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DNA pooling analysis of ADHD and genes regulating vesicle release of neurotransmitters.

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American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 139B:33 –37 (2005)
DNA Pooling Analysis of ADHD and Genes Regulating
Vesicle Release of Neurotransmitters
K.J. Brookes, J. Knight, X. Xu, and P. Asherson*
MRC Social, Genetic Developmental Psychiatry Centre, Institute of Psychiatry, United Kingdom
ADHD is one of the most prevalent, and heritable
behavioural disorders in childhood. Genetic associations have been reported with polymorphic
variants within or near to dopamine pathway
genes. Recently snap-25 has also shown association with ADHD in several datasets. We therefore
investigated other genes that produce proteins
that interact with SNAP-25 in the mechanism of
vesicular release of neurotransmitters at the
synapse. A total of 106 SNPs were screened for
minor allele frequency greater than 5% and 61
SNPs selected for analysis in DNA pools made
up from an ADHD clinical sample of DSM-IV
combined type probands (n ¼ 180) and a control
sample of 90 males and 90 females. Initial screening identified several SNPs that showed allele
frequency differences of 5% or more. One SNP
in the synaptophysin gene showed suggestive
evidence of association following case-control and
TDT analysis and warrants further investigation.
ß 2005 Wiley-Liss, Inc.
KEY WORDS:
DNA pooling; ADHD synaptic
components; association
INTRODUCTION
Despite the relative success of candidate gene association
mapping in ADHD [Asherson, 2004] there have been limitations to the rate of progress. The rate limiting-step for most
candidate gene studies has been the cost-effective generation of
individual genotypes. Genotyping individual samples, especially where large sample sizes are required to obtain adequate
statistical power remains expensive and has no guarantee
that an association will be found. For these reasons many
studies have restricted themselves to the analysis of one or only
a few markers for each gene investigated. There are only a few
examples of more systematic analyses that aim to test for
association with markers spanning intragenic regions and,
Abbreviations: ADHD, attention deficit hyperactivity disorder;
SNP, single nucleotide polymorphism; TDT, transmission disequilibrium test; snap-25, synaptosomal associated protein–
25 kD; stx1a, syntaxin 1A; vamp2, vesicle associated membrane
protein 2; syt1, *Synaptotagmin 1; syp, synaptophysin; vmat2,
vesicular monoamine transporter 2; SNARES, soluble N-ethylmaleimide-sensitive factor-attachment protein receptors; MRM,
meta-regression method.
Grant sponsor: Wellcome Trust project (to PA).
*Correspondence to: P. Asherson, MRC Social, Genetic Developmental Psychiatry Centre, Institute of Psychiatry, United
Kingdom. E-mail: p.asherson@iop.kcl.ac.uk
Received 24 March 2005; Accepted 2 June 2005
DOI 10.1002/ajmg.b.30216
ß 2005 Wiley-Liss, Inc.
as a consequence, negative reports fail to exclude association
to a particular gene, even within the power constraints set by
sample size.
One approach to overcome these problems is the use of
DNA pooling to screen dense sets of markers. We have
previously argued that at the present time a DNA pooling
approach using all available single nucleotide polymorphisms
(SNPs) spanning a candidate gene is an efficient method for
initial screening of genes for association [Jawaid et al., 2004].
The approach of using haplotype-tagging SNPs identified
from the HAPMAP database is a suitable alternative for
selection of SNPs for DNA pooling, particularly in regions of
strong marker-marker linkage disequilibrium, but may not
pick up associations with SNPs that are not strongly correlated
with available HAPMAP markers [Sham et al., 2002]. DNA
pooling reduces the amount of work involved when investigating multiple genetic polymorphisms by combining DNA
samples together into case and control pools and estimating
allele frequency differences from the pooled allele image
patterns [Sham et al., 2002]. Positive associations suggested
by the DNA pooling screening method can then be confirmed
and investigated further by conventional use of individual
genotypes.
