No significant association of 14 candidate genes with schizophrenia in a large European ancestry sample: implications for psychiatric genetics.

Abstract

The authors carried out a genetic association study of 14 schizophrenia candidate genes (RGS4, DISC1, DTNBP1, STX7, TAAR6, PPP3CC, NRG1, DRD2, HTR2A, DAOA, AKT1, CHRNA7, COMT, and ARVCF). This study tested the hypothesis of association of schizophrenia with common single nucleotide polymorphisms (SNPs) in these genes using the largest sample to date that has been collected with uniform clinical methods and the most comprehensive set of SNPs in each gene.
The sample included 1,870 cases (schizophrenia and schizoaffective disorder) and 2,002 screened comparison subjects (i.e. controls), all of European ancestry, with ancestral outliers excluded based on analysis of ancestry-informative markers. The authors genotyped 789 SNPs, including tags for most common SNPs in each gene, SNPs previously reported as associated, and SNPs located in functional domains of genes such as promoters, coding exons (including nonsynonymous SNPs), 3' untranslated regions, and conserved noncoding sequences. After extensive data cleaning, 648 SNPs were analyzed for association of single SNPs and of haplotypes.
Neither experiment-wide nor gene-wide statistical significance was observed in the primary single-SNP analyses or in secondary analyses of haplotypes or of imputed genotypes for additional common HapMap SNPs. Results in SNPs previously reported as associated with schizophrenia were consistent with chance expectation, and four functional polymorphisms in COMT, DRD2, and HTR2A did not produce nominally significant evidence to support previous evidence for association.
It is unlikely that common SNPs in these genes account for a substantial proportion of the genetic risk for schizophrenia, although small effects cannot be ruled out.

Full text

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AJP In Advance
1
Article
ajp.psychiatryonline.org
No Significant Association of 14 Candidate Genes With
Schizophrenia in a Large European Ancestry Sample:
Implications for Psychiatric Genetics
Alan R. Sanders, M.D.
Jubao Duan, Ph.D.
Douglas F. Levinson, M.D.
Jianxin Shi, Ph.D.
Deli He, B.S.
Cuiping Hou, B.S.
Gregory J. Burrell, B.S.
John P. Rice, Ph.D.
Deborah A. Nertney, B.S.
Ann Olincy, M.D.
Pablo Rozic, M.D.
Sophia Vinogradov, M.D.
Nancy G. Buccola, A.P.R.N.B.C.
Bryan J. Mowry, M.D.
Robert Freedman, M.D.
Farooq Amin, M.D.
Donald W. Black, M.D.
Jeremy M. Silverman, Ph.D.
William F. Byerley, M.D.
Raymond R. Crowe, M.D.
C. Robert Cloninger, M.D.
Maria Martinez, Ph.D.
Pablo V. Gejman, M.D.
Objective: The authors carried out a ge-
netic association study of 14 schizophre-
nia candidate genes (RGS4, DISC1,
DTNBP1, STX7, TAAR6, PPP3CC, NRG1,
DRD2, HTR2A, DAOA, AKT1, CHRNA7,
COMT, and ARVCF). This study tested the
hypothesis of association of schizophre-
nia with common single nucleotide poly-
morphisms (SNPs) in these genes using
the largest sample to date that has been
collected with uniform clinical methods
and the most comprehensive set of SNPs
in each gene.
Method: The sample included 1,870
cases (schizophrenia and schizoaffective
disorder) and 2,002 screened comparison
subjects (i.e. controls), all of European an-
cestry, with ancestral outliers excluded
based on analysis of ancestry-informative
markers. The authors genotyped 789
SNPs, including tags for most common
SNPs in each gene, SNPs previously re-
ported as associated, and SNPs located in
functional domains of genes such as pro-
moters, coding exons (including nonsyn-
onymous SNPs), 3′ untranslated regions,
and conserved noncoding sequences. Af-
ter extensive data cleaning, 648 SNPs
were analyzed for association of single
SNPs and of haplotypes.
Results: Neither experiment-wide nor
gene-wide statistical significance was ob-
served in the primary single-SNP analyses
or in secondary analyses of haplotypes or
of imputed genotypes for additional com-
mon HapMap SNPs. Results in SNPs previ-
ously reported as associated with schizo-
phrenia were consistent with chance
expectation, and four functional poly-
morphisms in COMT, DRD2, and HTR2A
did not produce nominally significant evi-
dence to support previous evidence for
association.
Conclusions: It is unlikely that common
SNPs in these genes account for a sub-
stantial proportion of the genetic risk for
schizophrenia, although small effects can-
not be ruled out.
(Am J Psychiatry Sanders et al.; AiA:1–10)
T he intensive search for DNA sequence variation un-
derlying susceptibility to schizophrenia has been moti-
vated by evidence that etiology is predominantly genetic:
heritability is ~80% based on twin studies (1), with over-
lapping risks of schizophrenia and schizoaffective disor-
der in families and a pattern of illness in families that sug-
gests complex mechanisms involving multiple genes of
small effect (2, 3). Currently, the genetic mechanisms re-
main unknown.
A decade ago, genes involved in monoaminergic neu-
rotransmission were the most widely studied schizophre-
nia “candidate genes” because drugs that blocked dopa-
mine receptors were the best available treatments. A new
set of mostly “positional” candidate genes has now
emerged—disease-related genes identified by their loca-
tion in relation to DNA markers or cytogenetic abnormali-
ties (4, 5). These genes are involved in pathways that can
plausibly be related to mechanistic hypotheses of schizo-
phrenia. We present here a study of the association of
schizophrenia to DNA sequence variants in 14 of the best-
supported of these current candidate genes selected on
the basis of our reading of the literature; others might cre-
AJP in Advance. Published January 15, 2008 (doi: 10.1176/appi.ajp.2007.07101573)

Copyright © 2008 American Psychiatric Association. All rights reserved.

