the 17 repeat
(.32}
and the 15 repeat (.08}. The only other allele was the 10 repeat. For analysis
of the
possible correlation between the AR alleles and individual quantitative
behavioral
scores, the AR gene was scored as follows: for males > 16 repeats = 0, >_16
repeats =
1; for females: > 16/> 16 repeats = 0; heterozygotes = 0; >_ 16/? 16 repeats =
1.
The TaqI A polymorphism (Grandy et al., 1989) of the dopamine DZ gene
(DRD2) was used to evaluate the role of this locus compared to the AR gene, in
conduct disorder. Based on evidence of positive heterosis at this polymorphism
(Comings and MacMurray, 1998), it was scored as 11, 22 = 0, 12 = 1.
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The insertion/deletion polymorphism in the promoter region of the serotonin
transporter gene was used (Hells et al., 1995, 1996). The HTT gene was scored
as SS
= 0 and SL, LL = 1 (Kauck et al.. 1997; Lesch et al.. 1996).
Possible differences in the frequency of the AR alleles, or groups of alleles,
in
S TS subjects versus controls, was tested by chi square analysis. To avoid the
need for a
Bonferroni correction, the eight behavioral scores were simultaneously
examined
using MANOVA. To examine the additive effect of the three genes, a
AR+DRD2+HTT score was formed. Here the individual gene scores were added for
each individual. This gave a range from 0 (no relevant genotypes for any of
the three
genes), to 3 (relevant genotypes for all three genes). Linear regression
analysis was
utilized determine the percent of the variance of the CD, ODD and ADHD scores
that
were accounted for by the AR, DRD2, and HTT genes, singly or in combination.
To
test the hypothesis that these three genes were additive in their effect, the
means for
each score, including a total ADHD score, were examined by linear ANOVA.
1 S Results. AR allele frequencies were determined. Among the S9 controls the
frequencies of the different repeat alleles were as follows: 10 - .01, 12 -
.09, 1 S - .03,
I6 - .47, I7 - .36, I8 -.01, 19 -.01, 20 - .01. The frequency of the alleles
in the
controls compared to the frequencies in the TS subjects was determined. The
chi
square between the controls and the TS subjects, including all the alleles =
19.69, d.f.
= 1 1, p = .073 1. In the inventors' studies of the potential relationship
between the
length of repeat alleles and a phenotypic effect, the inventors have found
that the most
parsimonious approach is to divide the alleles into to a short and a long
group, and to
make the two groups as equal in size as possible. Following this approach the
inventors divided the AR alleles into those > 16 and > 16 repeats. There was a
non-
2S significant tendency for the TS subjects to have an increase in the
frequency of the
?16 alleles (.66) compared to the controls (.60), a2 = 1.06, d.f. = I, p = 30.
Thus,
when the comparison was made between the controls vs. the TS subjects, there
were
no differences at a = .OS.
The results of MANOVA, sorted in decreasing order of significance, are
shown in Table I. The CD score was the most significant (p = .OOS), the ODD
score
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next (p = .022), with the hyperactivity score also being significant (p =
.032). The tic,
GSAP and LD scores were the least significant (p > .6). To examine the
question of
whether this preferential association with CD was only present in males, the
inventors
compared the results in males (n = 237) and females (n = 89). Because of the
loss of
power due to the smaller numbers, none of the waits were significant for
either sex.
However, in when sorted by p value, CD was the most significant for both
sexes.
Because of the potential association between AR variants and sexual behavior,
the inventors performed a post hoc analysis of the possible association of the
AR gene
with a sexual behavior score (Comings, 1994) in a subset of subjects 14 years
of age
or greater. This was not significant. A post hoc analysis was also performed
in
females to determine if the effect of the shorter alleles was recessive or
dominant. For
all the scores the means for heterozygotes were consistently similar to or
less than for
the >16/>16 homozygotes, indicating the effect of the >_16 repeat alleles were
recessive in their effect with only the >_ 16/>_ 16 homozygotes in females
showing the
I S effect.
The regression analysis results are shown in Table 74. For the CD score, the
AR gene accounted for 2.4% of the variance (p = .005). By comparison, the DRD2
gene accounted for 1.3% (p = .041 ) and the HTT gene for 0.5%. The AR and the
DRD2 gene combined accounted for 3.3% of the variance (p = .0009). This
increased
to 3.5% when the HTT gene was added (p = .0007). The AR gene accounted for I
.6%
of the variance of the ODD score (p = .022). Here the AR and DRD2 genes were
more
comparable with the DRD2 gene accounting for 1.4% of the variance, and the HTT
gene for 0.7%. All three genes accounted for 3.2% of the variance of the ODD
score
(p = .001 ). The AR gene accounted for 1. i % of the variance of the ADHD
score
(p = .053). All three genes accounted for 2.7% of the variance (p = .0027).
The Linear ANOVA results of an additive AR+DRD2+HTT score was
examined by linear ANOVA to determine if there was a progressive increase in
the
scores in subjects with increasing number of relevant genotypes. The
inattention,
impuisivity, hyperactivity, ADHD, ODD and CD scores were all significant at a
=
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.OS. At a Bonferroni corrected a of .05/6 or .0083, the hyperactivity,
impulsivity,
ADHD, ODD and CD scores were still significant.
By MANOVA there was a significant association between the presence of the
short GGC alleles of the AR gene and symptoms of CD (p = .005), ODD (p =
.022),
and hyperactivity (p = .023). The association of these alleles was greatest
for CD for
both males and females. The AR gene accounted for 2.4% of the variance of the
CD
score. The combination of the AR, DRD2 and HTT genes accounted for 3.5% of the
variance of the conduct disorder score (p = .0007), 3.2% of the variance of
the ODD
score (p = .001 ) and 2.7% of the variance of the ADHD score (p = .0027).
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Table 73
MANOVA for the Behavioral
Scores
(n = 326)
A. All Eight Behavioral
Scores
Score F-ratio p
CD 8.03 .005
ODD 5.32 .022
Hyperactivity 5.20 0.23
Impulsivity 3.18 .075
Inattention 1.76 .181
Tics 0.22 .634
GSAP 0.22 .643
LD 0.95 .923
Total (Wilkes) 1.48 .171
S
B. Males only (n = 237)
Score F-ratio p
CD 2.57 .110
Hyperactivity 1.50 .221
Tics 1.18 .277
ODD 1.13 .288
GSAP 0.38 .534
Learn 0.34 .559
Impulsivity 0.15 .b95
Inattention 0.11 .736
Total (Wilkes} 1.14 .334
C. Females only (n = 89)
Score F-ratio p
CD 1.63 .205
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ODD 0.61
.433
Inattention 0.54 .463
Impulsivity 0.48 . .489
GSAP 0.41 .523
Hyperactivity 0.15 .703
Tics 0.13 .712
Learn 0.03 .853
Total (Wilkes) 0.38 .929
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o, .-, c~ ~- -" o
°.°.~o°o°o °.°.~°0°00
M ~1 ~?' V1 01 M ~~ l~ 1~ ~O ~O N
00 O M M 00 ~ M ~~ V1 O ~ M
N N ~--~ M N M N N ~~ M N M
'fit M ~ M Y1 V1 \J '~ t~ 00 N
N O M N M O N N M
O O O O O O O O O O O O
N
U
y, M
w il
ca
V'1M M N 00 l~ C~ O1 ~O I~
V1.-~ I~ 00 N 00 N o0 ~O d'
00
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H E~
(~
x x
+ +
N N N N
(~
~ x ~ i~ x
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(~ E--,~- + + (~ i-.",+ -i-
-1-
~ (~ ~ t~ l~ ~ ~ E~
ra x ~ Q Q d c~ x a
0
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CA 02288990 1999-10-27
WO 98/48785 . PCT/US98/08684
I I°.~°.°.°o°o
c'Y'~,~o~,.w~o
N N M
00 N 00
o °0 0 0 0 0
vo a. ~ ~ ~n
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CA 02288990 1999-10-27
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EXAMPLE 25
THE EFFECTS OF CHROMIUM PICOLINATE SUPPLEMENTATION ON
BODY COMPOSITION: A RANDOMIZED DOUBLE-MASKED PLACEBO
CONTROLLED STUDY
Introduction: The inventors also sought to answer several of methodological
issues. First, does supplementation with CrP have affects on caloric intake
through its
impact on appetite and increase caloric expenditure through increased
metabolic or
daily activity levels? Second, would the same results be achieved if the
inventors
controlled for, and factored out, differences in caloric intake andlor energy
expenditure between the experimental and control groups? Third, would the
results
be replicated with other measures of body composition, such as Dual Energy X-
ray
Absorptiometry (DEXA), that are even more precise and less dependent upon the
subject's performance in taking the test than underwater testing? Fourth, did
the
relatively high dropout rates seen in other studies {Anderson, 1995) (29.7%)
bias the
findings though selective attrition in spite of the similarity of the three
study groups
on all baseline measures of body composition? Would these same results occur
if
methods were employed to decrease file dropout rates or if one used "intention
to
treat" statistical analyses?
To answer this questions the inventors employed measures to control for
differences in physical activity and caloric intake, used DEXA testing for
body
composition and used a method to obtain near-perfect compliance with
completion of
the ending test.
Materials and Methods. A total of 130 patients were enrolled in the study
and 122 (93.8%),I7 males and 105 females with an average age of 42.3,
completed alI
ending measurements. Patients were recruited from a variety of fitness and
athletic
clubs in San Antonio and Houston, Texas by fitness instructors and sales
personnel
who provided information about the study to club members who either
participated or
recruited friends or relatives to participate. In most cases, fitness
instructors were paid
to monitor the subjects as they progressed through the study insuring they
reported
their physical activity levels and caloric intakes in weekly tracking data and
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completed the ending testing. All subjects were asked to consult with their
personal
physician before giving informed consent.
A number of studies have also shown that DEXA can accurately measure fat
and lean content in meat samples and animal carcasses (Evans, 1989; Evans and
. 5 Meyer. 1992; Evans, 1993; McCarty, 1993) and correlates highly with actual
skeletal
mass and with total body calcium by neutron activation analysis with a typical
_ precision error for total body bone mineral content of less than 1% (Felig,
1975). It
has also been shown to be a precise method for assessing body composition in
both
obese and non-obese patients (Page et al., 1993; Eckel, 1992). The initial
studies on
the precision of the DEXA were reported in 1990 (Mooney and Cromwell, 1993)
and
confirmed in three subsequent studies (Page et al., 1993; Hasten et al., 1994;
Evans,
1989) suggesting that while DEXA correlates highly with underwater weighing,
deuterium dilution and total body potassium (Page et al., 1992), errors in
DEXA
measurements were less than half of those obtained using total body water or
underwater testing. Specifically, the coefficients of variations (CV%) for
Lunar
Corporation's DEXA have been reported as follows: Fat mass = 500 g + -2.Sg;
FFM = 6008 + -1.3g; and Total tissue mass = 4008 + -0.6g. In addition to being
used
to evaluate a variety of clinical disorders (Lindemann et al., 1993), DEXA's
reliability
make it possible to monitor the effects of relatively short-term dietary
restrictions
and/or exercise on regional and total body composition (Page et al., 1993;
Eckel,
1992). A recent review of research on DEXA has led one reviewer to conclude
that
DEXA is among the most critically analyzed body composition instruments
available
today (Lindemann et al., 1993).
DEXA provides a three-compartment model of body composition: fat, lean
tissue mass, and bone mineral content. Measurements are made using a constant
potential energy source at 78 kVp and a K-edge filter (cerium} to achieve a
congruent
beam of stable, dual-energy beam with effective energies of 40 and 70 keV. The
unit
performs a series of transverse scans moving from head to toe at I cm
intervals; the
scan area is approximately 60 em x 200 em. Data are collected for about 120
pixel
elements per transverse, with each pixel approximately 5 x 10 min. Total body
measurements are completed in 10-20 min with a scan speed of 16 em/sec or 20
min
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with a scan speed of 8 em/sec. The R-value (ratio of low to high-energy
attenuation
in soft tissue) ranges from 1.2 m 1.4 (Lindemann et al., 1993}.
In addition to comparing changes in scale weight, % body fat, fat mass and
FFM, the inventors also used an index of a body composition improvement (BCI)
as
described in the inventors' previous study (Glinsmann and Mertz, 1966). The
BCI is
based on the assumption that losses of body fat and gains in FFM are positive
treatment outcomes, while gains in fat mass and losses in FFM are negative
treatment
outcomes. Therefore, losses of fat mass and/or gains in FFM were scored as
positive,
gains in fat mass and/or losses in FFM negative, and the BCI was the net
result of
combining these scores. The superiority of the BCI over scale weight as a
measure of
change has been demonstrated in study examining changes in body composition
during participation in supervised exorcise programs (Liarn et crl.. 1993) as
well as
with patients undergoing pharmacotherapy with and without a behavior
modification
program (Hasten et al., 1994). Comparisons were made between the two groups in
the placebo group using two-tailed Student t-tests with the assumption of
equal
variance and, in the experimental group, by using analyses of co-variance to
equate
the groups on caloric intake and expenditure.
As a method of reducing dropout rates, in conjunction with signing the
informed consent form, patients were asked to provide a check for $100 which
was
not processed unless the patient failed to complete the ending DEXA test and
end-of
study questionnaire. Patients were advised that return of their deposit check
was
conditional solely upon completing the ending tests no matter how well or
poorly that
adhered to the research protocol as long as they reported candidly how much or
how
Little they complied. After completing an initial DEXA test, patients were
provided
with a report of their test results and were randomly assigned a number from 1
to 130
which corresponded to a bottle containing capsules with 400 meg of chromium
pieolinate or an inactive placebo that was identical in appearance. None of
the
investigators, research technicians dispensing the product, or patients knew
which
patient number corresponded to the placebo or active product. An independent
local
pharmacist acted as trustee for the study and randomly assigned subject
numbers to
bottles that had been pre-labeled either an "X" or "Y" to correspond with
either the
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active or placebo product. Upon completion of the study and when all data were
gathered and computerized, the trustee opened an envelope supplied by the
manufacturer indicating which product was active and subsequently notified the
senior investigator (GRK). All information was analyzed by the Department of
Computing Resource at the University of Texas Health Science Center under the
supervision of the second author (KB). At the conclusion of the test period,
patients
completed ending body composition test, were provided with their test results
and
deposit checks, and were asked to report how many of the capsules actually
consumed
each day as a cross-check of the amount of product used. A subsequent analysis
of
these data revealed the average active subject consumed 357 meg of CrP a day.
Patients were provided with a workbook outlining general procedures for
estimating caloric intake, nutritional information for common foods and a log
for
calculation and recording of daily calorie balances. To monitor and adjust for
differences in energy expenditure through physical activity, throughout their
waking
I S h, all patients wore a pedometer used in previous studies (Evans and
Press, 1989;
Kitchalong et al., 1993) that reflected the number of steps they took during
each day
or the step-equivalents for activities in which it was impractical to wear the
unit.
Patients recorded the total number of steps taken each day in the same daily
log used
to record their caloric intake which was subsequently used to adjust the
patients net
change in body fat using the formula of + or -3.500 calories for a change of
one pound
of body fat.
Results Table 75 provides the baseline descriptive statistics for the 122
patients who completed the study. A comparison of those who did not complete
the
study with those who did revealed that there no significant differences on any
of the
body composition parameters.
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TABLE 75
A COMPARISON OF BASELINE DEMOGRAPHIC DATA FOR I22
PATIENTS WHO WERE RANDOMLY SELECTED INTO GROUPS
RECEIVING EITHER A PLACEBO
OR 400 MCG OF CHROMIUM PICOLINATE PER DAY
Body Mass
Age (y)* Weight (kg)* % Body Fat* Index (kg/m2)*
Active (n=62) 41.1 + -10.585.5 + 23.042.4% + 8.3% 30.2 +
7.1
Placebo (n=60)43.5 = -7.679.9 = -20.441.8% _ -6.7%28.4 +
5.4
P level (two-tailed)p = 0.24 p = 0.16 p = 0.65 p = 0.13
*Mean + -SD
Table 76 provides a comparison of the changes that occurred during the 90-
day test period.