Here were have applied a DNA pooling strategy to search
for associations between ADHD and single nucleotide polymorphisms (SNPs) spanning six genes involved in the vesicular
release of neurotransmitters at the synapse; syntaxin 1A
(stx1a), vesicle associated membrane protein 2 (vamp2),
synaptotagmin (syt1), synaptophysin (syp), and vesicular
monoamine transporter 2 (vmat2). Interest in vesicular
synaptic proteins follows from reports of association between
ADHD and polymorphic variants within the synaptosomal
associated protein (snap-25) gene [Barr et al., 2000; Brophy
et al., 2002; Mill et al., 2002; Kustanovich et al., 2003; Mill et al.,
2004]. SNAP-25 forms part of a stable membrane bound
ternary complex to permit neurotransmitter release from
vesicles into the synaptic cleft. Proteins involved in
these complexes are known as SNARES (soluble N-ethylmaleimide-sensitive factor-attachment protein receptors) and
are essential for intracellular trafficking. The release of
neurotransmitters relies on the fusion of the vesicle with the
plasma membrane and three main proteins have been identified that are essential in this process; SNAP-25, STX1A, and
VAMP2.
In the neurone, the SNARE proteins form a bridge between
the synaptic vesicle and the plasma membrane, driving the
membrane fusion required for neurotransmitter release.
SNARES form a coiled bundle of 4 alpha helices that run
parallel, aligning the transmembrane domains of VAMP2 and
STX1A at the same end of the bundle. There are two SNARE
groups that complex during exocytosis. (1) The vesicle
membrane SNARE (v-SNARE) is made up of VAMP2, and
contributes one helix to the complex. The availability of
VAMP2 is limited, and is therefore a rate-limiting step in the
control of exocytosis. (2) The target membrane SNARE (tSNARE) is made up of a complex of SNAP-25 and STX1A
that contributes three helices to the fusion process. It is
believed that the proteins STX1A and VAMP2 are the minimal
34
Brookes et al.
machinery needed for membrane fusion [Weber et al., 1998].
It was found that these t- and v- SNAREs could form a stable
complex in the absence of other proteins, although they appear
to be under kinetic control via regulatory proteins in vivo
[Rothman and Sollner, 1997]. Therefore it would seem that
any alteration in their function, might impair the release of
dopamine into the synapse.
Other proteins that function in conjunction with SNARES
are synaptotagmin (syt1) and synaptophysin (syp). Synaptotagmin encodes a protein that is integral to the synaptic vesicle
membrane and binds calcium required for the release of
neurotransmitter at the synapse [Fernandez-Chacon et al.,
2001]. Synaptophysin also encodes a synaptic vesicle integral
membrane protein although is precise function is not known
[McMahon et al., 1996]. The vesicular monoamine transporter
2 (vmat2) acts to accumulate monoamines from the cytoplasm
into synaptic vesicles. It is essential for the correct functioning
of monoamine systems, and has previously been implicated in
human neuropsychiatric disorders [Peter et al., 1993].
METHODS
Study Design
For the initial screening step, equal quantities of DNAs
were combined together into case and control pools from which
allele frequency differences were estimated using a primer
extension assay (Snapshot, ABI Foster City). Several investigators have reported that the SNapShot reaction provides
an accurate method for determining allele frequency differences between case and control pools [e.g., Norton et al., 2002].
Significant associations suggested by the DNA pooling step
were confirmed by individual genotyping of case and control
samples and within family analysis using parental genotypes
to guard against potential stratification effects.
Samples
The clinical ADHD samples were ascertained from Child and
Adolescent clinics in London and the South East of England.
Cases were referred for assessment if they were thought by
experienced clinicians to have a diagnosis of the combined
subtype of ADHD under DSM-IV criteria, with no significant
Axis I co-morbidity apart from oppositional defiant disorder
(ODD) and conduct disorder (CD). Parents of referred cases
were interviewed with a modified version of the Child and
Adolescent Psychiatric Assessment (CAPA) [Angold et al.,
1995]. Information on ADHD symptoms at school was obtained
using the long form of the Conners questionnaire [Conners,
1995]. Following assessments HYPESCHEME data sheets
were completed using data gathered from the research interview, questionnaire and where necessary review of case notes.
HYPESCHEME is an operational criteria checklist for ADHD
and hyperkinetic disorders that summarises and applies DSM-
IV and ICD-10 operational criteria [Curran et al., 2000].
