A Genome-Wide Investigation of SNPs and CNVs in Schizophrenia

Abstract

We report a genome-wide assessment of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) in schizophrenia. We investigated SNPs using 871 patients and 863 controls, following up the top hits in four independent cohorts comprising 1,460 patients and 12,995 controls, all of European origin. We found no genome-wide significant associations, nor could we provide support for any previously reported candidate gene or genome-wide associations. We went on to examine CNVs using a subset of 1,013 cases and 1,084 controls of European ancestry, and a further set of 60 cases and 64 controls of African ancestry. We found that eight cases and zero controls carried deletions greater than 2 Mb, of which two, at 8p22 and 16p13.11-p12.4, are newly reported here. A further evaluation of 1,378 controls identified no deletions greater than 2 Mb, suggesting a high prior probability of disease involvement when such deletions are observed in cases. We also provide further evidence for some smaller, previously reported, schizophrenia-associated CNVs, such as those in NRXN1 and APBA2. We could not provide strong support for the hypothesis that schizophrenia patients have a significantly greater "load" of large (>100 kb), rare CNVs, nor could we find common CNVs that associate with schizophrenia. Finally, we did not provide support for the suggestion that schizophrenia-associated CNVs may preferentially disrupt genes in neurodevelopmental pathways. Collectively, these analyses provide the first integrated study of SNPs and CNVs in schizophrenia and support the emerging view that rare deleterious variants may be more important in schizophrenia predisposition than common polymorphisms. While our analyses do not suggest that implicated CNVs impinge on particular key pathways, we do support the contribution of specific genomic regions in schizophrenia, presumably due to recurrent mutation. On balance, these data suggest that very few schizophrenia patients share identical genomic causation, potentially complicating efforts to personalize treatment regimens.

Full text

A Genome-Wide Investigation of SNPs and CNVs in
Schizophrenia
Anna C. Need
1.
, Dongliang Ge
1.
, Michael E. Weale
2.
, Jessica Maia
1
, Sheng Feng
3
, Erin L. Heinzen
1
,
Kevin V. Shianna
1
, Woohyun Yoon
1
, Dalia Kasperavic
ˇiu¯te˙
4
, Massimo Gennarelli
5,6
, Warren J.
Strittmatter
7
, Cristian Bonvicini
5
, Giuseppe Rossi
8
, Karu Jayathilake
9
, Philip A. Cola
10
, Joseph P.
McEvoy
11
, Richard S. E. Keefe
11
, Elizabeth M. C. Fisher
4
, Pamela L. St. Jean
12
, Ina Giegling
13
, Annette M.
Hartmann
13
, Hans-Ju
¨rgen Mo
¨ller
14
, Andreas Ruppert
15
, Gillian Fraser
16
, Caroline Crombie
16
, Lefkos T.
Middleton
17
, David St. Clair
16
, Allen D. Roses
18
, Pierandrea Muglia
19
, Clyde Francks
19
, Dan Rujescu
13
,
Herbert Y. Meltzer
9
, David B. Goldstein
1
*
1Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America, 2Department of Medical and Molecular Genetics, King’s
College London, Guy’s Hospital, London, United Kingdom, 3Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States of
America, 4Institute of Neurology, University College London, London, United Kingdom, 5Genetic Unit, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy,
6Department of Biomedical Science and Biotech, University of Brescia, Brescia, Italy, 7Division of Neurology, Department of Medicine, Duke University Medical Center,
Durham, North Carolina, United States of America, 8Psychiatric Unit, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy, 9Department of Psychiatry, Vanderbilt
University School of Medicine, Nashville, Tennessee, United States of America, 10 University Hospitals Case Medical Center, Cleveland, Ohio, United States of America,
11 Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America, 12 Genetics Division,
GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, 13 Division of Molecular and Clinical Neurobiology, Department of Psychiatry, Ludwig-
Maximilians-University, Munich, Germany, 14 Department of Psychiatry, Ludwig-Maximilians-University, Munich, Germany, 15 Genetics Research Centre GmbH (GRC),
Munich, Germany, 16 Department of Mental Health, University of Aberdeen, Aberdeen, United Kingdom, 17 Division of Neuroscience and Mental Health, Neuroscience
Laboratories, Burlington Danes, Hammersmith Hospital, London, United Kingdom, 18 Deane Drug Discovery Institute, Duke University Medical Center, Durham, North
Carolina, United States of America, 19 Medical Genetics, GlaxoSmithKline R&D, Verona, Italy
Abstract
We report a genome-wide assessment of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) in
schizophrenia. We investigated SNPs using 871 patients and 863 controls, following up the top hits in four independent
cohorts comprising 1,460 patients and 12,995 controls, all of European origin. We found no genome-wide significant
associations, nor could we provide support for any previously reported candidate gene or genome-wide associations. We
went on to examine CNVs using a subset of 1,013 cases and 1,084 controls of European ancestry, and a further set of 60
cases and 64 controls of African ancestry. We found that eight cases and zero controls carried deletions greater than 2 Mb,
of which two, at 8p22 and 16p13.11-p12.4, are newly reported here. A further evaluation of 1,378 controls identified no
deletions greater than 2 Mb, suggesting a high prior probability of disease involvement when such deletions are observed
in cases. We also provide further evidence for some smaller, previously reported, schizophrenia-associated CNVs, such as
those in NRXN1 and APBA2. We could not provide strong support for the hypothesis that schizophrenia patients have a
significantly greater ‘‘load’’ of large (.100 kb), rare CNVs, nor could we find common CNVs that associate with
schizophrenia. Finally, we did not provide support for the suggestion that schizophrenia-associated CNVs may preferentially
disrupt genes in neurodevelopmental pathways. Collectively, these analyses provide the first integrated study of SNPs and
CNVs in schizophrenia and support the emerging view that rare deleterious variants may be more important in
schizophrenia predisposition than common polymorphisms. While our analyses do not suggest that implicated CNVs
impinge on particular key pathways, we do support the contribution of specific genomic regions in schizophrenia,
presumably due to recurrent mutation. On balance, these data suggest that very few schizophrenia patients share identical
genomic causation, potentially complicating efforts to personalize treatment regimens.