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Table 77
Comparisons of average changes in body composition parameters between
participants receiving a placebo or 400 mcg of chromium picolinate during a 90
day test period. All data are controlled for differences in caloric intake and
expenditure.
Weight Fat Free
(kg)* % Body Fat Fat Mass* Mass* BCI*
Active -7.g + -g,7 -6.3% = 8.5% -7.7 + -9.5 -0. I + -2.2 +7.6 + -4.5
(N=62)
Placebo -1.9 + -4.0 -1.2% + -5.7% -3.4 + -6.8 -0.3 + -2.0 +3.1 + -7.6
(n=60)
P level** p = < .001 p = < .001 p = .004 p = .568 p = .004
* Mean + -SD
* * two-tailed Student's t-test
Discussion A review of the baseline data in Table 76 reveals that there were
no statistically significant differences between the two groups on any of the
body
composition parameters suggesting the randomization process was successful in
providing two equivalent groups of patients. Data in Table 77 reveal that
making the
groups equivalent with corrections for caloric intake and energy expenditure,
supplementation with CrP had a highly significant effect on scale weight, %
body fat
and BCI. It is also worth noting that even without correcting for caloric
intake and
expenditure, changes in the active group were consistent with the changes
observed in
the inventors' previous study including a significant decrease in body fat (p
= .02).
It is worth noting that the major improvement in body composition in this
study was the reduction of body fat. DEXA testing is one of the few
technologies for
measuring body composition that provides a direct physical measurement of
adipose
tissue. Hydrostatic testing, as well a many other measures of body
composition, all
_ rely upon estimating the patient's body fat on the assumption that their
body density
reflects the same percentage of fat as found in a few cadaver studies.
Furthermore,
even hydrostatic testing does not actually measure the patient's body volume
for
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calculation of body density--it estimates it from scale weights obtained in
and out of
water. Thus, even with hydrostatic weighing the patient's body fat is derived
from
two different estimates, not from a physical measurement of adipose tissue.
And, of
course, estimates derived from hydrostatic testing can be affected by the
ability of the
subject to consistently exhale his/her air while underwater as well as
variations in lung
volumes over time even when exhalation is consistent. DEXA testing resolves
these
difficulties since obtaining the measurement requires only that the still on
an open
testing table for 15-20 min while the body is scanned. It seems to the
inventors that
when attempting to measure the efficacy of products that produce relatively
small
changes in body composition. controlling the variability of the testing
technology is
imperative.
The requirement for patients to provide a conditionally refundable deposit
appears to have made a dramatic difference in the number of subjects who
completed
the final testing negating the need to use statistical controls, such a
"intention to treat."
Post study critiques revealed that subjects viewed the requirement to provide
a
deposit, which was not processed, as a reasonable request. Of the 130 subjects
who
were recruited for this study, only 8 failed to complete the final test. One
subject
became pregnant and was asked to withdraw from the study, three others moved
from
the local area, one was ill during the post-testing and three could not be
accounted for.
Thus, for all intent and purposes, there were no dropouts from the study that
could
bias the results. Although the inventors' data are not definitive, the deposit
requirement appears to be something worthy of further study.
Since the requirement for patients to provide a conditionally refundable
deposit was based entirely on the subject completing the study and an end-of
study
questionnaire and had nothing to do with how little or how much the patient
complied
with the protocol. An equal number of patients failing to take the product in
the
placebo and active groups does not, of come, balance the effects across the
groups.
Placebo patients who fail to take the product will have no effect on the
outcome
measures since the placebo does not contain the active ingredient. However,
failure to
take the product in the active group will attenuate the effects of the active
product
could be having. In fact, a completely noncompliant active patient is actually
a
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placebo patient. Thus, lack of compliance will by its very nature attenuate
differences
between the two groups stressing the need to obtain accurate data on how much
of the
product the subject did consume. The use of weekly check-ins and personal
monitoring appears to have provided the inventors with more comprehensive data
and
reduced the mount of bias the lack of compliance could have on the outcome
measures.
Conclusion. These data indicate that supplementation with chromium
picolinate can lead to significant improvements in body composition when a
Body
Composition Index is used as the outcome criterion that represents a sum of
the net
gains in non-fat mass added to the sum of the net losses of body fat.
EXAMPLE 26
POLYGENIC INHERITANCE AND MICROIMINISATELLITES
Micro/minisatellite polymorphisms in psychiatric genetics Minisateliites
have been defined as repeat sequences of up to 65 base pairs in length
(Wright, 1994).
I5 Microsatellites consist of shorter repeats variously defined as 2-5 by in
length. For
the purposes of this example, unless specifically stated, the inventors will
use the term
micro/minisateliites to cover both.
Because of the high frequency of micro/minisatellite repeats throughout the
genome, the inventors and others have often used these polymorphisms in
association
studies. The inventors' initial assumption was that like the neutral or silent
single base
pair polymorphisms, the micro/minisatellite alleles would be in linkage
disequilibrium
with other 'critical' mutations that affect gene function. However, after
working with
these polymorphisms for several years the inventors began to suspect that the
micro/minisatellites themselves might be the 'critical' mutations. While the
inventor
has not included the extremely long triplet repeats polygenic inheritance,
these studies
do introduce the concept that the varying length of repeat alleles, even when
they are
well into 'normal' range, may affect gene function through a wide variety of
mechanisms. There are two aspects of these micro/minisatellite poiymorphisms
that
are relevant - their mutation rate and their size.
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The mutation rate of micro/minisatellite alleles is higher than for non-repeat
sequences (3effreys et al., 1987). Lack of exchange of flanking markers
suggests the
new mutations are due to replication slippage or complex conversion-like
events
(Wolff et al., 1989). A high mutation rate could make these polymorphisms less
valuable for association studies since over many generations there would be
too much
noise introduced into linkage disequilibrium relationships which require that
two
different polymorphisms remain in phase far many generations. However, if the
micro/minisatellite mutations were themselves the 'critical' alleles, they
would
actually be more powerful for association studies, despite the higher mutation
rate.
If linkage disequilibrium was involved in the association of
microlminisatellite
alleles with specific phenotypes, each time a new mutation in the satellite
occurred
there would be a specified chance it was occurring on the same chromosome as
the
'critical' alleles. However, on average there should be no trend for the
longer vs the
shorter mutant alleles to preferentially be in linkage disequilibrium with
these 'critical'
alleles. However, if the size of the repeats played a role in gene regulation
(see
below) there should be a trend for an association of quantitative traits with
groups of
different sized alleles rather than with specific individual alleles or allele
sequences.
If there is a major peak of repeat gene frequency, both the shorter and the
longer
alleles may be associated with phenotypic effects.
There is the possibility that both length and sequence can be involved, and
that
the different sized micro/minisatellite alleles might have an effect on gene
function.
This hypothesis would be much more plausible if different micro/minisatellite
alleles
could be shown to have an effect on the rate of transcription or translation
of the
genes. There are, in fact, many examples of this.
A general hypothesis of polygenic inheritance. The various aspects of a
micro/minisatellite hypothesis of polygenic inheritance are as follows: many
micro/minisatellites have an effect on the expression of the genes with which
they are
associated; many genes are associated with one or more micro/minisatellite
polymorphisms; as a result, many genes will present with a range of functional
alleleomorphic variants. Those genes associated with several
micro/minisatellite
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polymorphisms, each with multiple alleles, will present with an especially
large
number of functional haplotypes. While most genes will be associated with t10-
15%
of the average IeveI of gene activity, the range may be as high as ~40%, and
if
multiple micro/minisatellite polymorphisms are involved their effects can be
additive.
Additionally, significant functional variants are common in the general
population, with prevalences ranging from 1 to 100%. A 100% prevalence can
occur
if there is a bimodal distribution of alleles consisting entirely of
hyperfunctional
alleles (>10% of the average) and hypofunctional variants (<10% of the
average).
Individually these functional variants have only a modest effect on the
phenotype
(0.5-8% of the variance), and rarely cause disease, i.e. they are generally
not
responsible for the classic one gene-one disease autosomal dominant or
recessive
disorders. Since most of the micro/minisatellite poiymorphisms are not in
exons, the
mutations in poiygenic disorders are usually outside the exons and often
outside the
transcribed sequences. Log score linkage studies lose power when more than six
I ~ genes are involved in a given disorder (Risch and Merikangas, 1996; Weeks
and
Lathrop, 1995). Linkage studies are based on the assumption that the presence
of a
given allele is associated with the presence of the disorder and the absence
of the
allele is associated with the absence of the disorder. By contrast, in
poiygenic
inheritance the disorder is associated with the presence of a threshold number
of
alleles of several different genes. As such, many members of a pedigree may
carry a
given mutant gene but not have the disorder, because of the absence of the
necessary
threshold number of other mutant genes. Thus, many members of a family may
carry
a specific gene and not have the disorder, and other members of the family
with the
disorder may not carry the mutant allele (Comings, 1996f, l,m).
Both hyper- and hypo-functional alleles can have an effect on the phenotype.
For example, in psychiatric genetics, both an increase in the expression of a
given
receptor gene leading to receptor supersensitivity, or a decrease in the
expression of a
receptor gene leading to receptor hyposensitivity, could be associated with an
altered
phenotype. A polygenic disorder occurs when an individual inherits a threshold
number of hypo- or hyper-functional genes affecting the same phenotype. Thus,
if 20
different polygenes play a role in a given quantitative variable, anyone with
10 or
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more of these hypo- or hyperfunctional variants would present with a
significantly
altered phenotype. The threshold number would vary according to the degree of
hypo- or hyper-functionality of the alleles, and the severity of the altered
phenotype
would depend on the extent to which the number of polygenes exceeded a
critical
threshold number.
Because micro/minisatellites are so common, the probability of a polygenic
disorder occurring is relatively high. Thus, polygenic disorders are much more
common than single gene disorders. With the general population frequency of
many
polygenes ranging from 25% to over SO% the chance of inheriting a threshold
number
is much higher than for the single gene disorders. For a given
micro/minisatellite the
alleles can have a negative or positive effect on the phenotype depending upon
the
genetic background of other genes and on the nature of the phenotype. A
practical
example is the observation by Gelernter et al. (1994) of an association
between
cocaine-induced paranoia and the 9 allele of the dopamine transporter gene (DA
TI)
gene. By contrast, Cook et al. (1995) and Comings et al. (1996j) observed an
association between the 10 allele of the DA TI gene and attention deficit
hyperactivity
disorder. One might conclude from such divergent reports that one or the other
observation must be incorrect. However, there is a reasonable probability that
both
are correct, and that different alleles of a given micro/minisatellite may be
associated
with different phenotypes. A reasonable approach to the study of polygenic
disorders
is to choose candidate genes and examine the effect of alleles of the nearest
micro/minisatellite polymorphisms on relevant quantitative traits.
The above proposed characteristics of polygenic inheritance carry a number of
additional implications. If many micro/minisatellite polymorphisms have the
potential of altering the rates of transcription or translation of the gene
they are closest
to, and if many genes are associated with at least one micro/minisatellite,
then many
and possibly most genes have the potential to contribute in some degree to the
polygenic inheritance of the phenotypes they control.
Many studies of the potential role of specific genes in psychiatric or other
disorders start with a search for mutations in the exons of a candidate gene.
When
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such studies are negative it is often concluded that the gene has nothing to
do with the
disorder being examined. If the majority of the mutations involved in complex,
polygenic traits are in non-exons, such conclusions would be invalid. The
present
hypothesis does not exclude a role for exon mutations that have only a minor
effect on
the function of the gene, it only emphasizes that these studies can be
negative despite
the presence of functionally important variants of that gene.
If on average, each candidate gene contributes to only 0.5-8% of the variance
of a given quantitative trait, depending upon the phenotype and the size of
the study,
the results may be of modest, borderline or negative significance. However,
the
examination of the additive or epistatic effect of two or more candidate genes
may
show a much more significant effect.
Many association studies simply examine the frequency of specif c alleles in
subjects compared to controls. However, when a diagnosis is based on a complex
set
of symptoms, a given allele may show a significant association with several of
the
quantitative traits involved in the diagnosis, but not the diagnosis itself.
For example,
if schizophrenia is a polygenic disorder, the alleles of a specific candidate
gene may
show a significant association with negative symptoms (e.g., affective
flattening),
which are present in some but not all cases, but no association with positive
symptoms
(e.g., delusions or hallucinations), or with the diagnosis of schizophrenia
itself.
Limiting the analysis to simple dichotomous diagnoses, rather than the
examination of
sub-syndromal quantitative traits, may miss genes that make an important
contribution
to a polygenic disorder. Heterosis may also allow a gene to have a significant
effect
on the phenotype in the absence of difference in the frequency of individual
alleles in
controls vs patients (Comings and MacMurray, 1977).
A hypothesis is most useful if it has immediate heuristic value. One of the
disadvantages of the use of highly polymorphic micro/minisatellite
polymorphisms in
association studies is that when each of the large number of alleles, and even
larger
number of potential genotypes is examined, the power of the study may be so
seriously compromised as to render it useless. However, if the size of the
repeats is
the critical variable, even the most complex of polymorphisms can be reduced
to three
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genotypes consisting of homozygosity for the shorter alleles, homozygosity for
the
longer alleles, and heterozygotes. The inventors have found this approach of
value for
studies of the role of a number of genes (OB, MA OA, MAOB, CNRI. GABRA3,
GABRB3, DBH, FRAXA, and NOSI) in behavioral traits.
While many of the potentially interesting candidate genes have been cloned
and sequenced, for most, no polymorphisms have been reported in the Genome
Data
Base, making association studies impossible. Based on the present hypothesis,
if the
sequence is known, one method of identifying useful micro/minisatellites is to
obtain
large genomic clones carrying the gene of interest from commercial sources.
These
can then be screened for repeat sequences which may be highly informative in
association studies.
The present model suggests that the repeat sequences capable of forming
Z-DNA will prove to be the most valuable polymorphisms in studies of polygenic
inheritance. This suggests that screening known sequences using the Z-hunt-II
program (Schroth et al., 1992), could provide important clues about the
location of the
most informative polymorphic regions. To test this, the inventors utilized
this
program to examine the sequence of NOSI (Hall et al., 1994). Based on the
results of
studies showing aggression in knock-out mice, without the NOSI gene (Nelson et
al.,
1995), the inventors had examined various behavioral traits in humans using a
repeat
polymorphism the inventors identified by visual examination of the sequence of
the
NOSI gene (Hall et al., 1994). Since some associations were observed, the
inventors
wondered if: the polymorphism the inventors were using would be identified by
the Z-
hunt II program; and if there were other regions in the gene that might
contain
informative polymorphisms. Both predictions were true. Based on the assumption
that the various lengths of the NOSI (CA)n alleles would play a role in the
function of
the NOSI gene, the inventors divided the alleles into two groups consisting of
the
shortest 50% of the alleles (<199 bp) and the longest 50% of the alleles
(>_199 bp).
Preliminary studies suggest some associations with behavioral phenotypes. The
inventors then screened the published sequence of the NOSI gene for Z-DNA
regions
using the Z-hunt-II algorithm (Wang et al., 1979). This showed that the
polymorphism the inventors were using was one of three regions of high Z-DNA
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content. A second region with an even higher Z-score identified a new
previously
undetected polymorphic region.
These three examples illustrate in practical terms how the predictions of the
model may lead to the acceleration of the identification of the genes involved
in
polygenic disorders. The use of this mode! in conjunction with the sequencing
data
soon to be available from the Human Genome Project may contribute greatly to
the
inventors' knowledge about polygenic disorders through an increased ability to
identify the presence of micro/minisatellites in proximity to the known
candidate
genes.