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ASSOCIATION OF GENES WITH SCHIZOPHRENIA
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ate a slightly different list, but these genes would be given
consideration by most investigators.
The study has two important features. First, the large
sample was collected by uniform methods, whereas most
schizophrenia samples are smaller or were assembled from
separate studies. We studied subjects of European ancestry
(the larger of the two ancestry groups in our sample) be-
cause most previous support for these associations has
come from this population, and findings can be con-
founded by the varying frequencies of many DNA sequence
variants across populations. Second, we tested dense sets of
single nucleotide polymorphisms (SNPs) in each gene
(rather than a few), including “tags” for most known com-
mon SNPs, plus additional SNPs in critical gene elements
such as those that change amino acid sequence.
The selected genes include the following: RGS4, DISC1,
DTNBP1, STX7, TAAR6, PPP3CC, NRG1, DRD2, HTR2A,
DAOA, AKT1, CHRNA7, COMT, and ARVCF (data supple-
ment Table 2 available at http://ajp.psychiatryonline.org).
The strength of previous evidence for association varies
among these genes (6, 7). Generally, one or more studies
reported an experiment-wide significant result, but no
finding has been consistently observed.
The interpretation of genetic association studies de-
pends on the hypothesis. The reported associations in
these genes are generally for “common” SNPs (typically de-
fined as those present on at least 5% of chromosomes). Al-
though we cannot yet test all rare and common DNA varia-
tion by direct sequencing, we can systematically study
common SNPs because the HapMap project (hapmap.org)
has catalogued a large proportion of common SNPs ge-
nome-wide and shown how to “tag” them with subsets and
because high-throughput technologies can now test them
accurately (8). We agree with Todd (9) that to firmly estab-
lish a finding of association with a common SNP, one
should observe evidence across studies that clearly ex-
ceeds a statistical threshold (probably near p=10–7 for tests
of single SNPs [10]) that takes into account all common
variation in the genome. (Note that very low p values are
often reported for combinations of SNP alleles [haplo-
types], but testing haplotypes requires many more statisti-
cal tests and therefore an even more stringent threshold.)
None of these 14 genes has produced association evidence
at this level in a single study or across studies.
We tested these genes in a large sample using SNPs that
tagged most common variants plus SNPs previously re-
ported as associated and additional known SNPs in func-
tional elements. While recognizing that ultimately more
stringent statistical thresholds must be achieved to ac-
count for testing SNPs throughout the genome with a low
prior probability of association for any one SNP and given
the low prior probability of any single candidate gene as-
sociation being “true,” in the context of the absence of
well-established pathophysiological hypotheses for
schizophrenia, we have applied two empirically derived
criteria of significance: one that accounts for all tests in
this experiment (considered the primary criterion here)
and one that accounts for all tests in each gene. A gene-
wide threshold would be most appropriate for an associa-
tion that had been rigorously established by previous
studies. This does not appear to be the case, but given that
this is the first large-scale systematic study of most of
these genes, it is important to avoid false negative as well
as false positive results. We have also considered whether
in SNPs or haplotypes with previous experiment-wide evi-
dence for association we observed nominally significant
results more frequently than expected by chance. We were
unable to detect association of any one SNP with schizo-
phrenia by any of these criteria. The implications of these
findings are further discussed below.
Method
Complete details about the method and results are available in
the data supplement (text, tables, figures). We provide here a
summary of the most pertinent information.
Subjects
The study sample included 1,952 unrelated individuals with a
diagnosis of schizophrenia or schizoaffective disorder and 2,126
comparison subjects. After the quality control checks described
below, 1,870 case and 2,002 comparison subjects were included in
analyses.
Cases were recruited in three related studies (Table 1). Most
were recruited by the Molecular Genetics of Schizophrenia Part 2
study. The present investigators are currently completing the re-
cruitment of this large case-control sample of European ancestry
and African American individuals for genetic association studies
of schizophrenia, which is part of an NIMH repository program
(nimhgenetics.org). The present study includes approximately
two-thirds of the Molecular Genetics of Schizophrenia Part 2 Eu-
ropean ancestry sample. The remaining subjects are from the
Molecular Genetics of Schizophrenia Part 1 (11) and Schizophre-
nia Genetics Initiative (12, 13) studies of multiply affected pedi-
grees, with one case from each eligible family included here. Ap-
proximately 20% of cases had a first- or second-degree relative
with a known or suspected history of schizophrenia. Recruitment
sites are listed in data supplement Table 3. These subjects of Eu-
ropean ancestry (by self-report) included some cases from Aus-
tralia, where European ancestry is similar to the United States
(14). Cases (ages 18 and over) were identified from clinics, hospi-
tals, physician referrals, advocacy and support organizations, and
Internet and media announcements and advertisements.
All case subjects signed institutional review board-approved
written informed consent forms that authorized deposition of
their biological materials and nonidentifying clinical information
in NIMH repository for use in genetic studies. Cases were inter-
viewed by trained clinicians with the Diagnostic Interview for Ge-
netic Studies 2.0 (15) to elicit DSM-IV diagnostic and symptom in-
formation for psychotic, mood, and substance use disorders. In
98.6% of the cases, two of three possible types of information were
obtained: Diagnostic Interview for Genetic Studies, Family Inter-
view for Genetic Studies interview with an informant (16), and
psychiatric records; 26 cases who could not be meaningfully inter-
viewed were diagnosed with high confidence by means of psychi-
atric records alone. Two senior clinicians independently reviewed
all information and then assigned a primary consensus best-esti-
mate final diagnosis (17) and comorbid diagnoses. Eligible cases
received a “definite” or “likely” consensus best-estimate final diag-

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SANDERS, DUAN, LEVINSON, ET AL.