HYPESCHEME diagnoses were checked against researcher
applied DSM-IV criteria and discrepancies reviewed by two
researchers (PA and SR). Where consensus could not be
reached, cases were brought to case conference and final
consensus agreement made with a senior clinical researcher
(ET). DNA was obtained using cheek swabs as described in
Freeman et al. [2003]. Samples were all of English speaking
Caucasians of European origin. All probands had a research
diagnosis of ADHD combined subtype following the DSM-IV
criteria [APA, 1994]. Exclusion criteria included IQ less than
70, neurological disorders or brain damage and autism. The
sample analyzed here consisted of 180 probands. DNA was
available from both parents for 121 probands and from only
the mother for 64 probands. Ninety-six percent of the probands
were male.
A control sample was generated from ethnically matched
individuals taking part in a population twin sample [Trouton
et al., 2002]. This comprised 90 unrelated females and 90
unrelated males selected for low ADHD symptoms scores
(lower 20%) using a composite index of ADHD symptoms. The
composite index was derived from the average of maternally
rated strength and difficulty questionnaire [SDQ; Goodman,
1997] scores at ages 2,3, and 4 [Price et al., 2001].
DNA Pooling Screen
SNPs were identified from dbSNP (http://www.ncbi.nlm.
nih.gov). We aimed to include all SNPs within the region of
each gene that had minor allele frequencies of 0.05 or above.
In addition we included coding region SNPs and additional
SNPs with unknown minor allele frequencies in regions where
those with known heterozygosity were not listed.
In the first stage of the analysis, SNP markers were screened
for association using a DNA pooling approach. DNA pools
were constructed by mixing equal quantities of DNA quantified to a final concentration of 5 ng/ml (0.5 ng) prior to mixing.
The concentration of each DNA sample was measured using
the PicoGreen dsDNA quantitation reagent (Cambridge
Biosciences) in a Fluorimeter (Thermo Life Sciences). DNA
pools constructed consisted of two case pools (n ¼ 90, n ¼ 90)
and two control pools (n ¼ 90, n ¼ 90). Each genotype assay was
analyzed in triplicate on each pool using the SNaPshotTM
method (ABI, Foster City). Allele frequencies (AF) were estimated from the DNA pool images by averaging across each set
of triplicate data with no adjustment for unequal peak heights
observed in heterozygote samples: AF(allele 1) ¼ PH(allele 1)/
(PH(allele 1) þ PH(allele 2)).
In order to account for both technical error and sampling
error in estimating an appropriate significance value for
observed allele frequency differences between case and control
pools, we adopted a meta-regression method (MRM) for the
analysis of multiple pools [Xu et al., 2005]. For each pool we
TABLE I. SNP Markers That Were Selected for Individual Genotyping
Marker names
Gene
SNP
STX1A
SYP
SYT1
VAMP2
VMAT2
rs1569061
rs2293945
rs1245769
rs316987
rs363225
DNA pooling
Individual genotyping
Estimated
allele frequency
difference
MRM
P-value
Case allele
frequency
Control allele
frequency
Case-control
P-value
T
NT
TDT
P-value
0.09
0.09
0.06
0.05
0.07
0.75
0.03
0.09
0.29
0.49
0.94
0.25
0.68
0.905
0.44
0.92
0.32
0.72
0.904
0.39
0.24
0.09
0.23
0.91
0.31
15
16
41
26
49
6
4
37
19
41
0.05
0.005
0.73
0.30
0.40
DNA pooling, significance values were estimated from DNA pooling data using the meta-regression method (MRM); Case-control, allele counts and
frequencies from case and control pools; TDT, Transmitted (T) and non-transmission (NT) of the putative risk allele from heterozygote parents.
DNA Pooling Analysis of Synaptic Component
derived an estimate of the effect size, the variance of the
estimate and an independent variable relating to the phenotype of the individuals in each pool. Effect size of each pool is
taken to be the average allele frequency over the measurements from the replicate pools. The measurement variance for
each marker (j), within a pool, is calculated using data from the
replicate pools:
n
P
j Þ2
ðpij p
i¼1
2
sj ¼
n1
TABLE II. A Summary of the Findings in This Study
Gene
stx1a
N ¼ 27
with n the number of replicates; pj the average allele freq across
the replicates; pij the allele freq for each replicate. An average
measurement of experimental variance for a marker across the
different pools is calculated. The total variance for each pool is
then calculated:
Variance ¼
j Þ s
j ð1 p
p
^2
þ
n
2y
where pj is average allele frequency; y is number of individuals;
n is number of pools.