Monoamine oxidase A gene (MA OA) and VNTR polymorphism alleles
divided into four groups. There was a significant association with a number of
quantitative variables relating to specific symptoms. The results are shown
for a
manic symptoms score in 351 Tourette syndrome probands, their relatives and
controls. There was a significant increase in the score in subjects carrying
the longest
alleles (Gade et al., 1997). (b) Use of the same polymorphism in a group of
substance
abusers and controls. There was again a significant association with the
longest
alleles and a drug abuse score (Gade et al., 1997). (c) Association between a
quantitative score for anxiety, based on the SCL-90 test, and the obesity gene
(OB)
microsatellite polymorphism alleles divided into subjects homozygous for the
<208-by alleles (left) and individuals homozygous or heterozygous for the
>_208-by
alleles (right). (d) Association between the amplitude of the event-related
potential
(ERP) P300 wave (in uvolts) and the CNRI cannabinoid receptor gene
microsatellite
polymorphism alleles divided into subjects homozygous for the >_S repeat
alleles
(right) and individuals homozygous or heterozygous for the <5 repeat alleles
(left)
(Johnson et al., 1997). (e) Association between the Brown Adult ADD score and
the
GABAA, B3 (GABR83) receptor gene microsatellite polymorphism alleles divided
into subjects homozygous for >_185-by alleles (left) and those homozygous or
heterozygous for the <185-by alleles. (f) Association between performance IQ
and
the normal alleles of the FRAXA locus in random adults assessed for IQ
(Comings et
al., 1997a, b or c?).
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EXAMPLE 27
Additive Effect of Three Adrenergic Genes (ADRA2A, ADRA2C, DBH) on
ADHD in Subjects With and Without Learning Disabilities
The inventors have tested for associations or additive effects between three
adrenergic genes, adrenergic a2A receptor (ADRA2A), adrenergic a.2C receptor
(ADRA2C), and dopamine (~-hydroxylase (DBI~, and ADHD with and without
learning disabilities (LD).
These two types of ADHD and the potential candidate genes the inventors
have assigned to them, are summarized in Table 78.
TABLE 79
ADHD With and Without Cognitive Disabilities (CD)
ADHD without CD ADHD with CD
Cognitive disordersabsent present
Verbal IQ normal low
Brain region involvedprefrontal lobes parietal/temporal
lobes
Brain nucleus involvedventral tegmental locus coeruleus
area
Primary neurotransmitterdopamine noradrenaline
Secondary NA, GABA, serotonin,dopamine, GABA,
neurotransmitter and other serotonin, and other
Function analyzes data and orients to and engages
initiates a responsenew stimuli
Type of attention general selective
deficit
Executive dysfunctionpresent usually not present
Candidate genes DRD2, DRD4, DRD~, ADRAIA-ID, ADRA2A,
DATl, DBH ADRA2C DBH, NT,
PNMT
The correlations reported by Halperin et al. (1997) between plasma MHPG
and verbal but not performance IQ were remarkably similar to the findings of
lower
verbal IQ scores in juvenile delinquents arrested for violent or other major
crimes
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(Hirschi and Hindelang, 1977; Miller, 1988; Shulman, 1951; Moffitt and Silva,
1988).
The negative correlation between plasma MHPG levels and low verbal IQ suggest
that
NA genes might also be involved in antisocial behaviors.
Since adrenergic a2 receptors are the site of action of clonidine. are
enriched
in the prefrontal and parietal attentional centers, and play a role in the
regulation of
synaptic NA turnover, the inventors have sought to determine if different
genotypes of
the adrenergic a2A (ADRA2A), or the adrenergic a2C (ADRA2C) receptor, or the
dopamine ~3-hydroxylase genes are associated with ADHD per se, and if there is
a
preferential association with the ADI-ID + cognitive disorders subtype. The
inventors
utilized the MspI polymorphism in the promoter region of the ADRA2A gene
(Lario et
al., 1997), the dinculeotide repeat polymorphism of the ADRA2C gene (Riess et
al.,
1992). and the TagI B 1/12 polymorphism of the DBH gene (d'Amato et al., 1989;
Wu
et al., 1997).
Methods. The genotype frequencies of single base pair polymorphisms at the
ADRA?A and DBH genes and a dinucleotide repeat polymorphism at the ADRA2C
gene were determined in 325 subjects, 267 individuals with Tourette syndrome
and 58
normal controls. The behavioral variables for ADHD and LD were assessed by
parental or self=report questionnaire and personal interview. To assess a
possible
relationship to antisocial behaviors the symptoms of conduct and oppositional
defiant
disorder were also assessed.
The study group consisted of 325 unrelated subjects. Of these 267 fulfilled
the
DSM-III-R and DSM-IV criteria for Tourette syndrome and all were personally
interviewed by D.E.C. The remaining 58 were controls. All were non-Hispanic
Caucasians. The TS subjects came from the Tourette syndrome Clinic at the City
of
Hope Medical Center. The inventors have previously divided the inventors' TS
subjects into those with mild (grade l, chronic tics too mild to treat),
moderate (grade
2, severe enough to require treatment), and severe (grade 3, very significant
effect on
some aspect of their Life) (Comings, 1990; Comings and Comings, 1987b; Comings
and Comings, 1984). Among the TS subjects 25% were grade 3, 12% were grade 1,
and the remaining 71 % were grade 2. TS and ADHD are similar disorders and the
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majority of TS subjects that come to clinics have comorbid ADHD (Comings,
1990;
Comings and Comings, 1987b; Comings and Comings, 1984). The presence of
controls, TS subjects without ADHD and TS subjects with ADHD make this group
particularly well suited to examining the association between the alleles of
different
genes and ADHD as a continuous trait variable. Both the TS subjects and the
controls
have been described in detail elsewhere {Comings et al., 1996j; Comings,
1994a;
Comings 1994b; Comings 1995a, Comings, 199Sb}. The age of the TS subjects
averaged 18.0 years (S.D. 13.2). While the majority were older children and
adolescents, 29% were 21 years of age or older. The mean age of the controls
was
46.3 years {S.D. 15.38).
Each control and TS proband was required to fill out a questionnaire which
included assessment of all of the DSM-III, DSM-III-R, and DSM-IV criteria for
ADHD. For the older subjects, the questions referred to when they were
children.
The ADHD score was based on the DSM-IV (1994) criteria for ADHD. The
l5 questions asked if, during childhood and adolescence, these symptoms were
never or
rarely present (score = 0), occasionally present (score = 1 ) or always
present (score
= 3). All three responses were used to provide a full discrimination between
those
with all degrees of severity. The ADHD score was the sum of the DSM-IV
inattention. impulsivity, and hyperactivity symptoms.
Parents filled out the questionnaires when the children were less than 14
years
old, while subjects 14 years of age or older filled out the questionnaires
themselves or
with the assistance of their parents. The questionnaires were reviewed in
person with
subjects and family members to ensure their accuracy.
To assess the presence of problems with teaming disorders, subjects were
2S asked three questions. 1 ) Have you ever been placed in an educationally
handicapped
(EH), learning handicapped (LH) or learning disorder (LD) special class? 2)
Have
you ever been placed in a resource class? 3) Have you ever been told you had a
learning disorder? Each question was scored no = 0 or yes = 1 and added to
form the
LD score. In California, placement in any of the above special classes
requires a
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thorough evaluation by one or more educational psychologists, and the
assessment
that the student is two years or more behind his peers.
. To assess the academic performance in grade school the subjects were asked
"For grades 1 to 6 was your school performance on the whole average, below
average,
or above average in the following? a) math, b) reading, c) writing. The
answers for a
- c were scored as above average = 0, average = I , and below average = 2, and
summed to give the GSAP score.
A similar 0, 1, and 2 grading of the DSM-IV criteria for conduct disorder and
oppositional defiant disorder was used.
For dichotomous classifications, subjects were considered to have significant
ADHD problems if they met DSM-IV criteria for Attention-Deficit/Hyperactivity
Disorder, Combined type; Predominately Inattentive type; or Predominately
Hyperactive-impulsive type. Subjects were considered to have a learning
disorder if
they had been placed in one of the above special classes and told they had a
learning
disorder. Subjects that meet criteria for ADHD and a learning disorder were
classified
as a A+LD+ group. Subjects that did not meet criteria for ADHD but did meet
criteria
for a learning disorder, were classified as a A-LD+ group. Subjects that met
criteria
for ADHD but not for LD were classified in the A+LD- group. Finally, subjects
that
meet neither criteria were classified in the A-LD- group. A similar grouping
was used
based on the GSAP scores where those with a score of 0 - 4 were scored as GS-,
and
those with a GSAP score of 5 - 6 were scored as GS+.
The questionnaire (Comings, 1990) and is meant to provide a highly structured
method of providing quantitative variables relating to ADHD, the subscores of
the
ADHD scale, problems with learning, grade school academic performance, conduct
disorder and oppositional behavior. The accuracy, utility and sensitivity of a
questionnaire based approach to symptom evaluation has been demonstrated by
others
(Gadow and Sprafkin, 1994, Grayson and Carlson, 1991 ) by comparing the use of
- such an instrument to an interviewer administration of the same structured
instrument
The inventors' review of the questionnaires with each subject has indicated
they
accurately reflect the information obtained by personal interview.
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The ADRA2A was genotyped using a single base pair MspI polymorphism of
the promoter region (Lario et al, 1997). Their PCRTM conditions and primers
were
used. The ADRA2C was genotyped using the dinucleotide repeat polymorphism and
PCRT~" procedure (Riess et al., 1992). The polymerise chain reaction (PCRT'~)
was
used to amplify the target DNA using 0. 1 pM of each fluorescent labeled
primers.
The PCRT~' product was diluted with 100 pl of deionized water and 0.5 p.l was
added
to 2.5 pl of a mixture of 75 ~1 formamide + 9.5 pl of ROX standard + 9.5 pl of
Blue
dextrin dye. This was denatured for 2 sec at 92°C and the sample loaded
on 6%
PAGE of Applied Biosystems 373 DNA sequencer (Applied Biosystems, Inc., (ABI)
Foster City, CA) and gel was run for 4 h at 1200 volts and constant 30 W. The
gel
was preprocessed and analyzed using the internal standard, ROX 500. The peaks
were recognized by genotyper (version 1.1 ) (Applied Biosystems, Inc.) based
on the
color and the size of the fragments. This produced major alleles at 183 and
185 bp, a
less frequent 181 by allele, in addition to several very minor alleles . These
were
placed into three genotypes: 5183/<_183 by = 1, heterozygotes = 2, and
<_183/_<183 by
= 3.
The DBH gene was genotyped for the TaqI B 1/B2 polymorphism (d'Amato et
ul., 1989). While this originally required southern blotting with a labeled
DBH clone,
the inventors have adopted it to a PCRT"" based test (Wu et al., 1997).
The genotyping of the dopamine genes (DRD2 and DA TI ) has been described
previously (Comings et al., 1996). For the DRD~ gene the inventors used the
microsatellite polymorphism and technique {Sherrington et al., 1994).
Three types of statistical analysis were performed. First, to evaluate the
effect
of the ADRA2A and ADRA2C genotypes, ANOVA was performed on the total ADHD
score with each gene scored as 11 = l, 12 = 2, and 22 = 3. Once the genotype
grouping was chosen each gene was assigned a dichotomous variable to indicate
the
presence or absence of the relevant genotype. An A2A+A2C score was made to
evaluate the additive effect of the two genes. Here those with an ADRA2A score
of 0
and a ADRA2C score of 0 = 0. Those with an ADRA2A score of l and an ADRA2C
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score of 1 = 2. Those with a score of 1 for either gene were scored as 1. The
NA
genes score was formed by adding the individual scores for all three NA genes.
- To eliminate the need for Bonferroni correction, all six behavioral scores
were
examined as a group using MANOVA. This was done for each gene separately and
for the genes together. To obtain an estimate of the percent of the variance
of the
scores that were accounted for by each gene individually and together, a
linear
. regression analysis was performed for each of the individual gene scores,
the
A2A+A2C genes score, and the NA genes score, against the behavior scores.
Based
on the a priori hypothesis that the effect of the NA genes would be additive,
to
statistically evaluate the magnitude of this effect a linear ANOVA (polynomial
subcommand = 1 ) was performed for the results of adding the ADRA2A and ADRA2C
genes, or the ADRA2A, ADRA2C and DBH genes. A post hoc tukey analysis for
means that were significantly different at a = .05 was performed. All
statistical tests
were performed using the SPSS statistical package (SPSS, Inc, Chicago, IL).
Results. ANOVA was performed to examine the effect of the ADRA2A
genotypes on the ADHD score. The mean score for those carrying the I 1
genotype
was 17.4 (n = I 80, s.d. = 11.1 }, for the 12 genotype was 19.05 (n =125, S.D.
= 10.52)
and the 20 genotype was 22.05 (n =20, S.D. = 11.67), F-ratio = 2.05, p =.13.
For
MANOA the ADRA2A gene was scored as 11 =1, 12 =2 and 22 =3. For regression
analysis it was scored as 11, 12 = 0 and 22 =1.
MANOVA was performed for the six scores (Table 80-A). The significant
associations were with LD, GSAP, impulsivity, and the total MANOVA. Linear
regression analysis was performed for the ADHD, LD, GSAP, CD and ODD scores
(Table 3). There was a significant association with the LD and GSAP scores. If
a
Bonferroni corrected a of .OS/5 or .O1 was the LD score remained significant.
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TABLE 80
MANOVA for the ADRA2A, ADRA2C and DBH Genes
(N = 325)
Scores F-ratio p
A. ADRA2A gene
Inattention 1.45 .236
Impulsivity. 3.67 .027
Hyperactivity 1.23 .295
GSAP 3.42 .034
LD 3.80 .023
conduct 0.27 .763
ODD I .21 .299
Total (Wilkes) 1.8i .034
B. ADRA2C gene
Inattention 5.18 .006
Impulsivity 4.11 .Oi 7
Hyperactivity 2.82 .061
GSAP 1.91 .149
LD 1.78 .169
conduct 1.44 .239
ODD 1.64 .194
Total (Wilkes) 1.09 .360
C. DBH gene
Inattention 2.02 .134
Impulsivity. 1.28 .278
Hyperactivity 1.12 .325
GSAP 2.48 .086
LD 1.40 .506
conduct 1.40 .247
ODD 1.71 .182
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Scores F-ratio p
Total (Wilkes) 0.53 .9I5
D. ADRA2A + ADRA2C genes
Inattention 5.20 .006
Impulsivity 5.51 .004
Hyperactivity 3.73 .025
GSAP 5.26 .006
LD 5.40 .005
conduct 0.35 ,709
ODD 3.06 .050
Total (Wilkes) 1.62 .070
E. ADRA2A + ADRA2C + DBH genes
Inattention 5.25 .002
Impulsivity 4.21 .006
Hyperactivity 3.76 .011
GSAP 5.01 .002
LD 3.62 .013
conduct 0.85 .465
ODD 3.38 .018
Total (Wilkes) 1.49 .070
F. ADRA2A + ADRA2C + DBH genes + DRD2
+ DA TI + DRDS
Inattention 4.81 <.001
Impulsivity 5.31 <,0p
l
Hyperactivity 4.44 <,p
10
v GSAP 4.41 <.001
LD 2.91 .009
- conduct 2.80 .011
ODD 4.73 <.001
Total (Wilkes) 1.58 .01
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Table 81
Linear Regression
Analysis for the
ADRA2A, ADRA2C and
DBH Genes
(N = 325)
r rZ T p
ADHD score
ADRA2A gene .086 .007 1.57 .117
ADRA2C gene .161 .026 2.94 .0035
DBH gene .107 .011 1.94 .054
A2A + AZC .181 .033 3.32 .0010
All 3 NA genes .200 .040 3.67 .0003
All 3 DA genes .157 .025 2.85 .0047
NA + DA genes .247 .061 4.56 <.0001
LD score
ADRA2A gene .152 .023 2.78 .0056
ADRA2C gene .077 .006 1.40 . I 62
DBH gene .057 .003 1.04 .291
A2A + A2C .136 .019 2.49 .013
All 3 NA genes .136 .018 2.48 .0135
All 3 DA genes .028 .001 0.51 .61
NA + DA genes .I 1 .012 1.97 .049
I
GSAP score
ADRA2A gene .128 .016 2.32 .020
ADRA2C gene .102 .010 1.86 .065
DBH gene .122 .015 2.22 .027
A2A + A2C .148 .022 2.70 .007
All 3 NA genes .185 .034 3.40 .0008
All 3 DA genes .097 .009 1.73 .085
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r r2 T p
NA + DA genes .203 .042 3.66 .0003
conduct score
ADRA2A gene .0008 .0000 0.01 .989
ADRA2C gene .045 .002 0.83 .409
DBH gene .088 .008 1.59 .113
A2A + A2C .041 .002 0.74 .462
All 3 NA genes .085 .007 1.54 .125
All 3 DA genes .071 .005 1.25 .211
NA+DA genes .109 .012 1.92 .054
ODD score
ADRA2A gene .085 .007 1.55 .122
ADRA2C' gene .093 .009 1.70 .091
DBH gene .100 .010 1.81 .070
A2A + A2C .I21 .015 2.20 .028
All 3 NA genes .151 .023 2.77 .006
All 3 DA genes .162 .026 2.89 .004
NA + DA genes .221 .049 4.00 .0001
ANOVA was performed to examine the effect of the ADRA2C genotypes on
the ADHD score. The mean score for those carrying the 11 genotype was 18.9
{n = 122, S.D. = 10.41 ), for the 12 genotype was 15.90 (n = 112, S.D. =
10.99), and
for the 22 genotype was 20.54 (n = 91, S.D. = 11.1 ), F-ratio = 4.90, p =
.0080. By the
post hoc Tukey test the mean for the 12 heterozygote was significantly lower
than for
the 1 I or 22 homozygotes indicating the presence of negative heterosis
(Comings and
MacMurray, 1998). Thus, for the linear regression analysis the ADRA2C gene was
scored as 12 = 0 and 11 or 22 = 1.