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nosis of schizophrenia or schizoaffective disorder, with psychosis
judged unlikely to have been caused by substance use or medical
illness and without moderate or severe mental retardation.
Blood specimens for U.S. participants were shipped overnight
to the Rutgers University Cell and DNA Repository for transfor-
mation to lymphoblastic cell lines and DNA extraction; in Austra-
lia, lymphoblastic cell lines were established at Queensland Insti-
tute for Medical Research and aliquots shipped to Rutgers.
A marketing research company, Knowledge Networks (Menlo
Park, Calif.), recruited the comparison subjects (Molecular Ge-
netics of Schizophrenia Part 2). Knowledge Network’s national
online participant panel, recruited by random-digit dialing of res-
idential phone numbers, is demographically similar to the U.S.
population (age, sex, education, metropolitan/nonmetropolitan
residence) (data supplement Table 4). A member of approxi-
mately 30% of targeted households joined the panel. Those with-
out Internet access were given a web TV. Approximately 60,000 in-
dividuals of European ancestry were in the panel at some point
during Molecular Genetics of Schizophrenia Part 2 recruitment;
15,485 were randomly selected, sent a letter explaining the study,
then sent an e-mail message pointing to a web site to learn more
about the study, given preliminary online informed consent, and
completed a self-report clinical assessment; 3,364 (21.7%) com-
pleted these procedures and gave a blood sample, collected by
Examination Management Services Inc. (Irving, Tex.), which also
obtained written informed consent authorizing NIMH to use the
biological materials and clinical information for any medical re-
search study. We anonymized the comparison sample by destroy-
ing any hard copy materials (e.g., written informed consent
forms) or computer files with links between identification num-
bers and personal identifiers.
The online assessment included the Composite International
Diagonostic Interview—Short Form (18), modified for lifetime
common mood, anxiety, and substance use disorders; items for
lifetime diagnosis or treatment of psychosis or bipolar disorder; a
nicotine dependence screen; neuroticism and extraversion per-
sonality scales (19); and items for sexual orientation, current
height and weight, highest lifetime weight, and ancestral back-
ground, plus previously collected demographic information. We
excluded 9.4% of the comparison subjects (0.4% endorsed more
than 50 of 69 screening or personality items; 0.4% failed to answer
five or more of these questions; 0.6% were not fully screened ow-
ing to software failures; and 8% endorsed or failed to deny previ-
ous treatment or diagnosis of schizophrenia, schizoaffective dis-
order, auditory hallucinations, delusions, or bipolar disorder).
Self-Reported Ancestry
Cases reported up to four ancestries for each parent, and com-
parison subjects reported ancestries for each grandparent. We ex-
cluded cases mentioning non-European ancestry (except partial
Native American ancestry, which was overreported). Reported
ancestries were similar for cases and comparison subjects (data
supplement Figure 1).
SNP Selection, Genotyping, and Quality Control
We genotyped 224 ancestry-informative SNP markers (SNPlex
genotyping system, Applied Biosystems, Foster City, Calif.)
reported to differentiate European ancestry from African, Amerin-
dian, or Asian ancestries (20–22), including rs4988235 (located ~14
kilobases upstream from lactase and associated with lactase per-
sistence), whose frequency varies north-south across Europe (23).
We genotyped 789 SNPs (756 by SNPlex only, 20 by TaqMan
[Applied Biosystems] only, and 13 by both methods) in 14 candi-
date gene regions (2.38 Mb of sequence), including SNPs previ-
ously reported as associated with schizophrenia, SNPs tagging
most known common variation (based on HapMap I when this
study was planned), and additional SNPs in putative functional
gene elements, i.e., promoters, coding exons, 3′ untranslated re-
gions, and conserved noncoding sequences (Table 2).
We excluded 164 SNPs (30 ancestry-informative SNP markers,
134 in candidate genes) whose genotypes were called in less than
90% of samples or had inconsistent clustering by inspection, in-
cluding seven monomorphic candidate gene SNPs and nine chro-
mosome X ancestry-informative SNP markers. Five candidate
gene SNPs showed departures from Hardy-Weinberg equilibrium
(p<0.0001 by exact statistics) (PLINK [24]); none of these five SNPs
showed evidence for association. Valid SNPs included 185 autoso-
mal ancestry-informative SNP markers and 648 SNPs for associa-
tion tests of candidate genes (including 433 tag SNPs to assess
common variation). Pair-wise tagging analysis (Tagger [25]) at an
r2 threshold of 0.8 showed that candidate gene SNPs captured
94% of HapMap I common variants (minor allele frequency>0.05)
(range=84%–100% for individual genes) and 83% for HapMap II
(range=62%–93%) (data supplement Figure 2).
Sample Quality Control and Ancestry Analyses
Of genotyped cases (1,952) and comparison subjects (2,126)
(data supplement Table 3), we excluded 12 cases and 51 compar-
ison subjects with aggregate genotype call rates less than 95%
across all valid SNPs, two cases and 23 comparison subjects with
unresolved sex typing (amelogenin) discrepancies, five case and
two comparison samples that were duplicates of another sample,
13 comparison subjects who were apparently related to another
TABLE 1. Characteristics of the Case Samplea
Study
NIMH Schizophrenia
Genetics Initiative
Molecular Genetics of
Schizophrenia Part 1
Molecular Genetics of
Schizophrenia Part 2
Total cases
aShown are the numbers of cases (after all data cleaning and ancestry exclusions) subdivided by sex and diagnosis and by the recruiting study.