Citation: Need AC, Ge D, Weale ME, Maia J, Feng S, et al. (2009) A Genome-Wide Investigation of SNPs and CNVs in Schizophrenia. PLoS Genet 5(2): e1000373.
doi:10.1371/journal.pgen.1000373
Editor: Mark I. McCarthy, Oxford Centre for Diabetes, Endocrinology, and Metabolism and Wellcome Trust Centre for Human Genetics, University of Oxford,
United Kingdom
Received August 7, 2008; Accepted January 7, 2009; Published February 6, 2009
Copyright: ß2009 Need et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The US patients were part of an NIMH-funded Clinical Research Center at Case Western Reserve University and prospective clinical trials at Vanderbilt
University. The Aberdeen, Munich, Italian replication cohort and US cohort genotyping and the Genetics of Memory/ Genetics of Epilepsy cohort was funded by
Duke Institute for Genome Sciences and Policy start-up funding provided to DBG. Recruitment of the patients from Munich was partially supported by
GlaxoSmithKline (GSK). DBG is a paid consultant for GSK. Other than these GSK contributions, the funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: d.goldstein@duke.edu
.These authors contributed equally to this work.
PLoS Genetics | www.plosgenetics.org 1 February 2009 | Volume 5 | Issue 2 | e1000373

Introduction
Schizophrenia is a common neuropsychiatric disorder that is
characterized by positive symptoms such as delusions, paranoia
and hallucinations, negative symptoms including apathy, anhedo-
nia, and social withdrawal, and extensive cognitive impairments
that may have the greatest impact on overall function [1,2]. While
current antipsychotic drug treatments control positive symptoms
in most patients, negative symptoms and cognitive impairments
are much less improved by these agents [3]. A possible way to
improve the treatment of schizophrenia is to identify genetic risk
factors that might elucidate the underlying pathophysiological
bases as well as help to subclassify patients at a molecular level in a
manner helpful to therapy.
The etiology of schizophrenia as presently defined is not well
understood. While there are clear environmental contributors to
disease [4–10], it is clear that genetic predisposition is the major
determinant of who develops schizophrenia, with heritability
estimates as high as 80% [11,12], placing schizophrenia amongst
the most heritable of the common diseases.
Schizophrenia genetic research has traditionally focused on
identifying linkage regions or on candidate genes and polymor-
phisms, such as the val158met polymorphism in the dopamine
metabolizing gene COMT, or other types of variants such as
VNTRs (MAOA,DAT1,SLC6A4). Such studies have implicated
dozens of genes and variants, but none is generally accepted as
definitively associated with schizophrenia [13–15].
It is now possible to represent the majority of common genetic
variation by genotyping a selected set of tagging SNPs [16]. Such
hypothesis-free genome-wide association studies allow the discov-
ery of new genes and pathways affecting complex traits such as
schizophrenia with much greater power to detect small effects than
linkage studies. To date, there have been five genome-wide
association studies (GWAS) of schizophrenia. The first study used
a small sample size of 178 cases and 144 controls self-identifying as
Caucasian and recruited in the US, and reported the association of
a SNP in the pseudoautosomal region of the y chromosome at
p = 3.7610
27
[17]. The second used pooled DNA samples from
600 cases and 2,771 controls, all Ashkenazi Jews, and found no
genome-wide significant association, although they reported a
strong effect of a RELN SNP in females only [18]. The third used
pooled DNA from 574 schizophrenia trios and 605 unaffected
controls, all recruited in Bulgaria and again found no genome-
wide significant association [19]. The next study of 738 cases and
733 controls (each about 30% African-American, 56% European
American and 14% Other) found no evidence for the involvement
of common SNPs in schizophrenia [14]. The most recent study
included 479 cases compared to 2,937 WTCCC controls and
replicated the top SNPs in two further datasets respectively
comprising 1,664 cases and 3,541 controls and 6,666 cases and
7,897 controls [20]. Three of the loci remained associated after all
analyses, one in ZNF804A and two in intergenic regions.