MANOVA for the ADRA2C gene score was performed for the six behavioral
scores (Table 80-B). The significant associations were with the inattention
and
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impulsivity. By linear regression analysis (Table 81 ) the correlations were
significant
for the ADHD score. A t a = .01, the ADHD score was still significant.
The inventors have previously studied the dopamine B-hydroxylase gene in
TS - ADHD subjects (Comings et al., 1996j) and found the presence of the Tag
B1
allele (d'Amato et al., 1989) was associated with ADHD. Thus, the inventors
have
scored the DBH gene as 22 = 0 and 11 or 12 = 1. By MANOVA (Table 80-C) none
of the scores were significant. By linear regression analysis, the correlation
with the
GSAP scores were significant. At a .01, none were significant.
To examine the possible additive effect of both adrenergic a2 receptor genes
the individual gene scores were added to form a A2A+A2C score. Of 325
subjects,
106 (32.6%) had a score of 0, 205 (63.1%) had a score of l, and 14 (4.3%) a
score of
2. The results of MANOVA for A2A+A2C score and the six behavioral scores are
shown in Table 80-D. The results were significant for the inattention,
impulsivity,
hyperactivity, GSAP, LD and ODD scores. By linear regression analysis (Table
81 )
the correlations were significant for the ADHD, LD, GSAP, and ODD scores. At
a = .O1 the ADHD, GSAP scores remained significant. Linear ANOVA analysis for
all except the CD score, showed a progressive increase in all scores
progressing from
subjects with 0, 1 or 2 variant genes. The ADHD, inattention, impulsivity,
hyperacidity, and GSAP scores were all significant at p < .Ol .
To examine the possible additive effects of three adrenergic genes, the
ADRA2A, ADRA2C and DBH gene scores were added to form the NA genes score.
Of the 325 subjects, 36 (11.1% had a score of 0, 132 (40.6%) a score of 1, 145
(44.6%) a score of 2, and 12 (3.7%) a score of 3. The results of MANOVA for NA
genes score and the six behavioral scores are shown in Table 80-E. The scores
for
inattention, impulsivity, hyperactivity, GSAP, LD, and ODD were all
significant. By
linear regression analysis (Table 8 I ) the scores for ADHD, LD, GSAP, and ODD
were significant. At a -- .O1 the ADHD, GSAP and ODD scores remained
significant.
Linear ANOVA analysis of the ADHD subscores, LD, GSAP, and ODD
scores for NA genes score showed there was a progressive increase in all
scores across
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subjects with 0, 1, 2, or 3 variant genes. The ADHD, hyperactivity,
inattention,
impulsivity, GSAP, and ODD scores were all significant at p < .01.
. The inventors performed a chi square analysis of the NA gene score versus
the
four groups of ADHD-LD-, ADHD+LD-, ADHD-LD+ and ADHD~LD+ using linear
_ 5 ANOVA. These results are shown in Table 82. This showed that the frequency
of
subjects carrying all three variant NA genes increased across these groups
from .6%
for the ADHD-LD- group, to 5.4% for the ADHD+LD- group, to 7.7% for the
ADHD-LH+ group, to 11.4% for the ADHD+LD+ group. The frequency of subjects
carrying no variant NA genes decreased across these four groups from 14.4% to
8.9%
to 7.7% to 2.9%. When the entire 4 x 4 tables was tested by linear trend chi
square
analysis (Cochran, 1954) p = .0005. If the A-LD+ and A+LD+ groups were
combined, Pearson chi square = 17.6, d.f. = 6, p = .007, and linear trend chi
square
= 12.9, d.f.: 1., p = .0003.
TABLE 82
Chi square Analysis of the NA Gene Score versus the ADHD(A)-LD-,
ADHD+LD-, ADHD-LD+ and ADHD+LD+ Groups
[Number of cases (%)]
NA score A-LD- A+LD- A-LD+ A+LD+ T
0 24 (14.5) 10 (8.9) 1 (7.7) 1 (2.9) 36 (11.1)
1 73 (44.2) 41 (36.6) 4 (30.8) 13 (37.1 131 (40.3)
)
2 67 (40.6) 55 (49.1 7 (53.8) 17 (48.6) 14b (44.9)
)
3 1 (.6) 6 (5.4) 1 (7.7) 4 (11.4) 12 (3.7)
T 165 {51.1)112 (34.3) 13 (4.0) 35 (10.7) 325
( 100.0)
Pearson chi square 18.3, d.f. = 9, p -.03
Linear trend Chi square 12.0, d.f. = 1, p = .0005.
Since the association between the NA genes and the GSAP score was often
greater than with the LD score, the inventors also examined a cross tabulation
between the GSAP score and the NA genes score. This showed that frequency of
subjects with a NA genes score of 3 ranged from 1.6% for those with a GSAP
score of
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0 to 2, to 0.8% for those with a score of 3 to 4, to 11.8% for those with a
score of 5 to
6. For the whole range of both scores, the linear chi square was significant
at
p = .004. Thus, independent of the presence or absence of ADHD, those children
who performed below average in 2 or 3 academic subjects in grade school, were
most
likely to carry variant NA genes.
To examine the effect of the presence or absence of ADHD. the subjects were
again divided into four groups. Here GS+ = those with a GSAP score of 5 or 6
and
GS- = those with a GSAP score of 0 to 4, and the four groups were A-GS-, A+GS-
,
A-GS+ and A+GS+. The results of a chi square analysis versus the NA score is
given
in Table 83. The frequency of those with a NA genes score of 3 increased from
.6%
to 2.4% to 4.8%, to 12.5% across the four groups. The linear chi square for
the 4 x 4
table was signif cant at p = .0003.
TABLE 83
Chi square Analysis of the NA Gene Score versus the ADHD(A)-GS-,
ADHD+GS-, ADHD-GS+ and ADHD+GS+ Groups [Number of cases (%)1
NA score A-GS-- A+GS- A-GS+ A+GS+ T
0 23 ( 14.7) 7 (8.4) 2 (9.5) 4(6.3 ) 36 ( 1
I .1 )
1 71 (45.2) 31 (37.3) 6 (28.6) 23 (35.9) 131 (40.3)
2 62 (39.5) 43 (51.8) 12 (57.1) 29 (45.3) 146 (44.9)
3 1 (.6) 2 (2.4) 1 (4.8) 8 (12.5) 12 (3.7)
T 157 (48.3) 83 (25.5) 21 (6.5) 64 (19.7) 325
( 100.0)
Pearson chi square 25.89, d.f. =-9, p = .002
Linear trend Chi square 12.97, d.f. = 1, p = .0003.
The inventors examined a below average grade school performance in math,
reading and writing separately. This was done by a chi square analysis of the
NA
genes score versus scoring the math, reading and writing performance as
average or
above average = 0, and below average = I . For math the Pearson x2 = 9.17, p =
.027,
linear x2 = 4.18, p = .04. For reading the Pearson x2 = 15.14, p = .0017,
linear r2=
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13.14, p = .0003. For writing the Pearson X2 = 13.18, p = .004, linear x2 =
10.54,
p = .001. Thus, while below average performance in all three areas was
significantly
associated with the NA genes score, the effect was greatest for reading, then
writing,
and least for math.
For comparison the inventors also examined the dopamine genes score against
each academic subject. For reading Pearson x2 = 12.08, p = .007, linear x 2 =
8.24,
p = .004. For math Pearson X2 = 2.90, p = .41, linear x 2 = 2.21, p = .137.
For writing
Pearson r 2 = 1 1.1 l, p = .011, linear ~. 2 = .02. Thus, the dopamine genes
also played
a role in reading performance, but a much less of a role in reading and math
performance.
To examine the relative importance of NA versus dopamine (DA) genes the
inventors included the DA genes score in the MANOVA (Table 80-F). With all six
genes the inattention, impulsivity, hyperactivity, GSAP, LD, conduct, and ODD
scores were all significant. By linear regression analyses for all six genes
(Table 81 )
the correlations were significant for the ADHD, LD, GSAP, and ODD scores. As
shown previously (Comings et ul., 1996) the dopamine genes play a significant
role in
ADHD. This was confirmed with the three DA genes used here. They accounted for
2.5% of the variance of the ADHD score and when added to the NA genes all six
accounted for 6.1 % of the variance (p < .0001 ). By contrast, the DA genes
played
little role in the LD and GSAP scores. However, for the GSAP scores, the
percent of
the variance increased from 3.4% for the three NA genes alone to 4.2% when the
DA
genes were added. While the DA genes played little role in the conduct score,
they
played a significant role in the ODD score, accounting for 2.6% of the
variance
(p = .004). All six genes accounted for 4.9% of the variance of the ODD score
(p = .0001 ).
When the frequency of the three DA genes were the inventors examined in the
four ADHD:LD groups, all three genes were present in 10.0% of the ADHD-LD-
group, 19.8% of the ADHD+LD- group, I 5.4% of the ADHD-LD+ group, and 17.1
of the ADHD+LD+ group. Thus, in contrast to the NA genes, there was no
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progressive increase across the three groups. For the entire 4 x 4 table, the
Pearson
chi square = 10.48, d.~ = 9, p = .31, and the linear chi square = 0.90, d.~ =
I, p = .34.
MANOVA showed a significant association between quantitative scores for
inattention. impulsivity, hyperactivity, learning disorders, grade school
academic
performance and oppositional defiant behavior with the NA genes examined. The
greatest association (p = .0003) was for the additive effect of ail three
genes. There
was a significant increase in the number of variant NA genes progressing from
subjects without ADHD (A-) or learning disorders (LD-), to A+LD, to A-LD+, to
A+LD+ (p = .0005), but no comparable effect for dopamine genes.
Discussion. The identification of two subtypes of ADHD, one with and one
without cognitive defects, that involve distinct regions of the brain,
distinct
neurotransmitters, and distinct sets of genes (Table 80), could have
considerable
diagnostic and treatment value in the care of ADHD children. It must be kept
in mind,
however. that ADHD is a polygenic disorder (Comings, 1996a) with multiple
variant
genes being inherited from both parents (Comings, 1996b). As such it is likely
that
there would be considerable overlap with most children and adults having
components
of both types.
However, as shown in Table 82 and Table 83, the defects in NA metabolism
and variant NA genes are more likely to be involved in ADHD children with
cognitive
defects than ADHD children without cognitive defects.
Looking at additive scores also has the advantage that if a gene happens not
to
play a role in a given subject, or group of subjects, but a different gene
with a similar
function is present, the effect of both can be included. This is well
illustrated in the
present study. For example, when the phenotypic effect of each of three NA
genes
was examined separately, the significance levels of the MANOVAs were modest
(Table 80-A, Table 80-B and Table 80-C} and the correlation coefficients
frequently
failed to withstand a Bonferroni correction for the five scores examined by
linear
regression analysis (Table 81 ). However, when either the ADRA2A and ADRA2C or
the ADRA2A and ADRA2C and DBH genes were added, the results were robust. This
was also the case when the progressive additive effect was tested by linear
ANOVA.
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Using the ADHD score as an example, univariate regression analysis showed that
each gene individually accounted for only 0.7 to 2.6% of the variance of the
quantitative score (Table 82). However, when the ADRA2A and ADRA2C genes were
added they accounted for 3.3% of the variance (p = .001 ) and when all three
NA
genes were added they accounted for 4.0% of the variance (p = .0003). The
three
dopamine genes accounted for 2.5% of the variance of the ADHD score, and when
all
six genes were combined, they accounted for 6. I % of the variance (p < .0001
}. This
indicates that NA and DA genes play an additive role in ADHD.
Twin studies suggest that additive genetic effects account for 70%, or more of
ADI-ID (Sherman et al., 1997a; Sherman et al., 1997b; Gillis et al., 1992).
This
indicates that the three NA genes accounted for approximately 6% of the
genetic
component of ADHD, and the NA plus the DA genes accounted for approximately
10% of the genetic component. While this may seem small, as more and more
genes
are added, the percent continues to rise (Comings et al., 1998). It should
also be noted
that even though the six genes account for only 6. I % of the variance of the
ADHD
score, based on the correlation coefficient of .25, depending on other
variables, these
six genes have up to 25% predictability for ADHD.
The additive effect of the NA genes was also impressive for the GSAP score
with the percent of the variance increasing from 1.0 to 1.6% for the
individual genes,
to 3.4% for all three.
The results shown in Table 83, showing a progressive increase in the
frequency of subjects carrying 2 or more variant NA genes across the A-LD-,
A+LD-,
A-LD+, and A+LD+ groups. However, an alternative hypothesis is that the ADHD +
LD group, or ADHD + any other comorbid disorder group, simply represents a
group
with greater genetic loading for all the ADHD genes. The inventors were able
to test
this alternative by determining if a score for three different dopaminergic
genes gave
the same progressive increase across the three groups. In the subjects, the
inventors
have observed that of six different dopamine genes (DRDI, DRD2, DRD3, DRD~,
DRDS, and DATl ) the DRD2, DA TI and DRDS had the most significant additive
effect on the ADHD score. Individually they accounted for .5 to 1.0% of the
variance
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while together they accounted for 2.5% of the variance. Thus, the inventors
made a
dopamine genes score that also ranged from 0 to 3. The percent of the subjects
that
carried all three variant genes ranged from 10.0% for the A-LD- group, to
19.8% for
the A+LD- group, to 15.4% for the A-LD+ group, to 17. I % for the A+LD+ group.
Thus, contrary to the alternative hypothesis, there was not an increased
genetic
loading for these three dopamine genes for the A+LD+ group compared to the
A+LD-
group. In addition, for the whole range of dopamine gene scores, neither the
Pearson
nor the linear trend chi square results were significant. This stands in
marked contrast
to the linear chi square p = .0005 for the NA genes score. These results
further
support the I-Ialperin-Pliszka hypothesis b supporting the concept that NA
genes are
preferentially involved in ADHD+LD individuals, while dopamine genes are
equally
involved in ADHD whether cognitive defects are present or not.
An additional feature of note was that simply inquiring about an individual's
academic performance in grade school for math, reading and writing gave as
good a
separation by the number of variant NA genes as whether they had been placed
in a
special LD class. When examined separately the effect was greatest for
reading,
almost comparable for writing, and least for math performance. These results
suggest
that grade school children with ADHD who are doing below average in reading,
writing and math are the children most likely to be carrying variant NA genes.
This
also suggests a need for long term double blind studies of the efficacy of
clonidine in
the treatment of the academic problems of such children. In the personal
clinical
experience of one of the inventors {D.E.C.), over a period of several months,
the
treatment of ADHD children with poor grades with transdermal clonidine alone
can
result in a significant increase in academic performance.
The association between low verbal IQ and aggressive juvenile antisocial
behavior has been replicated in many studies (Hirschi and Hindelang, 1977;
Miller,
1988; Shulman, 1951; Moffitt and Silva, 1988; Moffitt, 1990). This association
between iow verbal IQ and delinquency holds up even after controlling for
factors of
socioeconomic status, race, academic achievement and motivation in taking the
test
(Moffitt, 1993). Moffitt and Silva (Moffitt and Silva, 1988) also showed that
the
association of low IQ and delinquency is not an artifact of slow-witted
delinquents
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being more easily caught by the police since undetected delinquents identified
by
interview were also found to have low IQs. The inventors tested NA genes for a
possible association with conduct disorder or oppositional defiant disorder.