All cases were of self-reported European ancestry and clustered with other individuals of European ancestry in analysis of ancestry-informa-
tive SNP markers (see text). The NIMH Schizophrenia Genetics Initiative (12) and the Molecular Genetics of Schizophrenia Part 1 (11) studies
recruited multiply affected pedigrees for linkage analysis; one case per eligible family has been included here. There was a small overlap be-
tween the NIMH Schizophrenia Genetics Initiative sample (accounted for 3% of the cases analyzed here) and the samples in which associa-
tions were first reported in samples of European ancestry for five of these 14 genes, although this did not affect the results (see data supple-
ment). The Molecular Genetics of Schizophrenia Part 2 study recruited unrelated cases and comparison subjects (see text).
CasesMaleSchizophrenia
Schizoaffective Disor-
der, Manic Type
Schizoaffective Disor-
der, Depressed Type
N%N
% by
StudyN Study %N Study %N Study %
56 3.0 42 75.056 100.000.00 0.0
239 12.8 16870.3239 100.00 0.00 0.0
1,575
1,870
84.2
100.0
1,088
1,298
69.1
69.4
1,383
1,678
87.8
89.7
121
121
7.7
6.5
71
71
4.5
3.8

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ASSOCIATION OF GENES WITH SCHIZOPHRENIA
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TABLE 2. Single Nucleotide Polymorphism (SNP) Coverage of 14 Schizophrenia Candidate Genesa
Gene Symbol
RGS4
DISC1
DTNBP1
STX7
TAAR6
PPP3CC
NRG1
DRD2
HTR2A
DAOA
AKT1
CHRNA7
COMT
ARVCF
Total
aGene symbols are from the Human Genome Organisation (HUGO) Gene Nomenclature Committee. Sizes (in kilobases) are quoted from Ref-
Seq. Tagged gene length includes an additional 2 kilobases on each side of each gene plus splice variants extending beyond RefSeq bound-
aries. Isoform GGF2 was used for NRG1. For DAOA, part of overlapping G30 gene was tagged. Sizes and numbers of exons are derived from
RefSeq, University of California at Santa Cruz, AceView, and Visualization Tool for Alignment, taking into account alternative splicing and par-
tial overlaps among classes of gene segments (so that the total of the lengths of single elements is slightly longer than the tagged gene
length). The number of SNPs in the nonsynonymous column are a subset of the number of SNPs in the exon column. Exon refers to the trans-
lated region. Nonsynonymous means change in coded amino acid sequence. Previous association=previously reported as associated with
schizophrenia (these SNPs are also counted in the column for each gene domain). “Cleaned SNPs”=SNPs passing all quality control filters. All
of the 70 SNPs previously reported to be associated with schizophrenia were successfully genotyped except rs4262285 in NRG1. Of the
attempted 789 SNPs, the genotyping failures were distributed among the genes roughly proportional to the number of SNPs attempted for
each gene (related most strongly to gene length), with lower minor allele frequency SNPs and less well-validated SNPs being overrepresented
in the failures.
Length of Kilobases
Number of
ExonsRefSeq geneTagged gene
12
421
144
62
PromotersExon
2
14
3
3
1
3
8
3
2
4
4
2
9
8
65
Intron
Conserved
Noncoding
Sequence
—
7.5
0.1
—
—
—
13.3
1.1
0.8
0.5
0.1
0.5
–
0.1
24.1
Untranslated
Region
2.2
—
0.2
6
—
0.3
3.6
0.8
3.3
–
1.0
4.6
0.2
4.6
26.8
76
6
8
6
2
6
45
414
140
53
395
137
50
—
97
1103
61
60
36
22
131
19
38
2,151
20
16
10
151
100
1104
66
63
25
26
139
27
47
2,212
104
1132
70
69
44
30
142
32
54
2,323
16
1916
8
4
6
6
4
18
12
9
4
13
11
10
16
21
171 108
TABLE 3. Single SNP Association Tests With Empirical Pointwise p<0.05a
Gene
RGS4
RGS4
DISC1
DISC1
DISC1
DISC1
DISC1
STX7
STX7
STX7
STX7
TAAR6
NRG1
NRG1
NRG1
NRG1
NRG1
DRD2
DRD2
DRD2
HTR2A
HTR2A
HTR2A
DAOA
DAOA
AKT1
CHRNA7
CHRNA7
COMT
ARVCF
aShown are all the 30 SNPs with an empirical pointwise p<0.05 value (Armitage trend test; p<0.01 bolded). Nominal pointwise and empirical
gene-wide p values are also shown for this test, as well as for the classical allelic χ2 test and for the EIGENSTRAT χ2 test that corrects for popu-
lation substructure. Carets indicate tag SNPs, and asterisks indicate SNPs that have previously been reported as associated with schizophrenia.