While the genotyping arrays used in genome-wide association
studies have very limited capacity to detect the effects of rare single
site variants, large copy number variants can be readily identified
using these arrays, even if they occur in only one or a few subjects.
Recently, considerable attention has turned towards identifying
rare copy number variants that show elevated frequencies in
various human diseases using these platforms.
In schizophrenia, four genome-wide screens for large CNVs
have recently appeared. Two papers showed that large (.100 kb),
rare deletions and duplications that disrupted genes were
significantly more common in schizophrenia cases than controls
[21,22], and that the disrupted genes in patients were dispropor-
tionately from neurodevelopmental pathways [21]. Another
showed that de novo CNVs were eight times more frequent in
sporadic cases of schizophrenia than they were in familial cases or
unaffected controls [23]. While neither of these papers succeeded
in identifying particular CNVs as definitive schizophrenia risk
factors, the greater load of CNVs reported in cases implicate this
type of genetic variant in schizophrenia. Consistent with this,
Stefansson et al. [24] recently screened for de novo CNVs and
focused on three recurrent CNVs in 4,718 patients and 41,201
controls (including, for replication purposes only, all samples
investigated in this study), located at 1q21.1, 15q11.2 and 15q13.3,
with odds ratios of 14.8, 2.7 and 11.5. Two of these same loci were
also reported as risk factors by the International Schizophrenia
Consortium (also including the Aberdeen samples used here) [22].
These papers collectively suggest that the common disease-
common variant hypothesis may be less relevant to schizophrenia
than rare variants with highly penetrant effects [25]. However it
should be noted that, to date, no WGA SNP study has been well
powered to detect effects of common SNPs, since they have either
been performed using pooled DNA, ethnically heterogeneous
samples or small samples sizes, so it is not yet possible even to rule
out reasonably large effects of common SNPs in schizophrenia.
Additionally, despite these strong suggestions of a role for rare
highly penetrant CNVs, there has been no test of whether any
common CNVs also contribute to the risk of schizophrenia.
Here we investigated the effects of common SNPs, and both
common and rare CNVs, on schizophrenia risk using genome-
wide SNP data from the Illumina HumanHap genotyping
BeadChips.
Results
Single Nucleotide Polymorphisms
We tested for SNP associations with schizophrenia with a
logistic regression model using the PLINK software [26] and
including sex and curated EIGENSTRAT axes as covariates. An
additive genetic model was tested. A series of quality control
procedures were undertaken before this analysis (for details see
Author Summary
Schizophrenia is a highly heritable disease. While the drugs
commonly used to treat schizophrenia offer important
relief from some symptoms, other symptoms are not well
treated, and the drugs cause serious adverse effects in
many individuals. This has fueled intense interest over the
years in identifying genetic contributors to schizophrenia.
In this paper, we first show that common genetic variants,
the focus of most research until recently, do not seem to
have a major impact on schizophrenia predisposition. We
then provide further evidence that very rare, large DNA
deletions and duplications contribute to or explain a
minority of schizophrenia cases. Although the small
number of events identified here do not restrict focus to
a finite set of molecular pathways, we do show one event
that deletes a gene known to interact with DISC1, a gene
known to cause psychiatric problems in one family. Such
convergent findings have potential implications for the
development of new therapies and patient subclassifica-
tions. We conclude that schizophrenia genetics research
must turn sharply toward the identification of rare genetic
contributors and that the most important tool in this effort
will be complete whole-genome sequencing of patients
whose clinical characteristics have been very thoroughly
assessed.
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 2 February 2009 | Volume 5 | Issue 2 | e1000373

Text S1). The results were then annotated using the WGAViewer
software [27]. No single polymorphism showed a genome-wide
significant association in the discovery cohort. The top 100
associated SNPs are shown in Table 1. The most strongly
associated SNPs at this stage were in the ADAMTSL3 gene (lowest
p = 1.34610
26
, Table 1).
Following these analyses, we genotyped the top 100 polymor-
phisms in a further independent Munich cohort of 298
schizophrenic patients and 713 healthy controls, all self-identifying
as of German or central European ancestry. Using the Sequenom
iPLEX system, we successfully genotyped 98 of the 100 SNPs and
found that 8 of these 98 variants showed an association that was
significant at the 0.05 level in the independent cohort (rs2135551,
rs950169, rs1911155, rs4745431, rs4745430, rs4487082,
rs3748376 and rs11635597). These included the most strongly
associated three SNPs in the list: rs2135551, rs950169 and
rs1911155 in ADAMTSL3 (in linkage disequilibrium with one
another) (Table 1). Since 3 of these 8 SNPs are in strong LD, this is
approximately the number of significant associations we would
expect by chance at p,0.05, however in all 8 cases the direction of
effect was the same as in the original cohort. The combined p
value for the strongest associated SNP (rs2135551) across the
original and first replication studies is 1.3610
27
. If we use a
Bonferroni correction for all the SNPs considered in this study
(312,565 SNPs that passed quality control and the minor allele
restriction), the 0.05 experiment-wide cut-off is 0.05/
312565 = 1.6610
27
, which means that this association is sugges-
tive, but falls short of the proposed threshold for genome-wide
significance of ,5610
28
[28].