While
there was no significant association between any of the three genes and
conduct
disorder, there was a modest association with oppositional defiant behavior.
The use of subjects with TS to study ADI-iD potentially has both advantages
and disadvantages. A practical advantage is that the inventors have a large
collection
of over 1,500 DNA samples on TS patients, all of whom have completed an
extensive,
highly structured questionnaire based on the Diagnostic Interview Schedule
(Robins et
ul., 1981 ) and DSM-IIIR criteria. This allows the inventors to select for
study
subjects with relatively severe symptoms. As such they are more likely to have
high
genetic loading than those with minimal symptoms. The use of more extreme
phenotypes has been widely recommended in genetic studies {Risch and Zhang,
1996;
Plomin et al., 1994). Since the majority, but not all (Comings and Comings,
1984;
1987b; Knell and Comings, 1993) TS patients referred to the clinic also have
ADHD,
this provides a set of subjects both with and without ADHD. This is
particularly
useful for the inventors' approach of examining the entire range of a
quantitative
ADHD trait, since it provides a wider range of scores than if only ADHD
subjects
were studied (Comings et al.. 1996j). A TS population is also useful because
of the
high frequency of comorbid disorders such as ADHD, conduct disorder,
oppositional
defiant disorder, and learning disorders in both the TS probands and their
relatives
(Knell and Comings, 1993; Comings and Comings, 1987a; Comings, 1995a,
Comings, 1995b). Biederman and colleagues have reported the same high degree
of
comorbidity for ADHD probands and their relatives (Biederman et al., 1990a,b;
Biederman et al., 1993). A potential disadvantage of using a TS sample is that
despite
the considerable overlap in clinical and genetic aspects (Comings and Comings,
1993), ADHD subjects with tics might have a slightly different set of variant
genes
than ADHD subjects without tics. Thus, it would be important to replicate
these
findings in ADHD subjects without tics. A further limitation of this study was
the
reliance on parental and self report questionnaires. Studies using WISC-R,
WRAT-R
and other direct testing are desirable. However, in the inventors' experience,
based on
actual personal interviews with all the subjects studied, these questionnaires
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accurately reflect the subject's respective behaviors. In addition, since the
inventors
have never observed a case in which a child has been placed in an EH, LD, LD
or
special education class who did not in fact have significant learning
problems, the
inventors believe that the assessments for the LD score in this manner
represents a
robust test for the presence of cognitive disabilities. Ironically, the
assessment of
whether a child performed below average in two or more academic subjects
(math,
reading, writing) in grade school, provided a better separation of the NA
genes score
than having been in an LD class. This indicates that actual classroom
performance
can provide a reliable estimate of cognitive abilities, and that many children
who are
doing poorly academically, are never placed into special classes.
Since there were so few 22 homozygotes at the ADRA2A gene, and thus a
small number of subjects scored as carrying both adrenergic a,2 genes or all
three NA
genes, one could argue that the present results might be driven by the chance
presence
of high scores in these few homozygotes. To test for this, the inventors also
scored
the ADRA2A gene as 11 = 0 and 12 or 2 = 1. When the two a2 genes were combined
using this scoring, the MANOVA was still significant for inattention (p =
.OIO), for
impulsivity (p < .OOI ) and for hyperactivity (p = .02 1 ) and by linear
regression this
combination still accounted for 3.1% of the variance (p = .0014). When the
ADRA2A
gene scored in this fashion was added to the ADRA2C and DBH genes there were
now
66 subjects with a NA genes score of 3. The MANOVA was significant for the
inattention (p = .00I ), the impulsivity (p = .001 ) and hyperactivity (p =
.004) scores.
By linear regression analysis this NA score accounted for 4.1 % of the
variance
(p = .0002). These results indicate that the present findings are not simply
due the
chance occurrence of high scores in the few ADRA2A 22 subjects.
The inventors contemplate that the present findings could be strengthened by
adding several additional NA genes were not included in the study. These
include the
genes for the noradrenaline transporter (NT), the adrenergic a 1 receptors
(ADRAIA to
ADRAID), and the PNMT gene, all of which play a role in the regulation of
synaptic
NA levels.
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EXAMPLE 28
ASSOCIATION OF THE NEURONAL NICOTINIC ACETYLCHOLINE
RECEPTOR a4 (CHR1VA4) GENE WITH ATTENTION DEFICIT
HYPERACTIVITY DISORDER AND TOURETTE SYNDROME
Introduction. Nicotine is an addicting drug which stimulates dopamine
reward pathways, enhances attention, arousal, learning and memory and has been
effectively used transdermally in the treatment of Tourette syndrome and ADHD.
The
- most prominent neuronal form of the nicotinic acetylcholine receptor is
a4~32. The
inventors hypothesized that genetic variants at the neuronal nicotinic
acteylcholine a4
receptor CHRNA4 gene might be associated with ADHD or Tourette syndrome.
The neuronal nicotinic acetylcholine receptors are composed of a (a2 - a9)
and ~3 (~32 - (34) subunits arranged around a central channel (Unwin, 1993).
The a
subunits possess a pair of cysteines similar to the a l subunit of the muscle
type of
nicotinic acetylcholine receptor. The ~3 subunits do not have this pair of
cysteines.
I S The major neuronal nicotinic acetycholine receptor subtype is composed of
a4 and (32
subunits {Whiting et al., 1991; Schoepfer et al., 1988). Because the a4 and
X32 were
the most widely expressed subunits in the brain (Wada et al., 1989; Whiting
and
Lindstrom, 1988; Whiting et al., 1991), the inventors sought to examine the
association between alleles of the CHRNA=l gene and CHRNB2 genes in Tourette
syndrome and ADHD. Since suitable polymorphisms were available only for the
CHRNA=l gene (Weiland and Steinlein, 1996), this is the gene the inventors
examined.
Methods. The inventors examined the association of the alleles of a complex
VNTR polymorphism of the CHRNA4 gene in 282 unrelated, non-Hispanic Caucasian
Tourette syndrome subjects and 63 controls. The study group consisted of 345
unrelated subjects. Of these 282 fulfilled the DSM-IV criteria for TS and all
were
personally interviewed by D.E.C. The remaining 63 were controls. The TS
subjects
came from the Tourette Syndrome Clinic at the City of Hope Medical Center.
These
are from the same group of subjects described in Example 27 dealing with the
role of
norepinephrine genes in ADHD and learning disorders. The behavioral scores and
the
separation by presence or absence of ADHD and learning disorders, are as
described
in that article. The ADHD score was based on the DSM-IV (Diagnostic, 1994)
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criteria for ADHD. The questions asked if, during childhood and adolescence,
these
symptoms were never or rarely present (score =_0), occasionally present (score
= 1 ) or
always present {score = 3). All three responses were used to provide a full
discrimination between those with all degrees of severity. The ADHD score was
the
sum of the DSM-IV inattention, impulsivity, and hyperactivity symptoms.
The polymorphism in the first intron of the CHRNA~ gene described by
Welland and Steinlein (Weiland and Steinlein, 1996) was utilized. The PCRTM
primers and conditions reported by them was used. In a study of 88 unrelated
Caucasians, they identified 14 alleles ranging from 196 to 264 by in length
(see Table
84).
Polymerase chain reaction (PCRT~ was used to amplify the target DNA using
0.2 ~M of each fluorescent labeled primers. The PCRTM product was diluted 10
fold
with deionized water and 0.5 pi of the diIuent was added to 2.5 p.l of mixture
made of
75 pl formamide + 9.5 ~1 of ROX standard + 9.5 pl of Blue dextrin dye. This
was
denatured for 2 min at 92 °C and the sample loaded on 6% PAGE of
Applied
Biosystems 373 DNA sequencer (Applied Biosystems, Inc., (ABI) Foster City, CA)
and gel was run for S h at 1200 volts and constant 30 W. The gel was
preprocessed
and analyzed using the internal standard, ROX 500. The peaks were recognized
by
genotyper (version 1.1 ) (Applied Biosystems, Inc.) based on the color and the
size of
the fragments.
Results. The inventors have genotyped 345 individuals in the present study
and several hundred in other studies. Not surprisingly, in addition to those
described
by Wetland and Steinlein (Weiland and Steinlein, 1996) the inventors found a
number
of additional alleles - 21 in all. Of these 17 were observed in the subjects
in the
present study. Since Wetland and Steinlein (Weiland and Steinlein, 1996)
utilized
silver staining and began counting alleles from the top to the bottom of the
gel, their
low numbered alleles were of the highest by and the highest numbered alleles
were of
the lowest bp. Since the ABI sequencer scans bands as they reach the bottom of
the
gel, in the inventors' procedure the low number alleles represented the lowest
by
alleles while the highest number alleles are the largest alleles. While the
inventors
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initially made every attempt to use the same numbering system as that of
Welland and
Steinlein (Weiland and Steinlein, 1996) since the sizes of the alleles in bp,
based on
sequencing, were different from theirs, especially at the extremes, this was
difficult.
Thus, the inventors have used the ABI numbering system and for comparative
purposes, the inventors have listed the two nomenclatures in Table 84.
Table 85 shows the results with sequencing four of the PCRT"' products for
different alleles. This illustrates the complexity, of this VNTR polymorphism.
Of the major alleles, the frequency of the 9, 12, 14, and 16 alleles were
higher
in TS subjects while the frequency of the 13, 15 and 18 alleles were higher in
the
controls. By chi square analysis of the 17 alleles x2 = 33.65, d.f. = 16, p =
.0061.
When the analysis was restricted to the alleles present in either the controls
or TS
subjects at a frequency of .OS or more, x2 = 71.6, d.f: = 6, p < .0000001. The
greatest
difference in allele frequencies was for allele #9.
To keep the analysis straight-forward and to allow the examination of
I S quantitative variables and the effects of homozygosity versus
heterozygosity, and to
produce a variable for regression analysis, the CNRNA=1 gene score assigned
those
with an x/x genotype (non-9/non-9) = 1, 9/x = 2 and 9!9 = 3.
In addition to the set of behavioral variables examined Example 28 on
norepinephrine genes (Comings et al.. 1998), the inventors also examined a
total tic
score (Comings et crl., 1996). The association between the CNRNA~ gene score
and
these variables were simultaneously examined using MANOVA. The results (Table
86) showed that there was a significant association between the CNRNA4 gene
and the
grade school academic performance (GSAP), learning disorder (LD), oppositional
defiant (ODD) and hyperactivity scores.
To evaluate the role of the different genotypes on these scores, a post hoc
ANOVA analysis of the total ADHD score and each variable significant by
MANOVA at p < .1 was performed (Table 87). This showed that every score was
highest for 9/9 homozygotes.
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Since an additional question was whether there was any association between
the CNRNA~t gene and smoking, the inventors examined two variables, 'ever
smoked?'
and 'packs smoked per day 7' in subjects who were over 17 years of age.
Neither was
significant, the mean number of packs smoked per day was 0.5 for the 9/9
homozygotes versus .10 to .20 for the other genotypes. Since there were only 9
subjects who were 9/9 homozygotes, a larger sample might give significant
results.
When a chi square analysis of the three CNRNA=I genotypes versus the four
categories of ADHD-LD-, ADHD+LD-, ADHD-LD+ and ADHD+LD+ were
examined, the frequency of the 9/9 genotype increased from 5.6% in the ADHD-LD-
group, to 6.0% for ADHD+LD- group, to 7.1 % for the ADHD-LD+ group and 13.5%
for the ADHD+LD+. The total correlation was not significant by Pearson chi
square
(p = .385), and of borderline significance by linear chi square (p = .051).
Thus, unlike
the norepinephrine genes examined (Comings et al., 1998), there was little
evidence
that the CNRNA;t gene played a role in the type of ADHD-LD associated with the
parietal lobe.
Univariate regression analysis using the genotype variable versus the total
ADHD score gave the following results: r = .123, r2 = .015, T = 2.30, p =
.022. There
was an increase in the frequency of the 9, 12, 14 and 16 alleles, greatest for
the 9
allele, in TS subjects, and an increase in the 13, 15 and 18 alleles, greatest
in the 13
allele, in controls. When all alleles were compared by chi square they were
significantly different at p = .002. When only the alleles with a frequency of
greater
than .05 were compared the groups were significantly different at x2 = 71.6,
d.f. = 6,
p < .0000001. Eight quantitative scores relating to ADHD, tics, conduct and
learning
versus genotype groupings based on the 9 allele (9/9, 9/x, x/x) were examined
by
MANOVA. The association was significant for the grade school academic
performance, learning disabilities, oppositional defiant, and hyperactivity
scores.
ANOVA showed that the magnitude of all scores was greatest for the 9/9
homozygotes. Linear regression analysis of the genotype scoring showed the
CHRNA4 gene accounted for up to 1.5% of the variance of the ADHD score {p =
.022).
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Conclusion. These results suggest the CHRNA=t locus is one of a polygenic
set of genes that contribute to the risk for ADHD and TS.
. Sequencing of the PCRT"' amplified segment used in these studies indicates
this is a very complex polymorphism involving both differences in size and
differences in sequence. The repeat polymorphisms themselves may play a role
in the
regulation of the genes with which they are associated. This is based on the
tendency
for these repeats to form Z-DNA. Both changes in length and sequence of the
repeats
play a role in determining the amount of Z-DNA formed which plays a role in
gene
regulation in a number of ways (Comings, 1997). The complex polymorphism
examined here showed significant changes in both size and sequence. As such it
may
be uniquely suited for association studies.
To avoid the loss of power involved in the independent assessment of many of
the other scores, the inventors restricted their analysis to the same
behavioral scores
examined in Example 2'7 plus a total tic score. Since nicotine has been used
in the
treatment of tics, this was added to determine if this gene was associated
with tics per
a~e. MANOVA was used to assess the scores as a group, obviating the need for a
Bonferroni correction. This showed a significant association with four of the
eight
scores (Table 86). When a post hoc ANOVA study of each variable was
undertaken,
the scores were highest for the 9/9 homozygotes. The combined results suggest
the
CHRNA~ is one of the genes that plays a role as a genetic risk factor in ADHD
and
TS, and especially TS subjects with ADHD. Regression analysis suggests it may
contribute to up to i .5% of the variance of the ADHD score.
Although the CHRNA4 showed somewhat greater association with learning
disorders and grade school academic performance than the ADHD subscores,
unlike
the norepinephrine genes examined in the companion article, there was little
evidence
for a progressive increase in the frequency of the 9/9 genotype across the
four groups
of those with or without ADHD and with or without learning disorders.
. While the inventors do not yet know whether the genotype groupings
described are associated with an increase or decrease in expression of the
CHRN4A
gene, the present studies support the clinical effectiveness of nicotine on
ADHD and
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TS symptoms. It is also likely that in other populations the differences
between
controls and TS or ADHD may involve some of the alleles other than the 9
alleie.
Over multiple samples, the most reproducible result may be the comparison by
chi
square analysis of the frequencies of the common alleles in controls versus TS
or
ADHD subjects.
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Table
84
Comp arison ed on pplied Biosystems,
of Comings the Inc
and A
Wu Alleles
bas
DNA Sequencer us the of Wellandand Steinlein
vers Alleles
{Weiland
and Steinlein,
1996)
Comings Wetland
and and
Wa Steinlein
No No
Allele Match Match freq. Allele Match Match freq.
bP bP bP bP
I 4 196 .04
13 198 .O1
1 203 .008
2 207 .000 12 208 .01
3 215 .024
4 217 .063
221 .000 11 208 .O1
6 223 .008 10 220 .04
7 225 .238 9 222 .Oi
8 227 .024
9 231 .015 8 228 .06
233 .008
11 235 .032
12 237 .063
13 239 .302 7 236 .45
14 241 .024
243 .238 6 240 .19
_ 16 245 .008 5 242 .O1
17 253 .000 4 248 .Ol
18 255 .063 3 250 .11
19 257 .024 2 252 .03
259 .000
1 264 .0l
21 272 .008
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H
U
C~
U
H
7
U
C7
,,.; U
U
as
_
C. = =
C7
s.