SNPPosition
159,771,435
159,772,153
228,191,952
228,234,115
228,247,572
228,279,546
228,361,984
132,823,032
132,824,835
132,826,709
132,877,343
132,934,025
32,524,438
32,525,690
32,526,536
32,549,006
32,623,943
112,822,277
112,823,217
112,834,984
46,362,858
46,367,941
46,369,479
104,922,458
104,925,835
104,316,296
30,189,338
30,198,685
18,304,105
18,352,201
AlleleMinor Allele Frequency
Minor/Major
A/G
G/T
G/A
G/C
G/A
G/C
C/T
A/G
T/C
C/A
T/C
A/G
A/G
A/C
C/T
C/T
A/G
A/G
C/T
A/C
G/A
T/C
T/C
G/C
T/C
C/T
A/G
C/A
A/G
A/G
Associated
G
T
G
G
G
C
T
G
C
A
C
G
G
C
T
T
A
G
T
A
G
T
T
G
T
T
G
A
A
G
Cases
0.470
0.420
0.198
0.099
0.101
0.199
0.400
0.291
0.087
0.086
0.077
0.066
0.439
0.440
0.440
0.433
0.134
0.297
0.461
0.452
0.425
0.425
0.425
0.193
0.012
0.468
0.347
0.144
0.492
0.318
Comparison
Subjects
0.493
0.443
0.179
0.083
0.085
0.217
0.423
0.321
0.105
0.103
0.093
0.079
0.466
0.466
0.468
0.463
0.118
0.322
0.484
0.424
0.402
0.400
0.400
0.175
0.007
0.491
0.370
0.161
0.468
0.344
rs2661319*^
rs2842030
rs10864695^
rs9431997^
rs9432010
rs17768115^
rs2038636^
rs3183732^
rs12207033^
rs3757298
rs4470875^
rs8192625*^
rs3802158^
rs7834206
rs4733130
rs2439305
rs7825588^
rs17529477^
rs4245147
rs7131056^
rs4941573^
rs6313*
rs6311*
rs1539070^
rs1557072^
rs2498794^
rs10438342^
rs2221223^
rs3788319^
rs2012714^

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Number of Cleaned SNPs Analyzed
Conserved
Noncoding
Sequence
0
33
1
0
0
0
62
6
6
3
1
3
0
1
116
Exon
Non-
synonymous
0
3
3
1
7
0
4
2
5
3
0
1
1
3
33
Untranslated
Region
1
0
1
4
0
0
1
4
2
0
0
0
0
2
15
Promoter
Intron
Previous
Association
Number of
SNPs
12
115
38
27
17
21
217
32
34
30
17
18
29
41
648
Proportion
0.02
0.18
0.06
0.04
0.03
0.03
0.33
0.05
0.05
0.05
0.03
0.03
0.04
0.06
1.00
43
2
4
5
3
2
7
5
2
2
2
2
44
21
15
10
7
13
17
11
8
17
11
1
18
21
59
17
8
7
6
14
12
2
4
4
11
2
2
6
2
1
3
2
69
130
6
16
8
3
12
1
9
10
8
57174286
Odds Ratio
1.10
1.10
1.13
1.22
1.21
1.12
1.10
1.15
1.23
1.22
1.22
1.22
1.12
1.11
1.12
1.13
1.15
1.12
1.10
1.12
1.10
1.11
1.11
1.13
1.84
1.10
1.10
1.14
1.10
1.13
Armitage Trend Test p Values
Pointwise Allelic χ2
0.05
0.05
0.04
0.01
0.02
0.05
0.04
0.00
0.01
0.01
0.02
0.03
0.02
0.02
0.02
0.01
0.05
0.02
0.05
0.01
0.04
0.03
0.03
0.05
0.01
0.04
0.04
0.04
0.04
0.02
EIGENSTRAT χ2
0.05
0.05
0.02
0.03
0.03
0.08
0.02
0.00
0.01
0.01
0.02
0.03
0.03
0.03
0.02
0.01
0.07
0.01
0.04
0.01
0.07
0.05
0.05
0.04
0.01
0.03
0.05
0.05
0.03
0.02
Nominal
Pointwise
<0.05
<0.05
<0.04
<0.02
<0.02
<0.05
<0.04
0.004
0.009
<0.02
<0.02
<0.03
<0.02
<0.02
<0.02
0.008
<0.05
<0.02
<0.05
<0.02
<0.05
<0.03
<0.04
0.051
<0.02
<0.05
<0.05
<0.05
<0.05
<0.03
Empirical
Pointwise
<0.05
<0.05
<0.04
<0.02
<0.02
<0.05
<0.04
0.004
0.009
<0.02
<0.02
0.026
<0.02
0.02
<0.02
0.008
<0.05
<0.02
<0.05
<0.02
<0.05
<0.03
<0.04
<0.05
<0.02
<0.05
<0.05
<0.05
<0.05
<0.02
Gene-Wide
0.26
0.23
0.87
0.58
0.69
0.94
0.91
0.051
0.11
0.15
0.18
0.28
0.83
0.86
0.77
0.60
0.98
0.24
0.47
0.18
0.54
0.41
0.44
0.53
0.18
0.32
0.42
0.41
0.47
0.26

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comparison subject, and 63 case and 35 comparison subject
specimens that lay outside the European ancestry cluster in a
principle components analysis of ancestry-informative SNP
marker data (EIGENSTRAT [26]). Thus, 1,870 case and 2,002 com-
parison samples were available for association analyses. The
groups were well matched for the first two EIGENSTRAT principal
component scores (data supplement Figure 3). The first score was
correlated (r=0.87) with rs4988235 (lactase persistence) geno-
types (data supplement Figure 4), presumably reflecting north-
south European genetic variation (23).
Association Tests for Single SNPs
Armitage trend tests of association were computed (PLINK) for
each of 648 SNPs. For comparison, we also computed classical χ2
tests of allele counts in cases versus comparison subjects and as-
sociation tests (EIGENSTRAT) that controlled for possible differ-
ential ancestry between cases and comparison subjects (the
squared correlation between the ancestry-adjusted genotypes at
the tested SNP and the ancestry-adjusted phenotypes under the
null hypothesis of no association, following a χ2 distribution with
1° of freedom [26]). Empirical significance of single-SNP Armitage
tests was estimated by permuting phenotype status to generate
100,000 data sets of all 648 SNPs under the null hypothesis of no
association. Experiment-wide and gene-wide empirical signifi-
cance were defined, respectively, as the probability of observing
at least one SNP in the experiment or in its gene with an Armitage
trend test at least as large as the observed one. Empirical signifi-
cance tests are necessary here to correct for the correlations in as-
sociation tests for SNPs, which are in linkage disequilibrium with
each other (data supplement Figures 5–16); i.e., there are pairs of
alleles at nearby SNPs that are usually found on the same chro-
mosomes because of their evolutionary history.