SNPs that obtained an MRM significance level <0.1 were
individually genotyped in the case and control samples, plus
parents and siblings of the probands. TDT analysis was
performed using unphased [Dudbridge, 2003].
RESULTS
106 SNPs were screened in the test pool of 50 non-ADHD
subjects to evaluate whether each SNP was sufficiently polymorphic to be used to screen for associations in the experimental pools. A marker was dropped if no second allele was
detectable or if the minor peak was less than 5% of the total
combined peak heights. Although in a few cases this might lead
to discarding a few markers with true MAF greater than 5%
(because we did not take into account peak height ratios from
heterozygote samples), we consider that peak heights less than
5% of the total give inaccurate estimates of allele frequency
differences between pools and cannot confidently be discriminated from background. Of the 106 database SNPs analyzed,
61 SNPs (58%) were found to be sufficiently polymorphic to be
tested in the experimental pools. A summary of the data from
the total set of SNP markers screened for heterozygosity is
available online (Table II).
As expected, the majority of SNPs analyzed showed no
estimated allele frequency differences in either of the DNA pool
comparisons. Out of the total of 61 comparisons made there
were two comparisons that were significant at the P < 0.1
significance level. These two SNPs (SYP- rs2293945; SYT1–
rs1245769) were individually genotyped. An additional three
markers were also selected for individual genotyping that
had estimated allele frequency differences greater than 7%
between case and control pools (STX1-rs1569061, VAMP2rs1061032, VMAT2-rs363225). A summary of the data from
SNPs selected for individual genotyping is listed in Table I.
The three SNPs selected on the basis of allele frequency
differences alone were not significant following case-control
of the individually genotyped data. Unexpectedly, TDT
analysis of one of the markers, rs1569061 in STX1a, showed
nominal significance (P ¼ 0.04). This finding was however nonsignificant when adjusted for the number of tests performed
and is therefore likely to represent a type I error.
Of the two markers selected on the basis of an estimated
MRM P-value <0.1, only one remained significant following
case-control and TDT analysis of the individually genotyped
data. SNP rs2293945 in SYP showed an estimated 8% difference in allele frequency between the proband and control pools
with MRM P-value ¼ 0.03. Individual genotyping confirmed
the proband control association with a trend for significance
35
vamp2
N ¼ 17
syt1
N ¼ 13
syp
N ¼ 19
SNP
reference
rs2293485
rs2091449
rs1569061
rs1049738
rs941299
rs941298
rs875342
rs867500
rs3830638
rs3793243
rs2030921
rs2030922
rs4504524
rs4582436
rs4717806
rs4363087
rs7807316
rs7793506
rs7793508
rs4717102
rs5884917
rs7776771
rs6460051
rs6951030
rs6956879
rs7790069
rs7794407
rs3208049
rs316987/1061032
rs1061089
rs930497
rs2278637
rs959303
rs1051498
rs1150
rs3178041
rs1051667
rs1527593
rs2304909
rs1051780
rs8079444
rs8066511
rs8066513
rs8067606
rs2037743
rs1918188
rs1918191
rs1465054
rs1465053
rs1245815
rs1245819
rs941133
rs1526958
rs1245769
rs7972950
rs1245816
rs1245820
rs1049749
rs7889267
rs2737731
rs2856747
rs2737732
rs3841666
rs6417804
rs7055023
rs3817678
MAF
NP
NP
0.055
NP
0.405
NP
NP
NP
NP
NP
NP
MAF
according
to pooling
0.49
—
0.17
—
0.25
0.32
0.13
—
0.19
0.44
0.26
—
0.11
—
0.2
0.43
0.02
—
—
—
—
0.39
0.36
0.28
0.49
0.03
NP
0.03
0.20
0.01
NP
0.465
0.466
NP
NP
NP
0.45
0.27
0.17
—
0.02
NP
NP
NP
NP
NP
NP
0.32
0.393
0.388
0.259
0.03
NP
0.298
0.05
NP
0.495
NP
0.354
NP
0.15
0.42
0.32
0.21
0.1
0.3
0.24
0.1
0.03
0.35
0.43
NP
NP
0.04
0.27
0.5
NP
NP
Casecontrol
ns
ns
ns
—
ns
ns
ns
—
ns
ns
ns
—
ns
—
ns
ns
ns
—
—
—
—
ns
ns
ns
ns
ns
—
ns
ns
ns
—
ns
ns
—
ns
ns
—
ns
—
—
—
—
—
—
ns
ns
ns
ns
ns
—
ns
ns
—
0.09
ns
—
ns
—
ns
—
—
ns
ns
ns
—
—
(Continued )
36
Brookes et al.