The most associated polymorphism (rs2135551) is in the 39UTR
of exon 30 of ADAMTSL3. To investigate a possible functional
mechanism for this SNP, we tested for association with alternative
splicing events in the associated region (exons 28, 29, and 30) using
brain tissue. We found that the associated SNPs rs950169 and
rs2135551 showed a highly significant correlation with the use of
an alternative splice acceptor site, resulting in a truncated PLAC
(protease and lacunin) domain in the ADAMTSL3 protein
(p,0.0001, Figure S2). We then confirmed a causal relationship
between rs950169 and the observed splicing pattern using a
MINIGENE system (Text S1), and showed an association between
rs950169 genotype and the splice form of ADAMTSL3 in brain
tissue from both healthy controls and Alzheimer’s disease patients
(Figure S3). Finally, we showed that ADAMTSL3 is particularly
strongly expressed in hippocampal pyramidal cells (Figure S4).
To try to further confirm this association, we genotyped the
ADAMTSL3 SNP rs2135551 (and, by proxy, rs950169) using
TaqMan assays in a further 394 cases and 524 controls from Italy.
However, in this cohort the p value was 0.311. We then
investigated the SNP in 589 schizophrenia cases and 11,491
controls from Iceland and found that it did not associate with
schizophrenia in these subjects either (p= 0.12) (Hreinn Stefa´ ns-
son, personal communication). Finally, we failed to replicate this
association in a third cohort of 179 cases and 267 controls of
European-American ancestry genotyped using the Illumina-610
Quad genotyping chip (and passing through the same quality
control procedures used for the discovery cohorts). The p value for
rs213551 was 0.19 and for rs950169 it was 0.22. Since we had
whole-genome data for the European-American cohort, we also
checked the top 100 SNPs from the discovery cohort but none of
the SNPs associated after being corrected for 100 tests (lowest raw
p value = 0.02).
In the combined Munich and Aberdeen discovery cohort, the
MAF of rs2135551 was 0.23 in cases and 0.30 in controls. In the
Italian cohort, the MAF was a little higher at 0.33 in cases and
0.36 in controls, in Iceland the MAF was 0.25 in cases and 0.27 in
controls, and in the European-American sample it was 0.28 in
cases and 0.25 in controls. These data indicate that despite its real
functional effect, the top association with schizophrenia in the
discovery and first replication cohorts is likely to be a false positive.
To assess more formally the combined evidence of association
for the ADAMTSL3 SNPS we extended the Bayesian framework
developed by Wakefield [29] to consider the cumulative posterior
odds for the hypothesis of true association with schizophrenia, as
data from successive datasets are added (see Methods). We found
that the posterior odds for true association at rs2135551 tracked
from 0.10 (after GWAS) to 0.68 (after Replication 1 in Germany)
to 0.30 (after Replication 2 in Italy) to 0.15 (after Replication 3 in
deCODE) to 0.02 (after Replication 4 in the US cohort). Thus,
under these assumptions, the odds for the association being true
never rise above one, and finally reduce to the null hypothesis
being 50 times more likely than the alternative hypothesis of true
association.
To investigate the degree of weight to put on the functional
effect of rs950169, using data from a genome-wide association
study examining the effects of approximately 550,000 SNPs on
expression of 1.41 million exons in human frontal cortex tissue
(Text S1), we looked to see what percentage of exons show
association with a nearby (+/2100 kb) SNP at or below the level
of significance that rs950169 associates with the expression of the
ADAMTSL3 exon 3605495 (https://www.affymetrix.com/analysis/
netaffx/index.affx). We found that 84,840, or 6%, of exons showed a
SNP association of this magnitude. This illustrates the importance of
weighing functional evidence against an appropriate null hypothesis,
and we note that in many cases arriving at a quantitative evaluation of
this kind can be very difficult. This fact, along with the failed
replication in the Italian, Icelandic and US data, suggests that
evidence of functional effect for SNPs implicated in GWAS studies
should not be considered an appropriate substitute for confirmation
in replication datasets.
Association with Previously Reported Schizophrenia Loci
GWAS. To check for common findings between our study and
previous schizophrenia GWAS studies, we first checked the six
SNPs from O’Donovan et al. that remained significant after
replication analyses [20]. None of the SNPs were directly
genotyped on our platforms, however the three strongest
associated SNPs, a well as two of the three others were
represented by a proxy SNP with r
2
$0.69. Since some of the
Munich samples from this study were used in the replication for
the O’Donovan et al. paper, we reported association statistics here
for the Aberdeen samples only (Table S1). In the Aberdeen
dataset, none of the SNPs showed a significant association with
schizophrenia.