H
H C7
z
U U U U
~ V ~ ~
C C
7 7
~
t7 C7 C7
U U U U
r U U o U
,
w C7 C7 C7 C7
U
t C7 C7 ~
7
o. U U U U
V C7 C7 C7
C7 C'7 L~ C7
U U U U U
~, C7 C7 C7 C7
C7 C7 C7 t7
w U U U U
a o U U U
a C~ C7 C7 C7
U U U U
~
H H ~ H
~i
U U U U
~ ~
E- H E H
- -~
U U U U
'n C7 C7 C7 C7
U U U U
U U U U
O~ M ~f
N N N N
~O ~n o0 Q~
~' 01 M d'
St N N N N
d
~ ..
CA 02288990 1999-10-27
WO 98/48785 PCT/US98/08684
Table 86
MANOVA for the Eight Behavior Scores
(N = 345)
CHRNA4 9/9=3, 9/x=2, xlx=1
F-ratio p
GSAP 3.60 .028
LD 3.60 .028
ODD 3.46 .033
Hyperactivity 3.10 .046
Inattention 2.92 .055
Tics 1.63 .198
Impulsivity 0.92 .401
CD 0.55 .573
Total (Wilks) 1.22 .246
Table
87
ANOVAs for he ividual
t Ind Scores
for
the
ADHD
Factor
CHRNA4 (n =
345)
Score Genotype N Mean S.D. F p F~ p~
ADHD xlx 204 17.28 11.12
13/x 113 18.94 11.05
9/9 23 22.61 9.09 2.83 .060 5.27 .022
*
Hyperactivityxlx 209 5.75 4.43
91x 113 6.18 4.18
9/9 23 8.08* 3.86 3.1I .045 4.85 .028
Inattention x/x 209 8.72 5.53
9/x 112 9.73 5.5 5
9/9 23 11.08 4.88 2.69 .069 5.34 .021
ODD x/x 209 3.45 3.21
9/x 113 3.83 3.19
9/9 23 5.26* 2.87 3.46 .032 5.60 .0185
LD ~ xlx 209 0.61 0.93
91x 113 0.84 1.03
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score Genotype N Mean S.D. F p F~ p~
9/9 23 1.04 1.10 3.61 .0287.21 .0076
GSAP x/x 209 2.78 1.99
9/x 113 3.35* 1.90
9/9 23 3.40 1.92 3.61 .0286.49 .011
Other
Ever smoked x/x 91 1.39 0.49
(age> I 7 yrs) 9/x 49 I .31 0.46
9/9 6 1.66 0.52 1.66 .1920.000 .995
Packs/day xlx 91 0.12 0.44
9/x 49 0.10 0.37
9/9 6 0.50 0.84 2.24 .1101.02 .314
* significantly different .OS
from x/x at a = by
Tukey
test.
Fi - linear ANOVA,
p, = p for linear ANOVA
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EXAMPLE 29
CORRELATION OF LENGTH OF VNTR ALLELES AT THE X-LINKED
MAOA GENE AND PHENOTYPIC EFFECT IN TOURETTE SYNDROME
AND DRUG ABUSE
Introduction. Abnormalities in monoamine oxidase (MAO) levels have been
implicated in a wide range of psychiatric disorders. The inventors have
examined a
VNTR polymorphism at the X-linked MA OA gene to test two hypotheses: ( 1 ) Do
variants of the MA OA gene play a role in any of the behavioral disorders
associated
with Tourette syndrome or drug abuse? (2) If so, is there any correlation
between the
length of the alleles and the phenotypic effect? The inventors examined two
independent groups: 375 TS patients, relatives and controls, and 280 substance
abusers and controls. The alleles were divided into four groups of increasing
size.
There was a significant association between the MA OA gene and behavioral
phenotypes in both groups, and in both the longest alleles were associated
with the
greatest phenotypic effect. The strongest effect was for the diagnosis of drug
dependence (P = 0.00003). The VNTR allele groups were in significant link-age
disequilibrium with the Fnu4H1 polymorphism previously shown to be associated
with MAO-A activity. These results are consistent with the possibility that
different-sized alleles of the short-repeat polymorphisms themselves may play
a role
in gene regulation.
Because of the potential effect of age (Devor et al., 1994), alcohol,
anti-depressants, drugs, gender, laboratory technique, diet, and other
variables, on
platelet enzyme levels (Fowler et al., 1982), the use of genetic polymorphisms
at the
MAO genes may give more reproducible results than enzyme levels. The cloning
and
sequencing of the MA OA and -B genes and identification of associated
polymorphisms now allow such genetic studies.
To determine if genetic variants at the MAOA gene were associated with TS or
ADHD, the inventors have examined the MA DA VNTR polymorphism (Hinds et al.,
1992) in a series of controls, TS probands and their relatives, using the
techniques
reported in the DRD2, D~H and DA TI genes in TS (Comings et al., 1996a). Since
a
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simple comparison of the frequency of the different alleles in controls vs TS
probands
might miss the possibility that the MAO genes were only associated with a few
specific behaviors not present in all cases, the inventors tested for the
possible role of
these genes in 27 different behavioral variables.
The inventors have become interested in the hypothesis that the length of the
alleles per se might be related to phenotypic effect. The rationale for this
is that the
sequence of most simple repeats result in the formation of Z-DNA with the
amount
being dependent upon the length of the repeat (Schroth et al., 1992). The Z-
DNA
conformation opens the DNA helix and exposes the individual bases, making it
uniquely capable of interacting with nuclear proteins (Rich et al., 1984). For
these
and other reasons Z-DNA has been implicated in gene regulation (Comings,
1996a;
1996b). If the mini- and microsatellite polymorphisms do play a role in the
variations
in gene function involved in polygenic inheritance, their effect must be
subtle, since if
the effects were major, they would result in single gene rather than polygenic
I S disorders.
X-linked genes form a unique vehicle to examine this hypothesis and search
for subtle effects since, at least in males, each allele is hemizygously
present thus
eliminating the confounding factor of heterozygosity, which can be extensive
when
multiple alleles of different size are present. To test the hypothesis that
repeat length
might be related to phenotypic effect the inventors divided the VNTR alleles
into four
groups of increasing length. This allowed the inventors to determine if the
shorter or
longer alleles were preferentially associated with a greater phenotypic
effect.
Methods. The subjects included 57 controls, 229 TS probands most of whom
were severely affected with multiple associated behavioral disorders (Comings,
1990),
and 90 affected and unaffected relatives of TS probands. All subjects were
non-Hispanic Caucasians and over 90% were of Western European origin. The
controls for the TS group consisted of adopting and step parents of TS
probands,
subjects with non-psychiatric disorders from other clinics at the City of
Hope, and
professional and non-professional hospital staff from the City of Hope Medical
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Center. Both the TS subjects and the controls have been described in detail
elsewhere
(Comings, 1995b; Comings et al., 1996a; 1997b).
Each TS control and TS proband or relative was required to fill out a
questionnaire based on the Diagnostic Interview Schedule(Robins et al., 1981 )
or
DSM-III-R(Diagnostic and Statistical Manual of Mental Disorders, 1987)
criteria.
This provided a structured review of a wide range of psychiatric symptoms.
These
symptoms were grouped into 27 different behaviors including ADHD, substance
abuse, mood, anxiety, school performance, stuttering, tics and others. The
questions
used for these behavioral scores have been described in detail elsewhere
(Comings,
1995a; 1994a; 1994b; 1995b; Comings et al., 1996a; Robins et al., 1981;
Comings,
1995c). Two behavioral scores were used to assess ADHD. The first, called
ADHD,
was based on the presence of at least half of a series of 22 ADHD variables
from
DSM-III and DSM-III-R criteria. The second, ADHD-R was based on the DSM-III-R
diagnostic criteria. Three QTVs not used previously were inattention,
impulsivity and
hyperactivity. These were the three subscores that cumulatively produced the
ADHD
score. QTV abbreviations include CD for conduct disorder, ODD for oppositional
defiant disorder (Comings, 1995a), and MDE for major depressive episode
(Comings,
1995c) symptoms.
The rationale for examining comorbid behaviors is the prior observation that
certain genes may be more strongly associated with specific comorbid behaviors
present in TS than with the diagnosis per se (Comings et al., 1996a). This
questionnaire is not meant to provide DSM-III-R or DSM-IV diagnoses but rather
to
provide a highly structured method of producing QTVs for different areas of
behavior.
The advantage of continuous traits is that they provide a greater range of
severity than
- 25 dichotomous diagnoses. The accuracy, utility and sensitivity of a
questionnaire-based
approach to symptom evaluation has been demonstrated by others (Gadow and
- Sprafkin, 1994; Grayson and Carlson, 1991) by comparing the use of such an
instrument to an interviewer administration of the same structured instrument.
The
. inventors' review of the questionnaires with many hundreds of subjects has
indicated
that they accurately reflect the information obtained by personal interview.
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A second group of subjects consisted of 120 non-Hispanic Caucasian males
from an inpatient Addiction Treatment Unit (ATU) of the Jerry L Pettis
Veterans
Administration Hospital in Loma Linda, California, Since October 1994, all new
admissions to the ATU who give informed consent, were entered into a National
Institute of Drug Abuse sponsored study of genetic factors in drug
abuse/dependence.
All ATU subjects were assessed with the Michigan Alcoholism Severity Test
(Davis et al., 1987}, a 24-item self administered questionnaire revised to
include drug
abuse {MAST-R), the clinician-administered Diagnostic lnrerv;P.N c~-hP.~l7~P
(DSM-III-R version) (Robins et al., I981), to diagnose the presence of
substance
dependence disorders, and the clinician-administered Addiction Severity Index
Fifth
Edition (ASI) (Hodgins and Guebaly, 1992), to evaluate a range of alcohol and
drug
use variables.
The inventors utilized the Drug/Alcohol use and the legal status sections of
the
ASI. The areas covered were the following:
a) Specific substances used To assess the use of specific substances,
questions were asked about the lifetime use (in years) of alcohol use to
intoxication,
heroin, other opiates/analgesics, barbiturates, other
sedatives/hypnotics/tranquilizers,
cocaine, amphetamines, cannabis, hallucinogens, and inhalants.
b) Route o~~ administration. For each of the above, where relevant, the
subjects were asked about the route of administration. The options were oral,
nasal,
smoking, and IV injection. The continuous variable #IV drugs used was
calculated by
adding up the total number of different drugs injected IV. The variable IY
drug use
was a dichotomous variable of 0 for no IV drug use and <_1 for use of one or
more
drugs IV.
c) Problems. 'How many times have you had alcohol DTs? Overdosed
on drugs?' 'How many days in the past 30 days have you experienced alcohol
problems? Drug problems?'
d) Money spent. 'How much would you say you spent during the past 30
days on alcohol? On drugs?'
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e) Severity. An interviewer-based severity assessment for the need for
treatment ranged from 0 (no treatment necessary} to 9 (treatment needed to
intervene
in a life-threatening situation}. Alcohol abuse? Drug abuse?
Legal status. Questions were also asked about various legal aspects of
drug and alcohol abuse. 'How many times in your lifetime were you charged with
driving while intoxicated?' 'How many times in your lifetime were you arrested
and
charged with drug charges? How many of these charges resulted in convictions?'
g Summary scores. When the responses could range from 0 to any
number, they were scored as a '0' for a 0 and a ' 1' for any other number.
Those
questions relevant to alcohol use were summed for a total alcohol score and
those
relevant to drug use were summed for the drug score.
The controls for the substance abuse group were independent of the controls
for the TS patients. They consisted of two sets. The first were 45 older male,
non-Hispanic Caucasian students from the California State University at San
Bernardino (mean age of 30.1 years). Those with significant problems with
substance
abuse were excluded on the basis of the MAST-R test. The second set consisted
of
the male parents of twins from the Minnesota Twin Family study. Since these
are
ascertained from the entire state simply on the basis of having had twins 1 1
or 17
years of age, they represent a more random set of all socioeconomic and
educational
groups than the college students. Although all the controls were scored as
negative on
the substance abuse variables, since the results of substance abuse
assessments were
not yet available on the twin controls, some may have been positive. However,
since
this is a random cross-section of a predominately rural state the inventors
assume the
number of false negatives in this group is small.
The rationale for choosing the VNTR polymorphism at the MA OA gene is as
follows. A short tandem repeat polymorphism was chosen to specifically examine
the
hypothesis that the length of the repeat might be associated with a phenotypic
effect.
An X-linked gene was chosen since, at least when males are studied, the
complication
of how to interpret heterozygotes is avoided. An MAO gene was chosen because
it is
X-linked. The MAOA gene was chosen because two different repeat polymorphisms
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WO 98/48785 . PCT/US98/08684
have been reported to be associated with it. The inventors chose the VNTR
polymorphism (Hinds et al., 1992) because it gave a wider spread in allele
size
(40+bp) than the (CA)n repeat (16 bp).
This complex polymorphism consists of a GT microsatellite directly adjacent
to an imperfectly duplicate novel 23-by VNTR motif, with alleles differing in
both the
number of dinucleotide repeats and VNTR repeats. The VNTR polymorphism was
present in a 2.9-kb SaII-EcoRI fragment from phage 6.12 which contained the
first
exon of the MA OA gene (Hinds et al., 1992). DNA was extracted from whole
blood
by standard procedures. Target DNA was amplified by PCRT"' (Mullis et al.,
1986).
To label the PCRT"' products, 0.1 ~,M of each primer labeled with fluorescent
HEX or
FAM Amidite (Applied Biosystems, Foster City, CA, USA) primers were used in
the
reactions (Table 88). Two microliters of the 10-fold diluted PCRTM product
were
added to 2.5 p.I deionized formamide and 0.5 p,l of ROX 500 standard (Applied
Biosystems) and denatured for 2 min at 92°C and loaded on 6%
polyacrylamide gel in
an Applied Biosystems 373 DNA sequencer. The gel was electrophoresed for 5 h
at
1100 V and constant 30 W. The gel was laser scanned and analyzed using the
internal
ROX 500 standards. The peaks were recognized by Genotyper (version 1.1 )
{Applied
Biosystems) based on the color fragments sized by base pair length. Complete
information for each sample was printed from every gel file and the compiled
data
were submitted for analysis.
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WO 98/48785 PCT/US98I08684
a
VJ GC ' tC t0 t0 (O tC tV cC tC R
p, N O~ ~O~N ~D ~_ N d: h ~ ~ d' 00
M 00 00 V7 00 M I~ h
p \D Ul V1 N l!7 ~h~ 'CT~ M M M
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b0
d M W n .-. O d O ~n ~ ~O h
R ~--~O~ O t~ M ~D00 N M O O~ ~n
O O O O O O O N N N
O O O O O O O O O O O O
L O
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CA 02288990 1999-10-27
WO 98/48785 PCTIUS98/08684
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CA 02288990 1999-10-27
WO 98/48785 PCTIUS98/08684
>,
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CA 02288990 1999-10-27
WO 98/48785 PCT/US98/08684
To examine the hypothesis that the length of the MA OA alleles might correlate
with a phenotypic effect, the alleles were divided into four groups (see
Results).
These were labeled 1 to 4, shortest to longest to form the MAOA genotype
variable.
Females were utilized only in the TS group. Only those that were homozygous
for a
given allele group were included in the analysis.
The inventors used two rules for bining: a) there must be enough groups to
examine a range of lengths; and b) to maximize statistical power the number of
subjects should be similar in each group. Thus, if there was a single teak of
allele
frequencies the division would have been into the shortest 1/3, the middle 1/3
and the
longest 1I3. However, the distribution of allele sizes for the MA OA VNTR was
into
two peaks. For males only, the smaller peak contained 32% of the alleles and
ranged
in size from 299 to 314 by in length. The larger peak contained 68% of the
alleles
ranging in size from 323 to 338 by in length. Since the majority of the
alleles were in
this peak the inventors divided it as if there was a single peak (i.e.
shorter, middle, and
I S longer group of alleles). The center of this peak contained a single 334
by group
consisting of 31 % of the alleles that could not be subdivided. The
application of these
rules resulted in four bins <320 bp, 320-333 bp, 334 by and >_335 by
consisting of 32,
23, 31 and 14% of the alleles.