TABLE II. (Continued )
Gene
vmat2
N ¼ 17
SNP
reference
rs2293945
rs7060540
rs6417805
rs6520404
rs7061766
rs5906752
rs5905721
rs5905722
rs5905723
rs2737733
rs363390
rs363333
rs363397
rs363342
rs363343
rs929493
rs2283140
rs363225
rs363272
rs363231
rs363236
rs2619094
rs1042543
rs3026051
rs363387
rs363388
rs363411
MAF
MAF
according
to pooling
0.32
NP
0.46
0.34
NP
0.18
0.35
0.31
NP
NP
0.12
0.07
NP
0.23
0.473
0.021
NP
NP
NP
0.08
NP
0.144
0.39
0.15
0.24
0.48
0.16
0.07
0.35
0.12
Casecontrol
0.034
—
ns
ns
—
ns
ns
ns
—
—
ns
ns
—
BA
ns
ns
ns
0.208
ns
ns
ns
—
—
—
0.539
—
BA
MAF, reported (database) minor allele frequency; MAF-pool, estimated
minor allele frequency from test pools; NP, non-polymorphic markers; ns,
non-significant MTR P-value; BA, bad assay.
(P-value ¼ 0.09). TDT analysis provided increased evidence
for the association with a P-value of <0.01.
DISCUSSION
In summary we used a DNA pooling approach to screen 61
SNPs from a selection of 106 database SNPs for association
between ADHD and six genes involved in the mechanisms of
vesicular release of neurotransmitters. In the first stage of the
analysis we discarded 45 SNPs where the allele peaks was less
than 5% of the combined peak heights. From the remaining
61 SNPs that were used in the DNA pooling comparisons of
proband versus control we identified only two markers that
generated estimated significance values <0.1. Only SNP
rs2293945 in SYP showed overall evidence for association by
passing each stage of the analysis. The level of significance for
this marker following individual genotyping was however
only 0.09 that would be expected to occur by chance alone more
than four times in this study. On the other hand, a greater
level of significance was obtained following TDT analysis of
0.01 that would occur less than once by chance alone with the
number of SNPs analyzed.
The syp gene is located on the X chromosome, with the SNP
rs2293945 located within the 50 UTR, a region known to possess
regulatory properties. The fact this is an X chromosome gene
might be of interest since ADHD occurs more commonly in
males than females [Ford et al., 2003]. The reasons for this are
unknown although quantitative genetic data suggests that
the same genetic risks apply equally to males and females;
however the mean for ADHD symptoms in the population is
lower for females than males suggesting a possible twothreshold model. X chromosome genes may play a role since
females who are heterozygote for a risk allele will on average
have a lower risk for ADHD. The second X chromosome may
also protect females from the effects of an associated polymorphism since genes can escape or show only partial Xinactivation. Males only have one X chromosome therefore
would not be as protected. We did not have sufficient females
in this sample to explore the strength of this association in
females.
Limitations of this study include small sample size with
80% power to detect odds ratios of around 1.5, depending on
minor allele frequency. There was a tendency for initial
estimated significance values to be inflated compared to
individual genotyping of markers. This is most likely due
minor differences in quantification of individual DNA samples
prior to mixing into pooled samples and demonstrates the
critical importance of the initial quantification step in the
first phase of the experiment. The use of TDT alongside casecontrol analysis guards against the possibility that observed
differences are due solely to population stratification effects
and can in some cases identify SNP associations that are
significant when controlled for in this way. Our data show that
the meta-regression method of analysis can correctly identify
significant allele associations in case-control pooling screens.
ACKNOWLEDGMENTS
We thank all families involved in this research for their
participation.
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