We then went on to check the p values for the following SNPs,
or their closest proxy within 100 kb that was genotyped in our
schizophrenia samples: the top 63 SNPs shown to be associated
with individual genotyping in Kirov et al [19], the top 25 SNPs
from Sullivan et al [14] and all individually genotyped SNPs that
had a p,0.05 in combined males and females from Shifman et al
[18]. These particular SNPs were chosen because only these p
values were made publically available in the papers. Of these 116
SNPs, 8 were not represented in our dataset. Of the other 108, 6
were associated at p,0.05, and the lowest p value was 0.002 for
rs11595716, a proxy for rs17746501, associated in Shifman et al.at
p = 0.007 [18](see Table S2 for all data). This SNP is not located
in a gene, nor is it strongly associated with any other SNP in or
close to a gene. The other associated SNPs had p values of 0.011
(proxy for rs151222), 0.015 (proxy for rs234993), 0.020 (proxy for
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 3 February 2009 | Volume 5 | Issue 2 | e1000373

Table 1. The top 100 SNPs associated with schizophrenia.
Rank SNP Discovery P Replication P Combined P OR MAF Case
MAF
Control Chr Position SNP Type
Major/ Minor
alleles Closest gene Distance to gene
1 rs2135551 1.34E-06 0.03 1.35E-07 0.68 0.23 0.30 15 82498855 3PRIME UTR A/G ADAMTSL3 0
2 rs950169 2.28E-06 0.04 3.14E-07 0.68 0.23 0.30 15 82497465 NON SYNONYMOUS C/T ADAMTSL3 0
3 rs12910334 2.93E-06 0.21 2.87E-06 0.69 0.24 0.30 15 82749322 3PRIME UTR G/A Q96PW8_HUMAN 0
4 rs1911155 4.18E-06 N/A 4.18E-06 0.69 0.23 0.30 15 82578639 INTERGENIC A/G ADAMTSL3 79044
5 rs4814019 1.41E-05 0.77 9.39E-05 1.38 0.36 0.29 20 11456980 INTERGENIC A/C C20orf61 281900
6 rs4894485 1.90E-05 0.19 1.29E-05 1.37 0.43 0.35 3 176560380 INTRONIC C/T NAALADL2 0
7 rs1435194 1.92E-05 0.81 0.0001 1.44 0.25 0.19 5 105481256 INTERGENIC C/A N/A N/A
8 rs2007744 1.96E-05 0.91 0.0002 1.52 0.18 0.13 9 78044132 INTRONIC G/T PCSK5 0
9 rs2717907 2.05E-05 0.70 0.0001 0.67 0.15 0.21 7 25269991 INTERGENIC T/C NPVF 235361
10 rs715969 2.09E-05 0.52 6.28E-05 0.59 0.08 0.12 8 132543045 INTERGENIC A/C ADCY8 2419191
11 rs1146313 2.16E-05 0.88 0.0002 0.74 0.43 0.50 1 118827613 INTERGENIC C/T SPAG17 2298256
12 rs983037 2.19E-05 0.13 9.59E-06 0.73 0.32 0.39 2 12997671 INTERGENIC A/C TRIB2 197360
13 rs7175728 2.79E-05 0.55 8.55E-05 0.74 0.36 0.43 15 53681850 INTERGENIC C/T PYGO1 213507
14 rs1229119 2.91E-05 N/A 2.91E-05 0.74 0.35 0.43 1 118825163 INTERGENIC C/T SPAG17 2295806
15 rs7943936 3.10E-05 0.29 3.61E-05 1.34 0.53 0.46 11 130214277 INTERGENIC C/T SNX19 36708
16 rs1463259 3.17E-05 N/A 3.17E-05 0.70 0.22 0.28 8 82900300 INTRONIC C/T SNX16 0
17 rs725710 3.30E-05 0.41 6.18E-05 0.65 0.11 0.16 16 73431014 INTERGENIC C/A WDR59 33961
18 rs7379110 3.41E-05 0.23 2.80E-05 0.74 0.33 0.39 5 34089378 INTRONIC C/T C1QTNF3 0
19 rs7943757 3.64E-05 0.55 0.0001 0.74 0.43 0.50 11 130221367 INTERGENIC C/T SNX19 29618
20 rs2239385 3.96E-05 1.00 0.0004 1.39 0.29 0.23 21 39949617 INTRONIC A/G B3GALT5 0
21 rs505703 4.18E-05 0.50 0.0001 1.49 0.19 0.13 4 13530080 INTERGENIC C/T FAM44A 2291654
22 rs230669 4.28E-05 0.93 0.0003 0.74 0.34 0.42 11 77553034 INTERGENIC A/G KCTD21 6917
23 rs1943624 4.47E-05 0.48 0.0001 0.75 0.46 0.53 11 -9 N/A C/T N/A N/A
24 rs4772445 4.88E-05 N/A 4.88E-05 1.52 0.17 0.12 13 101601632 INTRONIC G/A FGF14 0
25 rs4714675 5.52E-05 1.00 0.0005 0.63 0.09 0.13 6 43395871 REGULATORY REGION C/T CRIP3 211358
26 rs3748376 5.64E-05 N/A 5.64E-05 0.72 0.23 0.29 15 83129356 INTRONIC C/T ZNF592 0