Haplotypic Analyses
We carried out tests of combinations of SNPs (haplotypes)
(data supplement Tables 5–7), including haplotypes that had pre-
viously been reported as associated with schizophrenia and addi-
tional exploratory analyses with UNPHASED (27) to compute a
global p value accounting for all possible haplotypes (with a fre-
quency of 3% or greater) for each set of SNPs and PLINK to com-
pute a p value and odds ratio for the most associated haplotype.
Exploratory analyses included “sliding windows” of two and three
SNPs and also an “anchored” stepwise procedure, starting with
SNPs with nominal pointwise (p<0.05) association and then
searching for two- and three-SNP combinations within each gene
with greater evidence for association. Empirical p values were de-
termined for these tests by permutation if the nominal global p
value was less than 10–3.
Imputation of Nongenotyped HapMap SNPs
As an additional exploratory analysis, genotypes were imputed
for all ungenotyped HapMap II SNPs in candidate gene regions (if
present in at least three HapMap CEPH Utah chromosomes) using
MACH 1.0 (www.sph.umich.edu/csg/abecasis/MACH/). MACH
uses Markov chain models to infer the probability of each possible
genotype of an SNP in each subject based on a training data set
(haplotyped CEPH Utah HapMap II data). Score tests were used to
test allelic association with the sums of these probabilities in cases
versus comparison subjects. Imputed data can suggest additional
SNPs that merit genotyping for more precise association tests (28).
Power Analyses
Data supplement Table 8 (all SNPs) and Table 9 (tag SNPs)
show the power of this sample to detect experiment-wide empir-
ical significance across a range of genetic models, assuming 1%
disease prevalence and weak or strong linkage disequilibrium be-
tween the true susceptibility variant and the associated SNP. Data
supplement Table 10 shows the minimum genotypic relative risk
at which empirical gene-wide significance can be detected with
80% power for each gene. Genotypic relative risk is the increase in
risk produced by carrying one risk allele for dominant or multipli-
cative models or two risk alleles for recessive models. The sample
has excellent power to detect gene-wide significant association
for genotypic relative risk values of 1.25–1.50 in the presence of
strong linkage disequilibrium or 1.3–1.7 SNP with weak linkage
disequilibrium, except for less common alleles with recessive ef-
fects (a well-known limitation of case-control studies).
Results
Association Analyses for Single SNPs
The results are shown in Table 3 and Table 4, Figure 1,
and data supplement Table 11. Pointwise empirical p<0.05
values (expected 5% of the time by chance) were observed
in 4.6% of Armitage trend tests for all SNPs (30 of 648 tests)
and 4.8% for tag SNPs (21 of 433 tests); and p<0.01 values
(expected 1% of the time by chance) were observed in
0.5% of tests (three of 648 and two of 433, respectively),
with the lowest value observed in tag SNP rs3183732 in
STX7 (empirical pointwise p=0.004). No SNP achieved em-
pirical experiment-wide significance. Thresholds for the
5% significance level were nominal p<0.00008 for all SNPs
or p<0.0002 if the analysis was limited to tag SNPs only;
thresholds for “suggestive” association (i.e., expected once
per experiment of this size) were p<0.002 or p<0.003, re-
spectively. One SNP (rs3183732) in STX7 showed gene-
wide significance based only on tag SNPs (empirical gene-
wide p<0.05) but not based on all SNPs (gene-wide p=
0.051), a marginal result for an SNP for which association
has not been previously reported. Exclusion of schizoaf-
fective disorder cases did not alter these conclusions.
Analyses using a correction for the possible effects of case-
control ancestry differences (EIGENSTRAT) produced the
same results. Tests of haplotypes and of imputed geno-
types for additional HapMap common SNPs did not pro-
duce additional positive findings (data supplement Tables
5–7 and 12–13, data supplement Figure 17).
Figure 2 shows the quantile-quantile distribution of ob-
served versus expected p values for tag SNPs. A straight
line indicates good fit of a theoretically uniform distribu-
tion. There is a small departure below the null line, within
the 95% confidence interval (perhaps reflecting modest
linkage disequilibrium among the tag SNPs), consistent
with a lack of evidence for association.
Our tests of the 70 SNPs, chosen because of previous
positive reports of association with schizophrenia, can be
viewed as a particularly interesting subset of tests (al-
though not clearly as “replication” tests because previous
evidence for association in these genes did not achieve a
very strong threshold of significance). Of these, 69 (all ex-
cept rs4262285 in NRG1) were successfully genotyped, and
four (5.8%) produced p<0.05 values (Table 3), consistent
with chance expectation. No nominally significant p val-
ues were observed for three functional polymorphisms
that have been reported to be associated with schizophre-

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SANDERS, DUAN, LEVINSON, ET AL.
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nia: rs4680 (Val/Met, COMT), rs1801028 (Cys311Ser,
DRD2), and rs1799732 (–141C Ins/Del, DRD2). Two func-
tional SNPs in HTR2A that are in strong linkage disequilib-
rium (rs6313, T102C; rs6311, –1,438A/G) were nominally
associated (p<0.03–0.04), but for the opposite rs6313 allele
(T) than the previously reported association (C).
Comparison Sample
Epidemiological and clinical characteristics of the com-
parison sample are described in data supplement Table 4.