It was not possible to use the binning described by Hinds et al. (1992)
because
three of their five groups had a very iow allele frequency. In fact, the
inventors had
no subjects in any of these three minor groups.
Fnu=lHl polymorphism The test for this polymorphism was based on the
procedure of Hotamisligil and Breakefield (1994). The inventors have termed
their'-'
as the inventors' 1' allele and their '+' as the inventors' '2' allele. In
their study the +
allele had the higher MAOA activity.
For the Tourette syndrome group, ANOVA was used to examine the relative
magnitude of each QTV for the four different allele groups. Linear ANOVA was
used
to test for a significant progressive increase in means across the four allele
groups.
The SPSS (SPSS, Inc, Chicago, IL, USA) statistical package was used. For
linear
ANOVA the subcommand polynomial was set to I . MANOVA was used to determine
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CA 02288990 1999-10-27
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if any of the QTVs were significant when all the variables were examined
simultaneously. Multivariate linear regression analysis was used as a second
approach to determine if any of the QTVs was significant when all the
variables were
examined simultaneously. The MA OA genotype was set as the dependent variable
and the 27 QTVs were entered stepwise as the independent variables.
Chi Squared analysis indicated that the group with the longest alleles had the
highest means for the majority of the QTVs. The potential progressive decrease
in
frequency of the >_335 by allele group was compared across four groups with
progressively fewer TS symptoms: TS probands with ADHD, TS probands without
ADHD, relatives with TS and relatives without TS.
For the Substance Abuse Group, MANOVA was used to determine if there was
a significant association between the four MAOA allele groups and the two
summary
variables, the alcohol and the drug score. ANOVA was used to examine the means
of
the alcohol and drug scores for the four allele groups.
Linear chi square was used to examine the potential progressive increase in
the
frequency of the __>335 by group across three groups: controls, the substance
abusers
without the behavior (ATU without), and the substance abusers with the
behavior
(ATU with). The ATU without group was included to rule out the possibility
that this
allele group might be increased in frequency in the substance abusers because
of
comorbidity for a different behavior. To help exclude this, the frequency of
the allele
group had to be at least 20% higher in the substance abusers with the behavior
than
without the behavior. Since the hypothesis was that the frequency of these
alleles
would progressively increase across these three groups, the linear chi square
statistic
was used.
To determine the maximum percent of the variance of drug-related variables
accounted for by the MA OA gene, regression analysis was performed in which
subjects carrying the <335 by alleles were scored as 1, and those carrying the
>_335
alleles scored as 2. This was performed for the drug dependence variable
(controls: 1,
ATU without scored 2, and ATU with scored 3) since this was the chi square
variable
most highly associated with the MA OA gene.
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Results. Distribution of the alleles of the MAOA VNTR polymorphism (total
number of was alleles = 768). Since this was a complex VNTR the alleles did
not fall
into a clear-cut pattern of even or odd numbers of base pairs. The results are
shown
exactly as they were generated by the Genotyper program. There were no alleles
between 316 by and 323 bp, thus producing two clear major groups of <320 and
>320 bp. However, to allow an examination of the hypothesis that phenotypic
effects
might be related to size, the alleles of the larger 323-339 by group were
divided into
three sub-groups consisting of alleles shorter than the main peak 320-333 bp,
the main
peak of 334 bp, and alleles longer than the main peak of >_335 bp. There were
219
males and 156 females for a total of 375 subjects in the TS group. Of the
females, 88
were heterozygotes. When these were removed it left 287 subjects in the study
of
whom 36 were controls. In this final group, there were no significant
differences in
the frequency distribution of the four allele groups in males vs females.
The ANOVA results for each of the QTVs vs the four allele groups for the TS
group are shown in Table 88. The results for regular ANOVA are shown under
F-ratio and P value. The F-ratio for linear ANOVA is shown under the FZ
column,
with a superscript of a for those that were significant at <0.05. The QTVs are
ordered
by the decreasing magnitude of the F-ratio in the FZ column. Those allele
groups
where the means were significantly less than for the >_335 by group, as
determined by
the Tukey test with a set at <_0.05, are shown by an asterisk. With the
exception of
stuttering, shopping and panic (which gave the lowest F-ratio), for the
remaining 24
QTVs the means were highest for those subjects carrying the >_35 alleles.
The results of MANOVA for all 27 QTVs were significant for sexual
{P = 0.012), learning problems {P = 0.023), gambling (P = 0.025), and mania
(P = 0.025).
When all 27 QTVs were examined simultaneously in a stepwise multivariate
regression analysis, the variable grade school problems (P = 0.012) and
gambling
(P = 0.038) were signif cant. Based on the r' values, the MA OA gene accounted
for
only 3.9% of the variance of these QTVs.
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Using Chi Square analysis, there was a significant progressive decrease in the
percent of subjects that carried the >_335 alleles, progressing from TS
probands with
ADHD (24%, n = 129), to TS probands without ADHD (20.0%, n = 50), to relatives
with TS (12.5%, n = 16) to non-TS relatives (5.6%, n = 56) (P = 0.003).
S Controls vs ATU subjects in the Substance Abuse Group were compared. For
the 160 combined controls, the distribution of the four allele groups was as
follows:
<320 34.4%, 320-333 38.1%, 334-335 21.3%, ?335 6.3%. For the 120 ATU subjects,
the frequencies were as follows: <320 39.2%, 320-333 i 8.3%, 334 20.8%, >_335
21.7%. These were significantly different, X~ = 22.17, P = 0.00006. The
frequency
of the >_235 by group was comparable in the two control groups, 8.9% for the
San
Bernardino group and 5.2% for the parents of the twins (X' = 0.744, P = 0.38).
MANOVA for the alcohol and drug score indicated that while both showed a
significant association with the MA DA gene VNTR alleles, this was more
significant
for the drug score (P = 0.001 ) than for the alcohol score (P = 0.012) (Table
89). The
result for the combined MANOVA was also significant (P = 0.007). The n of 257
is
smaller than the total of 160 controls + 120 ATU or 280, because only 97 ATU
subjects had completed the ASI. By contrast, all 120 completed the DIS for
verification of the DSM diagnosis of alcohol and/or drug dependence.
TABLE 89
MANOVA for Alcohol and Drug Scores for the Substance Abuse Group vs the
MA OA Allele Groups (n = 257 Males Only)
Variable F ratio p
Alcohol score 3.72 0.012
Drug score 5.85 0.001
Total (Wilks) 2.99 0.007
ANOVA for the two scores showing the means for each allele group, are
shown in Table 90. As for the TS group, the highest means were present in the
>_335 by allele group. For the drug score, the three other allele groups were
significantly lower than for the >_335 by group by the Tukey test.
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TABLE 90
ANOVA for Alcohol and Drug Scores of the Substance Abuse Group vs MA DA
Allele Groups
Allele group n Mean s.d F ratio P
Alcohol score
<320 94 2.27 3.1
320-333 81 1.49a 3.0
334 56 1.94 2.7
>_335 26 3.73 3.52 3.72 0.012
Drug score
<320 94 3.59a 5.2
320-333 81 1.94a 3.8
334 56 3.34a 4.9
>_335 26 6.42 6.2 5.85 0.0007
aSignificantly lower than the mean for <335 allele group at ce = 0.05 by the
Tukey test.
To determine if the MAO gene was preferentially associated with certain types
of substance abuse, 14 of the variables relevant to the type of substance used
were
examined using Chi square analysis. The frequency of the >_335 by allele group
in the
controls vs ATU subjects without the behavior (ATU without) vs ATU subjects
with
the behavior (ATU with), is shown in Table 91. Since 14 types of substance use
variables were examined only those with a P of less than 0.0036 (0.05/14) are
considered significant with a Bonferroni correction. Only those with a P of
<0.01 are
shown. The exception is alcohol dependence only. This is shown to illustrate
the fact
that there was little increase in frequency of the >_335 by alleles in
subjects with
alcohol dependence only compared to those with drug dependence, or drug and
alcohol dependence. By contrast. the drug dependence only variable gave the
highest
value (X~ = 17.4, P = 0.00003).
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a~
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CA 02288990 1999-10-27
WO 98/48785 PCTIUS98/08684
The results of regression analysis of the allele group (<335 vs >_335) vs the
diagnosis of drug dependence gave the following results: r = 0.25, r' =
0.0625,
T = 4.305, and P = 0.0001.
To examine the potential linkage disequilibrium between the VNTR and
Fnu4H I alleles, the inventors genotyped 273 males that were also genotyped at
the
VNTR polymorphism. The inventors restricted the analysis to males since the
results
were clearer than in females. There was a highly significant non-random
association
of the alleles at the two polymorphisms (X2 = 132.91, P < 0.000001). The <320
VNTR allele group was associated with the less common Fnu4H 1 2 allele, while
the
remaining three VNTR groups were associated with the Fnu4H 1 1 allele.
Based on the comparison of the data, it would be expected that if the VNTR
alleles were divided into <320 by and >320 by it should give results similar
to the
Fnu4I-I1 polymorphism with the Fnu4H1 2 ~ <320 and Fnu4H1 1 allele ~ >320. For
the TS group there were 71 subjects genotyped at both polymorphisms. Since
mania
gave the most significant results (Table 88) this variable was used for the
comparison.
The mean for the 53 subjects carrying the Fnu4H1 I allele was 2.01 (s.d. 2.17)
and for
the 2 allele was 1.55 (s.d.). The comparable figures for the VNTR were 1.94
(s.d.
2.12) for the >320 bp, and 1.75 (s.d. 1.98) for the <320 by group. (The mean
for the
16 >_335 subjects was 2.43 (s.d. 2.42).) Because of the relatively small
numbers
neither grouping was significant. Since the Hotamisligil and Breakefield study
(1994)
showed the Fnu4H 1 allele {the inventors' I allele) was associated with lower
MAOA
activity, the inventors assume the >_335 VNTR allele group was associated with
the
lowest MAOA activity.
Tourette syndrome is uniquely suited for such studies because it is highly
heritable and is often associated with a wide range of impulsive, aggressive,
affective,
hypersexual and other behaviors. The present results suggest that the MA OA
gene is
one of the genes playing a modest role in the etiology of a number of the
associated
behaviors in TS.
While MANOVA showed a significant association between the MA OA alleles
and both the alcohol and drug scores, there is a great deal of comorbidity of
these two
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forms of substance abuse. As shown in Tabie 91, when drug dependence and
alcohol
dependence were examined separately the association was much greater with drug
than with alcohol dependence.
While the predominance in males is probably due in part to hormonal and
- 5 environmental factors, X-linked genes could also be a factor. For the TS
group,
determination of r' using a regression coefficient. indicated that for the
different
QTVs the MA OA gene accounted for at most 2.5% or less of the variance of any
QTV
suggesting that the X-linked MA OA gene does not account for the male
predominance
of TS, ADI-1D or related disorders. By contrast, the r 2 for the absence or
presence of
the >_335 by alleles vs the diagnosis of drug dependence, suggested that up to
6.2% of
the variance could be due to the MfIOA gene. This could play a modest role in
the
male predominance of drug dependence.
The inventors have begun to suspect that the different length alleles of micro-
and minisatellite polymorphisms might play a role in the regulation of the
genes with
which they are associated. While the association of the longer minisatellite
alleles
with specific QTVs in the Tourette syndrome group was modest, as shown in
Table
89, there was a remarkable degree of uniformity in the trends across ali the
QTVs.
Since this could have been a chance, random association, the inventors sought
to
determine if they could replicate these results in a totally separate group of
subjects
and controls. This group (the substance abuse group) showed an even stronger
association between the longer alleles of the MA OA VNTR, especially the >_335
by
alleles, than was observed in the TS group. The pattern for the two groups is
remarkably similar, with the highest scores for >_335 by alleles, modestly
higher
scores for the lowest size alleles (<320), and intermediate scores for the 334-
335 by
alleles.
To gain some insight into whether the >_33~ by alleles might be associated
with a higher or lower MAO-A activity the inventors also genotyped 273 of the
inventors' males for the Fnu4H1 polymorphism. The linkage disequilibrium with
the
VNTR allele groups was highly significant (P < 0.000001 ). The less common
Fnu4H 1 2 allele was associated with the <320 VNTR group while the more common
I
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allele was associated with the 320-333, 334 and >335 VNTR groups. Since others
have shown that a range of behavioral disorders are associated with low MAD-A
activity, and since the inventors observed that the greatest phenotypic effect
of the
VNTR polymorphism was associated with the >_335 by group, this suggests that
this
group is also associated with the lowest MAO-A activity. These results
indicate that
when the subjects carrying the Fnu4H 1 1 allele are placed into subgroups on
the basis
of the VNTR polymorphism, it is the subjects carrying the >_335 by alleles
that are
driving the Fnu4H 1 results. While the ultimate proof of these suggestions
will require
studies of the VNTR allele in subjects tested for serum or fibroblast MAO-A
activity,
the findings are consistent with the possibility that the reason the Fnu4Hl
polymorphism is associated with differences in MAO-A activity is that the 1
allele is
in linkage disequilibrium with the >_335 VNTR allele, and the large difference
in
number of repeats between the <320 alleles (associated with high MAO-A
activity) vs
the >_335 by alleles (presumably associated with lowest MAO-A activity) plays
a role
1 ~ in the regulation of the MAO-A gene.
This correlation with the size of the repeat alleles is consistent with the
possibility that the minisatellites themselves might play a role in the
regulation of the
MAO genes. However, it is clear that this does not prove the hypothesis since
linkage
disequilibrium with another as yet unidentified site could still be occurring.
Studies
with expression vectors, and the possible interaction of the longer alleles
with
transcription factors, is needed to prove the case for the MA OA gene.
The results for the TS group indicated a relatively low magnitude of the
effect
of the MA DA gene on a range of behaviors. Although four variables were
significant
by MANOVA, two were significant by multivariate regression analysis, and 12 of
the
27 were significant by linear ANOVA, one could object that when a complete
Bonferroni correction is applied to the ANOVA results none are significant at
0.05/27
or 0.0018. However, this is exactly the point, i. e. that despite the large
literature
implicating MAO in different behaviors, when examined at the level of a
specific
gene polymorphism, the ~1~IAOA gene appears to make only a modest contribution
to a
wide range of behavioral variables.- While the effect was much stronger in
drug
abuse, even here the percent of the variance accounted for by the MAOA alleles
was
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CA 02288990 1999-10-27
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still modest. Replication is an important aspect of association studies, and
these
results were found in two completely different sets of subjects. These
findings are
consistent with the concept of polygenic inheritance in which a number of
genes are
involved in various behaviors, each with a small effect; and with the
hypothesis that
the minisatellite polymorphisms themselves may play a role in providing the
functional allelomorphic variants fundamental to polygenic inheritance.
EXAMPLE 30
A PROPHETIC EXAMPLE RELATES TO AMINO-ACID THERAPY AND
PREMENSTRUAL DYSPHORIC DISORDER (PMDD)
Premenstrual dysphoric disorder (PMDD) is a premenstrual mood disorder
that cyclically recurs during the majority of menstrual cycles. It is included
under the
category of "depressive disorders not otherwise specified" in the DSM-IV.
However,
a number of factors (biological and cognitive studies, treatment responses)
differentiate PMDD from other mood disorders (Yonkers, 1997).
I S Despite the predictability of luteal phase symptom expression, the
etiology of
this disorder has not been established. Theories regarding hormonal ad vitamin
deficiencies have been associated with PMS and may or may not be relevant to
PMDD. Nonetheless, neither absolute nor relative deficits of progesterone,
estrogen,
prostaglandin, insulin, vitamin B~, or thyroid hormone (Severino, and Moline,
1989;
Rickels et al., 1990; Bancroft and Retmie, 1993) have been established in
patient
groups with either PMS or PMDD. Similarly, functional hormonal tests such as
the
thyroid-releasing hormone response to thyroid-stimulating hormone and the
results of
glucose tolerance testing are not abnormal in patients with PMDD (Casper et
al.,
1989; Girdler et al., 1995; Haskett et al., 1984; Roy-Byrne et al., 1987).