27 rs6696438 5.86E-05 0.42 0.0001 0.64 0.09 0.13 1 194351115 INTERGENIC C/T KCNT2 110421
28 rs2000191 6.68E-05 N/A 6.68E-05 1.93 0.07 0.04 6 22612727 INTERGENIC A/C HDGFL1 264930
29 rs7119425 6.71E-05 0.19 4.03E-05 0.75 0.40 0.46 11 130238395 INTERGENIC C/T SNX19 12590
30 rs7676721 6.76E-05 0.38 0.0001 1.39 0.28 0.22 4 13525449 INTERGENIC G/T FAM44A 2287023
31 rs3786603 7.05E-05 0.53 0.0002 1.87 0.07 0.04 19 16596788 INTRONIC C/T MED26 0
32 rs1586030 7.07E-05 N/A 7.07E-05 0.75 0.41 0.47 8 3496385 INTERGENIC C/T CSMD1 2237389
33 rs4745431 7.26E-05 0.02 3.83E-06 0.75 0.40 0.45 9 77461915 INTERGENIC C/T PCSK5 2233491
34 rs9375543 7.75E-05 0.52 0.0002 0.75 0.37 0.44 6 128352833 INTRONIC T/C PTPRK 0
35 rs2631879 8.26E-05 0.16 3.81E-05 1.55 0.15 0.10 8 21174982 INTERGENIC C/T GFRA2 418830
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 4 February 2009 | Volume 5 | Issue 2 | e1000373

Rank SNP Discovery P Replication P Combined P OR MAF Case
MAF
Control Chr Position SNP Type
Major/ Minor
alleles Closest gene Distance to gene
36 rs12365680 8.65E-05 0.88 0.0005 0.76 0.38 0.44 11 130254269 INTRONIC G/A SNX19 0
37 rs6737733 8.91E-05 0.94 0.0006 0.74 0.28 0.34 2 118291938 INTRONIC C/T DDX18 0
38 rs2216670 0.0001 0.72 0.0004 1.81 0.07 0.04 19 16555094 INTRONIC T/G MED26 0
39 rs154981 0.0001 N/A 1.00E-04 1.32 0.53 0.46 6 32988971 INTERGENIC T/C NM 002118 21413
40 rs566353 0.0001 N/A 1.00E-04 1.32 0.48 0.43 11 60472288 INTRONIC C/T SLC15A3 0
41 rs3021461 0.0001 0.28 9.40E-05 0.71 0.19 0.24 3 129578342 INTRONIC A/G EEFSEC 0
42 rs2844511 0.0001 N/A 1.00E-04 0.76 0.42 0.50 6 31497763 INTERGENIC C/T Q5SS58_HUMAN 5768
43 rs9402011 0.0001 N/A 1.00E-04 0.73 0.25 0.31 6 128347581 INTRONIC T/C PTPRK 0
44 rs4280783 0.0001 N/A 1.00E-04 0.76 0.38 0.44 4 67771960 INTERGENIC C/T CENPC1 248624
45 rs2304066 0.0001 N/A 1.00E-04 1.39 0.24 0.19 5 145760885 INTERGENIC T/C TCERG1 246181
46 rs1379552 0.0001 N/A 1.00E-04 0.72 0.20 0.25 5 111007057 INTERGENIC T/C C5orf13 85351
47 rs12680924 0.0001 0.70 0.0004 1.31 0.48 0.43 8 15098795 INTRONIC G/A SGCZ 0
48 rs1328657 0.0001 N/A 1.00E-04 1.33 0.32 0.27 13 96981887 INTERGENIC C/T RAP2A 63644
49 rs718796 0.0001 N/A 1.00E-04 1.75 0.08 0.05 7 16585507 INTRONIC A/G ANKMY2 20425
50 rs17257972 0.0001 0.67 0.0003 1.31 0.52 0.46 1 118908135 INTERGENIC G/A TBX15 319054
51 rs4669887 0.0001 0.48 0.0002 1.38 0.26 0.20 2 12893630 INTERGENIC G/T TRIB2 93319
52 rs6868716 0.0001 N/A 1.00E-04 1.54 0.12 0.09 5 145769452 INTERGENIC C/T TCERG1 237614
53 rs7762279 0.0001 N/A 1.00E-04 0.63 0.08 0.12 6 32863268 INTERGENIC T/C A2ADX3_HUMAN 223981
54 rs596958 0.0001 0.40 0.0002 1.81 0.07 0.04 21 39945361 INTRONIC G/A B3GALT5 0
55 rs764855 0.0001 N/A 1.00E-04 1.31 0.46 0.40 11 56366988 INTERGENIC G/T Q3C1V7_HUMAN 223228
56 rs7815272 0.0001 N/A 1.00E-04 0.73 0.23 0.29 8 82854364 INTERGENIC G/T SNX16 20013
57 rs7775397 0.0002 N/A 2.00E-04 0.64 0.09 0.13 6 32369230 NON SYNONYMOUS T/G C6orf10 0
58 rs2523554 0.0002 N/A 2.00E-04 0.76 0.36 0.42 6 31439808 INTERGENIC T/C HLA-B 26894
59 rs1766803 0.0002 N/A 2.00E-04 0.76 0.35 0.41 1 119185341 INTERGENIC T/C TBX15 41848
60 rs3934902 0.0002 0.12 6.26E-05 0.72 0.17 0.23 9 85367221 INTERGENIC G/A FRMD3 223940
61 rs1217461 0.0002 N/A 2.00E-04 0.73 0.20 0.24 5 104241987 INTERGENIC T/G N/A N/A
62 rs1289726 0.0002 N/A 2.00E-04 0.63 0.07 0.11 1 162679471 INTERGENIC G/T PBX1 2116213
63 rs4329498 0.0002 N/A 2.00E-04 1.30 0.52 0.46 1 118935132 INTERGENIC T/C TBX15 292057