This comparison sample is currently used for the analysis
of association with multiple psychiatric disorders by nu-
merous research groups.
Discussion
We did not detect a significant association of schizo-
phrenia with SNPs in 14 candidate genes that have been of
great interest to the field in a large sample of case and
comparison subjects with closely comparable ancestry,
studied with analysis of single SNPs, haplotypes, and im-
puted genotypes with a comprehensive map of common
SNPs, additional SNPs with known or putative functional
effects, and SNPs in these genes that had been previously
reported as associated with schizophrenia.
Our sample could possibly be in some way atypical, al-
though we doubt that our findings can be explained in this
way. It is possible, for example, that a sample limited to
known familial cases would produce different results,
given that pedigree-based linkage studies identified the re-
gions in which many of these genes are located. However,
most of the original and subsequent association reports
have been in European ancestry case-control samples. Our
cases were also likely to be clinically representative of other
samples based on our collective experience in multicenter
studies and our own data: we demonstrated high cross-site
interrater reliability for schizophrenia and schizoaffective
disorder diagnoses (kappas of 0.88 and 0.89) (11). Although
we interviewed highly screened subjects (i.e., subjects with
eligible clinical diagnoses were further screened by study
clinicians), the best-estimate final diagnosis process still
excluded approximately 10% of interviewed cases, indicat-
FIGURE 1. Association Results for Single Nucleotide Polymorphisms (SNPs) in Candidate Genesa
aShown are the results of Armitage trend tests of association for all 648 candidate gene SNPs as the –log10 of the pointwise nominal p value
for each SNP (y axis) ordered along the x axis by analyzed SNP relative physical position within each gene region (Mb in hg17, i.e., National
Center for Biotechnology Information Build 35), each gene region demarcated by a vertical line. The horizontal line at –log10=1.3 corresponds
to a pointwise nominal p=0.05, and the horizontal line at –log10=2.0 to a pointwise nominal p=0.01.
2.5
2.0
1.5
1.0
–log (p)
Single Nucleotide Polymorphism
0.5
0.0
RGS4
DISC1
DTNBP1
STX7
TAAR6
PPP3CC
NRG1
DRD2
HTR2A
DAOA
AKT1
CHRNA7
COMT
ARVCF

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ASSOCIATION OF GENES WITH SCHIZOPHRENIA
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ing a high priority on diagnostic accuracy. Excluding
schizoaffective disorder cases from the analysis did not
change the results. Another potential difference in our
study compared to many others in the field is that we used
psychiatrically screened comparison subjects, which led to
excluding from genotyping ~8% of the comparison sub-
jects who would have otherwise been in the experiment
but were excluded for not denying one or more of three
psychosis screens: treatment, diagnosis, or presence of 1)
schizophrenia or schizoaffective disorder, 2) auditory hal-
lucinations or delusions, or 3) bipolar disorder or manic
depression. The largest single item contributing to the 8%
was endorsement of previous treatment or diagnosis of bi-
polar disorder or manic depression, which was endorsed
by 3.6% of the comparison subjects of European ancestry.
The screening of comparison subjects can increase power
by reducing the number of affected subjects in the com-
parison portion of the sample. However, if the large major-
ity of those excluded from the comparison group based on
a suspicion that they might have been affected were in re-
ality unaffected, it can be argued that such screening
would represent an overall loss of power owing to a larger
effect of a smaller sample size in the comparison subjects.
We do not know which might be the case in our sample but
adopted the more conservative approach of using
screened comparison subjects.
What do we learn from these results? First, we cannot
definitively rule out a role for any of these genes in schizo-
phrenia. Many of the odds ratios for association are in a
plausible range (1.10–1.23) for small susceptibility effects
but below what would produce significant p values in this
sample or in the smaller samples used in previous studies.
The larger odds ratios in some previous reports could ei-
ther be false positives or inflated estimates of the genetic
effects, as is common in initial reports—the so-called
“winner’s curse” (29). Also, only the hypothesis of associa-
tion with common SNPs has been tested in a reasonably
systematic way, both here and in the previous studies of
these genes. We will learn more from future studies using
resequencing methods to detect rare SNPs and genome-
wide SNP arrays to detect genomic deletions and inser-
tions as well as large-scale analyses of gene-gene interac-
tions.
Second, the results demonstrate the importance of
large-scale, systematic tests of genomic hypotheses. Al-
though these candidate genes represent the best findings
of the first generation of positional approaches to schizo-
phrenia, evidence for each of them has been modest and/
or inconsistent. Many of the initial associations were iden-
tified by the screening of candidate regions with what
would now be considered small samples and inadequate
coverage of common SNPs as well as (in some cases) older
genotyping technologies that yield more missing data and
higher error rates than current methods. Genome-wide as-
sociation studies of large samples provide more powerful
and systematic tests of common SNPs and of insertion/de-
letion variants throughout the genome and have already
produced robust replicable association findings for other
complex genetic phenotypes (10, 30–33). Multiple ge-
nome-wide association studies of schizophrenia are under
way, including our study of the Molecular Genetics of
Schizophrenia Parts 1 and 2 and Schizophrenia Genetics
Initiative samples, the first phase of which is part of the Ge-
netic Association Information Network (34). One caveat is
that large-scale SNP arrays do not optimally cover every
gene, so focused studies such as this one will still be
TABLE 4. Single SNP Association Results for SNPs by Genea
Tested SNPs
Np<0.05p<0.01
RGS4
1220
DISC1
11550
DTNBP1
3800
STX7
2742
TAAR6
1710
PPP3CC
2100
NRG1
21751
DRD2
32 30
HTR2A
3430
DAOA
3020
AKT1
1710
CHRNA7
1820
COMT
2910
ARVCF
4110
Total648303
aShown are the numbers of all tested SNPs and of tag SNPs with
pointwise empirical Armitage trend test values of p<0.05 and
p<0.01. Note that 4.6% of tested and 4.8% of tag SNPs had p<0.05,
and 0.5% in each set had p<0.01, i.e., chance expectation.