Invoking a hypothesis that premenstrual symptoms are induced by withdrawal
of endogenous opiates, several groups have evaluated (3endorphin levels in
symptomatic women and controls. In a study that included women who
retrospectively reported premenstrual symptoms, Giannini and colleagues found
a
decline in ~3endorphin during the luteal phase of the cycle (Giannini et al.,
1984);
however, there was no control group in this study. Nonetheless, four other
studies
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have found lower luteal phase (3endorphin levels in symptomatic patients
compared
with controls (Tulenheimo et al.. 1987; Facchinetti et al., 1987; Chuong et
al., I985;
and. Giannini et al., 1990). One of the aforementioned studies found lower
levels in
follicular phase as well as luteal phase (Tulenheimo et al., 1987), and, in an
additional
study, (3endorphin levels were lower in symptomatic women during the peri-
ovulatory
phase (Chuong et al., 1994). Notably, only two of the investigations
previously
mentioned included a population in which symptoms were prospectively
determined
that may or may not have met severity criteria for a diagnosis of PMDD.
Differences
in patient populations, a small sample size or a combination of the two may be
the
I O basis for different conclusions in a recent study that failed to find
differences between
PMDD patients and controls during either phase of the cycle {Bloch et al.,
1996). In
this study, however, ~3endorphin levels decreased during the premenstrual
period in
both groups. Changes in portal blood levels of ~3endorphin during the
menstrual cycle
have also been found in primates, although the difference was most notable
during the
15 peri-ovulatory phase (Wehrenberg, et al., 1982).
Fluctuations of ~3endorphin that decline precipitously during the menstrual
cycle can increase adrenergic activity in women with PMDD and may explain the
results of an investigation into adrenergic receptor binding. Halbreich and
colleagues
(Holbreich et al., 1993) found increased imidazoline receptor binding in
20 premenstrually symptomatic women during the luteal phase of the cycle. As
reviewed
by Grunhaus and colleagues ( 1990), alterations in adrenergic receptor binding
are also
associated with MDD and panic disorder, although the direction of the change
(increased vs. decreased affinity) is dependent on the platelet preparation
and the
ligand used in the assay.
25 Moreover, endorphin and estrogen levels have been shown to vary. During the
postpartum and premenstrual period, levels of both change rapidly and
substantially
(Halbreich and Endicott, 1981 ) and others have shown that the narcotic
antagonists
reduce PMS symptoms.
Halbreich and colleagues found decreases in plasma gamma-aminobutyric acid
30 (GABA) levels during the luteal phase in women with dysphoric premenstrual
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.. ~.~,~.. _. ~ ~ . .. .... . _ u.._ ~ .. , ,
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WO 98148785 PCT/US98/08684
symptoms (Holbreich et al., 1996). Low plasma GABA levels have also been found
in patients with MDD (Petty et al., 1992), although how this may be related to
the
above findings is not known.
Theories on the etiology of PMS have focused almost exclusively on estrogen,
' S progesterone, or prolactin secretion. In contrast, Labrum (1983) in the
mid-80's first
proposed that symptoms occurring in PMS had a common etiological base
involving
abnormal fluctuations in brain levels or serotonin, GABA and interrelated
neuroendocrine processes. Estrogen feedback may be a factor in the excessive
fluctuations, particularly of serotonin.
With regard to serotonin, a number of different approaches have been used to
evaluate this system in women with both PMS and PMDD, including measurements
of serotonin in whole blood, platelet 5-HT uptake, and neuroendocrine
challenge. On
the basis of primate and other evidence that low serotonin is associated with
changes
in sleep, appetite, and irritability, Rankin ( 1992) investigated whole-blood
serotonin
I S in women with severe premenstrual dysphoria and found that compared with
asymptomatic controls, symptomatic women have lower levels of serotonin. Some
investigators (Ashby et al., 1988; Taylor et al., 1984; Ashby et al., 1990)
but not all
groups (Mahngren et al., 1987; Rojansky et al., 1991) find that luteal phase
platelet S-
HT uptake is decreased in women with PMS or PMDD compared with controls.
lmipramine binding sites have also been shown to be reduced in women
specifically
evaluated for PMDD compared with controls during either the early luteal phase
(Rojansky et al., 1991 ) or both phases of the cycle (Steege et crl., 1992).
In the later
study, statistical significance was attained only during the follicular phase.
Administration of tryptophan to women with PMDD produces a blunted
growth hormone and cortisol response during both phases of the menstrual cycle
(Bancroft et al.. 1991 ), suggesting trait differences between PMDD patients
and
controls. However, in the same two groups, the prolactin response to
tryptophan is
. blunted only during the premenstrual phase of the cycle (Bancroft et al.,
1991 ). On
the other hand, when the 5-HT,A partial agonist buspirone is administered to
PMDD
patients and healthy controls during the follicular phase, it produces a
blunted
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WO 98/48785 PCT/US98/08684
prolactin response (Yotham, 1993). Data regarding blunted prolactin response
to
fenfluraminc administration are mixed with one group finding a blunted
response in
well characterized PMDD subjects versus controls (FitzGerald et al., 1996) and
another group finding no differences (Bancroft and Cook, 1995). Finally,
depleting
the serotonin precursor tryptophan is significantly more likely to provoke
premenstrual symptoms during both luteal and follicular phases in PMDD
patients
compared with asymptomatic women (Menkas e1 al., 1994).
A number of recent investigations have been conducted to evaluate certain
psychoactive drugs for the relief of PMDD and includes antidepressants,
including
clonipramine (Sunblad et crl., 1993) fluoxetine (Stone et al., 1991; Wood et
al., 1992;
Steiner et al., 1995; Brandenberg et al.,; Pearlstein and Stone, 1994),
bupropion
(Pearlstein et al., 1995), paroxetine (Eriksson et al., 1995; Yorkers et al.,
1996a),
maprotiline (Erikssor. et al., 1995), sertraline (Yorkers et al.. 1996b),
nefaxodone
(Girdler et al., 1995), and fenfluramine (Brzezinski et al., 1990).
It is expected by the inventors that one-single drug with only limited effects
on
one single or possibly even two individual neurotransmitters is insufficient
to
overcome the abnormal state of the "reward" system which occurs by hormonal
shifts
in the female pre-, during, and post-menstrual phase. Moreover, while the
above
biological evidence does not definitively implicate any single,
neurobiological system,
changes in the adrenergic receptor binding, GABA levels, and various assays of
the 5-
HT system suggest neurobiological abnormalities associated with the expression
of
PMDD. Changes in these markers also are found for unipolar MDD. To date no
studies reported on the positive effects of enkephalinase inhibitors on PMDD.
In fact
the use of Narcotic antagonists like TrexanR (Dupont, Delaware) have been
shown to
reduce rather than enhance PMDD, opposite to what the inventors are doing in
this
invention.
With this in mind coupled with the most recent findings by Figuerola and
associates (deLourdes-Figuerola et al., 1997) showing an increase in plasma ME
and a
decrease in plasma norepinephrine (NE) levels on day 22 in the menstrual
migraine
group and an increase in plasma ME, NE during pain. The authors conclude that
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._ .... .. ~ . , . fi
CA 02288990 1999-10-27
WO 98/48785 PCT/US98I08684
changes occur in plasma ME and in the sympathoadrenal function, not only
during
pain but also in the mid-luteal phase.
The inventors contemplate that the use of amino-acid therapy would be of
benefit to sufferers of PMDD. Work accomplished in a number of alcoholic
patients
S utilizing the composition as proposed by Table 11 specifically for this
disorder
reduces significantly typical PMS symptoms including irritability, tension,
painful
breasts. headaches, and depression. The specific example to be utilized is
specified in
the PMXTM formula outlined in Table 11.
A double blind placebo controlled study will be pursued in an outpatient
PMDD clinic in Dallas, Texas. A total of 100 patients will be studied. A PMDD
Scale will be developed by the inventors and provided to all one hundred
participants.
The scale will be scored prior to receiving any medications (either placebo or
PMXTM). For this study at least three cycles will be the minimum for inclusion
into
the study. The patients must by in child bearing age and not pregnant. A
comparison
1 S will be obtained between the two groups and statistical analysis will be
performed by
the University of Texas Health Sciences Center Department of Computing
Resource
under the direction of Robert Wood. Only Morbid PMDD candidates assessed via
the
DSM-IV criteria will be studied. Following the three month phase, each patient
will
be crossed with either the placebo or PMXTM. Additionally, each patient will
be
genotyped utilizing the MAA technique as proposed in this invention. Therefore
a
total of at least 29 genes will be evaluated.
It is expected that carriers of the DRD2 A1 allele, and the other RDS related
alleles disclosed herein, will respond well to PMXT"' and will have the most
difficult
time under the placebo. It is further expected that genotyping for individuals
will
provide additional information to predict specific targeted treatment
outcomes.
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WO 98/48785 PCT/US98/08684
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(F) POSTAL CODE (ZIP): 78258
(A) NAME: Kenneth Blum
(B) STREET: 1211 Lost Stone
(C) CITY: San Antonio
(D) STATE: TX
(E} COUNTRY: USA
{F) POSTAL CODE (ZIP}: 78258
(A) NAME: David E. Comings
(B) STREET: 59 Crestview Court
(C) CITY: Duarte
(D) STATE: CA
(E) COUNTRY: USA
(F) POSTAL CODE (ZIP): 91010
(A) NAME: John L. Ivy
(B) STREET: 10405 Skyline Drive
(C) CITY: Austin
(D) STATE: TX
(E) COUNTRY: 78759
(F} POSTAL CODE (ZIP) : USA
(ii) TITLE OF INVENTION: ALLELIC POLYGENE DIAGNOSIS OF REWARD
DEFICIENCY SYNDROME AND TREATMENT
(iii) NUMBER OF SEQUENCES: 34:
-627-
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(iv) COMPUTER READABLE FORM:
(A) MEDIUM TYPE: Floppy disk
(B) COMPUTER: IBM PC compatible
(C) OPERATING SYSTEM: PC-DOS/MS-DOS
(D) SOFTWARE: PatentIn Release #1.0, Version #1.30 (EPO)
(v) CURRENT APPLICATION DATA:
APPLICATION NUMBER: US Unknown
{2) INFORMATION FOR SEQ ID N0: 1:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 8 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 1:
ATTTGCGC g
(2) INFORMATION FOR SEQ ID NO: 2:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 8 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 2:
TAAACGCG 8
(2) INFORMATION FOR SEQ ID NO: 3:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 20 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 3:
GCTGCTTCTG TTAACCCTGC 20
-628-
...__~.~..~......~..~~ ~......_.. _ .r .. , .
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(2) INFORMATION FOR SEQ ID NO: 4:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 21 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 4:
TACATCTCCG TGTGATGTTC C 21
(2) INFORMATION FOR SEQ ID NO: 5:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 23 base pairs
(B) TYPE: nucleic acid
(C} STRANDEDNESS: single
(D} TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 5:
GACACTTCTG GAATTAGTGG AGG 23
(2) INFORMATION FOR SEQ ID NO: 6:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 21 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D} TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 6:
GAAGTTAAAT CCATGTGGCT C 21
(2) INFORMATION FOR SEQ ID NO: 7:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 22 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi} SEQUENCE DESCRIPTION: SEQ ID NO: 7:
GCTGATTTTC AGACTGAGTG TG 22
-629-
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(2) INFORMATION FOR SEQ ID NO: 8:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 25 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: B:
CTACAAACAT ATTTAAACAT ATGTT 25
(2) INFORMATION FOR SEQ ID NO: 9:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 24 base pairs
(B) TYPE. nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 9:
CTATTCTTAT CCCTCTTTTC TTAA 24
(2) INFORMATION FOR SEQ ID NO: 10:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 25 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 10:
ATATTCTTAT CCCTCTTTTC TTAAT 25
(2) INFORMATION FOR SEQ ID NO: 11:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 23 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 12:
TATATATTAC GGTTTATTAC CGT 23
-630-
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(2) INFORMATION FOR SEQ ID N0: 12:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 96 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 12:
TCATTAATCC TCTGGGTATT GTAAATGTGG ATTTAGGTTA ATGTATTATA TATAATGCCA 60
AATAATGGCA GATAAGAATA GGGAGAAAAA GAATTA g6
(2) INFORMATION FOR SEQ ID NO: 13:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 20 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 13:
ATTAATCCTC TGGGTATTGT 20
(2) INFORMATION FOR SEQ ID NO: 14:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 23 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 14:
TAGTCTTATC CCTCTTTTTC TTA 23
(2) INFORMATION FOR SEQ ID NO: 15:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 24 base pairs
_ (B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
' (xi) SEQUENCE DESCRIPTION: SEQ ID NO: 15:
CTCTTACAAT AGAAGAAACC ATTT 24
-631-
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(2) INFORMATION FOR SEQ ID NO: 16:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 20 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
{D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID N0: 16:
TCTCCTCTCT TTCCCTTCCC 20
(2) INFORMATION FOR SEQ ID NO: 17:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 19 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
{D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 17:
GCATGGCTGG AAGAACTCC 19
{2) INFORMATION FOR SEQ ID NO: 18:
{i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 22 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 18:
TCTTCCAGGC CTCTGGTCAT AT 22
(2) INFORMATION FOR SEQ ID NO: 19:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 20 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 19:
CACCCATTTA CAAGTTTAGC 20
-632-
. . r w , , ,
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(2) INFORMATION FOR SEQ ID NO: 20:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 22 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
~ (xi) SEQUENCE DESCRIPTION: SEQ ID NO: 20:
CACTGATTAC TAATTCAGGA TC 22
(2) INFORMATION FOR SEQ ID NO: 21:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 21 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID N0: 21:
AGTGGGCAGG GCGGGGCAGG T 21
(2) INFORMATION FOR SEQ ID NO: 22:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 23 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 22:
CGCTGCCTCC CTTCCACCTG TTG 23
(2) INFORMATION FOR SEQ ID NO: 23:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 25 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 23:
AGGTGGCACG TCGCGCCAAG GTGCA 25
-633-
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(2) INFORMATION FOR SEQ ID NO: 24:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 25 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 24:
TCTGCGGTGG AGTCTGGGGT CGGAG 25
(2} INFORMATION FOR SEQ ID NO: 25:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 23 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 25:
GACACTTCTG GAATTAGTGG AGG 23
{2) INFORMATION FOR SEQ ID NO: 26:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 21 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
{xi) SEQUENCE DESCRIPTION: SEQ ID N0: 26:
GAAGTTAAAT CCATGTGGCT C 21
(2) INFORMATION FOR SEQ ID NO: 27:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: I01 base pairs
{B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 27:
TCATTAATCC TCTGGGTATT GTAAATGTGG ATTTAGGTTA ATATATTATA TATAATGCCA 60
AATAATGGCA TAGATAAGGA ATAGGGAGAA AAAGGGAATT A 101
-634-
,.... ? . , ,
CA 02288990 1999-10-27
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(2) INFORMATION FOR 5EQ ID NO: 28:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 25 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 2B:
TAGTCTTATA TCCCTCTTTT TCTTA 25
(2) INFORMATION FOR SEQ ID NO: 29:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 20 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 29:
GCTGCTTCTG TTAACCCTGC 20
(2) INFORMATION FOR SEQ ID NO: 30:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 21 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 30:
TACATCTCCG TGTGATGTTC C 21
(2) INFORMATION FOR SEQ ID NO: 31:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 27 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 31:
CTCTTGTTCC TGTTGCTTTC AATACAC 2~
-63 5-
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(2) INFORMATION FOR SEQ ID NO: 32:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 25 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 32:
CACTGTGCTA GTTTAGATTC AGCTC 25
(2) INFORMATION FOR SEQ ID NO: 33:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 18 base pairs
(B) TYPE: nucleic acid
(C) STRANDEDNESS: single
(D} TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 33:
CATCTCCTGG GACGTAGC lg
(2) INFORMATION FOR SEQ ID NO: 34:
(i) SEQUENCE CHARACTERISTICS:
(A) LENGTH: 20 base pairs
(B) TYPE: nucleic acid
(C} STRANDEDNESS: single
{D) TOPOLOGY: linear
(xi) SEQUENCE DESCRIPTION: SEQ ID NO: 34:
AGAGAAGGGA AGGAGGGAAG 20
-636-
~_ __ ..ra__.wW. . _ ... ;a._m . ~~. , . ,.
CA 02288990 1999-10-27
' s
f
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CECI EST LE TOME ~ DE
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