64 rs9427727 0.0002 0.18 9.77E-05 1.32 0.40 0.34 1 200305568 INTERGENIC C/T GPR37L1 253084
65 rs1054869 0.0002 0.12 6.34E-05 0.77 0.43 0.49 11 130247840 DOWNSTREAM G/A SNX19 3145
66 rs7758512 0.0002 N/A 2.00E-04 1.54 0.13 0.09 6 30078568 INTRONIC T/G Q6ZU40_HUMAN 0
67 rs6924102 0.0002 N/A 2.00E-04 0.76 0.40 0.47 6 32919361 INTRONIC A/G PSMB8 0
68 rs4236064 0.0002 N/A 2.00E-04 1.41 0.21 0.16 6 39522005 INTRONIC G/T KIF6 0
69 rs4487082 0.0002 0.01 5.70E-06 0.59 0.06 0.09 2 229432205 INTERGENIC A/G PID1 164729
Table 1. cont.
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 5 February 2009 | Volume 5 | Issue 2 | e1000373

Rank SNP Discovery P Replication P Combined P OR MAF Case
MAF
Control Chr Position SNP Type
Major/ Minor
alleles Closest gene Distance to gene
70 rs2119137 0.0002 N/A 2.00E-04 1.31 0.40 0.34 2 174725960 INTRONIC A/G OLA1 0
71 rs39829 0.0002 N/A 2.00E-04 0.76 0.39 0.44 5 13772997 INTRONIC A/C DNAH5 0
72 rs11635597 0.0002 N/A 2.00E-04 0.74 0.24 0.29 15 82966703 3PRIME UTR C/T ZSCAN2 0
73 rs16878312 0.0002 N/A 2.00E-04 0.75 0.28 0.34 5 71741140 INTERGENIC C/A ZNF366 33850
74 rs7603333 0.0002 N/A 2.00E-04 1.30 0.49 0.43 2 79211103 INTERGENIC G/A Q53S57_HUMAN 6855
75 rs2631878 0.0002 0.39 0.0003 1.52 0.14 0.10 8 21173339 INTERGENIC G/A GFRA2 420473
76 rs4745430 0.0002 0.01 8.30E-06 0.76 0.33 0.39 9 77461845 INTERGENIC T/C PCSK5 2233561
77 rs4901053 0.0002 N/A 2.00E-04 0.77 0.46 0.52 14 50287324 INTRONIC T/G NIN 0
78 rs5756219 0.0002 N/A 2.00E-04 1.30 0.49 0.41 22 35221804 INTRONIC A/C EIF3D 0
79 rs220420 0.0002 N/A 2.00E-04 0.76 0.33 0.38 6 86918598 INTERGENIC G/T C6orf161 2416093
80 rs1294028 0.0002 0.69 0.0006 1.31 0.42 0.36 1 9287221 INTRONIC G/A SPSB1 0
81 rs7738388 0.0002 N/A 2.00E-04 1.34 0.32 0.27 6 141494838 INTERGENIC T/C N/A N/A
82 rs12966353 0.0002 0.56 0.0005 1.31 0.39 0.33 18 24297001 INTERGENIC C/A CDH2 2285812
83 rs2236711 0.0002 N/A 2.00E-04 0.77 0.43 0.49 11 130244494 INTERGENIC G/A SNX19 6491
84 rs927743 0.0002 0.22 0.0001 1.31 0.46 0.39 1 59141535 INTERGENIC G/T JUN 2118948
85 rs2073848 0.0002 N/A 2.00E-04 1.43 0.19 0.14 11 113557114 INTRONIC T/C ZBTB16 0
86 rs6457374 0.0002 N/A 2.00E-04 0.74 0.24 0.30 6 31380240 INTERGENIC T/C 1C07_HUMAN 232354
87 rs2372897 0.0002 N/A 2.00E-04 0.76 0.36 0.43 11 77420474 INTERGENIC A/G KCTD14 28486
88 rs2071538 0.0002 N/A 2.00E-04 1.37 0.26 0.20 6 32926656 INTRONIC G/A TAP1 0
89 rs1482294 0.0002 N/A 2.00E-04 1.30 0.46 0.39 12 128276744 INTRONIC G/A TMEM132D 0
90 rs3099844 0.0002 N/A 2.00E-04 0.67 0.10 0.14 6 31556955 INTERGENIC G/T HCP5 15495
91 rs1794282 0.0002 N/A 2.00E-04 0.65 0.08 0.13 6 32774504 INTERGENIC C/T HB25_HUMAN 220208
92 rs8023192 0.0002 N/A 2.00E-04 1.30 0.49 0.44 14 20265311 INTERGENIC C/A FAM12A 218628
93 rs1451487 0.0002 N/A 2.00E-04 0.71 0.15 0.19 2 199703113 INTERGENIC T/C SATB2 139355
94 rs10516269 0.0002 N/A 2.00E-04 1.41 0.20 0.14 4 13546358 INTERGENIC T/G FAM44A 2307932
95 rs30168 0.0003 N/A 3.00E-04 0.77 0.38 0.44 5 13772089 NON SYNONYMOUS G/A DNAH5 0
96 rs4800613 0.0003 N/A 3.00E-04 0.76 0.29 0.34 18 20861721 INTERGENIC A/G ZNF521 34168
97 rs3903663 0.0003 N/A 3.00E-04 0.74 0.25 0.30 6 128354781 INTRONIC A/G PTPRK 0
98 rs4658504 0.0003 0.70 0.0009 0.77 0.34 0.40 1 241107670 INTERGENIC C/T CEP170 246683
99 rs3131379 0.0003 N/A 3.00E-04 0.66 0.09 0.13 6 31829012 INTRONIC G/A MSH5 0
100 rs1377347 0.0003 N/A 3.00E-04 1.33 0.33 0.27 4 41067359 INTRONIC A/C LIMCH1 0