Gene
Tag SNPs
p<0.05
1
4
0
3
1
0
2
2
1
2
1
2
1
1
21
N
6
p<0.01
0
0
0
2
0
0
0
0
0
0
0
0
0
0
2
78
22
15
15
8
159
17
27
19
13
15
19
20
433
FIGURE 2. Quantile-Quantile Plot of Observed Versus Ex-
pected p Values for Tag Single Nucleotide Polymorphisms
(SNPs)a
aThe blue dots represent the relationship between the expected (x
axis) and observed (y axis) p values for pointwise nominal Armitage
trend tests for the 433 SNPs that represent tags (at r2>0.8) for com-
mon SNPs in each gene. The solid line represents the null expecta-
tion. The observed distribution is within the 95% confidence inter-
val of the null expectation, consistent with a lack of evidence in our
sample for association with schizophrenia in the tested candidate
genes. The lowest p values are slightly below the line (less signifi-
cant than expected) but still within the confidence interval.
0.001
0.001
0.01
0.01
0.1
0.1
Expected p Value
1
1
Observed p Value

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SANDERS, DUAN, LEVINSON, ET AL.
ajp.psychiatryonline.org
needed for genes whose role in schizophrenia is supported
by candidate gene, linkage, genome-wide association, or
biological studies. More systematic approaches to studying
rare DNA sequence variants should also soon be available.
Our results suggest that, taken together, common DNA
variants in these 14 genes are unlikely to explain a large
proportion of the genetic risk for schizophrenia in popula-
tions of European ancestry. More robust findings are likely
to be discovered using genome-wide association methods
and, as our knowledge of the biology of mental illness con-
tinues to improve, focused studies of genes based on more
precise mechanistic hypotheses. Nevertheless, although
larger samples could possibly detect small genetic effects
that were missed in this experiment, our findings suggest
it is unlikely that true associations exist at the population
level for the alleles that have formed the basis for the large
candidate gene literature for these 14 postulated schizo-
phrenia candidate genes.
Received Oct. 7, 2007; revision received Nov. 30, 2007; accepted
Dec. 3, 2007 (doi: 10.1176/appi.ajp.2007.07101573). From the Center
for Psychiatric Genetics, Department of Psychiatry and Behavioral Sci-
ences, Evanston Northwestern Healthcare and Feinberg School of
Medicine, Northwestern University, Evanston, Ill.; the Department of
Psychiatry and Behavioral Sciences, Stanford University School of
Medicine, Palo Alto, Calif.; the Departments of Psychiatry and Genet-
ics, Washington University, St. Louis; the Queensland Centre for Men-
tal Health Research and the Queensland Institute for Medical Re-
search, Brisbane, Queensland, Australia; the Department of
Psychiatry, University of Colorado Health Sciences Center, Denver; the
School of Nursing, Louisiana State University Health Sciences Center,
New Orleans; the Atlanta Veterans Affairs Medical Center and Depart-
ment of Psychiatry and Behavioral Sciences, Emory University, At-
lanta; the Mental Health Clinical Research Center and Department of
Psychiatry, University of Iowa College of Medicine, Iowa City, Iowa; the
Department of Psychiatry, Mount Sinai School of Medicine, New York;
the Department of Psychiatry, University of California, San Francisco;
and the Département de Génétique, INSERM-U563, Institut National
de la Recherche et de la Santé Médicale, Toulouse, France. Address
correspondence and reprint requests to Dr. Gejman, Evanston North-
western Healthcare Research Institute, 1001 University Place, Evan-
ston, IL 60201-3137; pgejman@northwestern.edu (e-mail).
Data and biomaterials from the NIMH Schizophrenia Genetics Ini-
tiative, the Molecular Genetics of Schizophrenia Part 1, and the Mo-
lecular Genetics of Schizophrenia Part 2 case samples and the Molec-
ular Genetics of Schizophrenia Part 2 comparison sample were
collected by a number of investigators and institutions as detailed in
data supplement Table 1, under the following grants: NIMH Schizo-
phrenia Genetics Initiative U01s: MH46276, MH46289, and
MH46318; and Molecular Genetics of Schizophrenia Part 1 and Part
2 R01s: MH67257, MH59588, MH59571, MH59565, MH59587,
MH60870, MH59566, MH59586, MH61675, and MH60879. Research
Career Development Awards (to Drs. Duan and Sanders) at the Evan-
ston Northwestern Healthcare Research Institute, Evanston, Ill., sup-
ported this work.
The authors thank the study participants for their assistance and
the individuals at each participating institution for their contribu-
tions, especially Heather Smith, Cheryl Filippich, Duncan McLean,
Margaret Baier, Erich Conrad, Sherri Spera Chalona, Edmond Bedjeti,
Roberta Fishman, Jihad M. Abdallah, Ilya M. Karagodin, Katherine
Radwanski, Sandra Shi, Taylor J. Reif, Rick Li, Mike Dennis, John Cor-
coran, and Douglas A. Fugman. Genotyping was carried out at Evan-
ston Northwestern Healthcare Research Institute.
Dr. Amin has received funds from Pfizer, Organon, and the Founda-
tion for NIH. Dr. Black has received research support from Shire and
Forest, has been on the speakers bureau of Pfizer, and has received
consulting honoraria from Forest and Jazz. The remaining authors re-
port no competing interests.
Drs. Sanders and Duan made equal contributions to this article.
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