Displayed are: (1) rs#for the top 100 SNPs; (2) combined pvalue in Munich and Aberdeen discovery cohorts; (3) pvalue in the second independent cohort; (4) Combined pvalue in discovery and replication cohorts using Stouffer’s
method [60]; (5) odds ratio; (6) minor allele frequency in patients; (7) minor allele frequency in controls; (8) chromosome; (9) chromosomal position; (10) a description of the relative position of the SNP in the closest gene; (11) major
and minor alleles; (12) the symbol of the closest gene; and (13) distance to the closest gene.
doi:10.1371/journal.pgen.1000373.t001
Table 1. cont.
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 6 February 2009 | Volume 5 | Issue 2 | e1000373

rs208799), 0.044 (rs4761874) and 0.048 (proxy for rs297257).
Since rs151222 and rs234993 have an r
2
of 1 with one another,
these results are no more significant than what we would expect to
see by chance when examining 116 SNPs.
Additionally, we examined rs7341475, a SNP proposed in this
same study as a female-specific risk factor, in just the females from
our study (n = 275 cases and 361 controls) and found no
association (p = 0.24). Finally, a recent whole genome association
study of schizophrenia [17] found that one polymorphism,
rs4129148, achieved genome wide significance and a second
associated common polymorphism, rs28414810, was in one of the
nearest genes [17], both located in the pseudoautosomal region of
chromosomes X and Y. As in the original paper, we tested the
SNPs separately in males and females, but could find no evidence
of association in males or females of either cohort using either an
additive or a recessive model.
Candidate Genes. It has often been argued that associations
in candidate genes with strong a priori hypotheses of disease
involvement (due to previous association or due to biological
plausibility) should be treated with more weight in a genome-wide
association study than SNPs in other genes or in non-genic
regions. We have therefore also reassessed a set of previously
reported schizophrenia candidate genes with a less stringent
correction for multiple testing [30]. For each gene we first checked
whether a previously-associated candidate SNP itself (or a suitable
proxy) showed any association in our cohort and secondly whether
any SNP in the gene showed association in our cohort following
correction for all the SNPs tested in the relevant gene (Table 2).
None of the SNPs in these genes, however, shows a significant
association with schizophrenia after correction for only those SNPs
in the 25 previously-associated candidate genes (n = 782). If we
restrict our correction to only the SNPs within single candidate
genes, however, then two of the genes contain SNPs that remain
following gene-wide correction: FEZ1 (corrected for 13 SNPs) and
NOTCH4 (corrected for 28 SNPs). The most strongly associated
SNP in NOTCH4 (rs3134942, a synonymous coding SNP in very
high LD with rs8192585, a nonsynonymous coding SNP) was
actually the top hit in the Aberdeen cohort (p = 0.000016), but has
not itself been implicated in other schizophrenia studies, and was
not even marginally significant in the Munich dataset (p = 0.93).
Where we have been able to test a previously-associated SNP (or
its proxy) in each relevant gene, we have also assessed the
maximum effect size on schizophrenia risk that is consistent with
our failure to see effects in this study. The maximum permitted
allelic odds ratio is 1.23, and most have values close to 1.15,
suggesting that these variants generally have little or no impact on
disease susceptibility (see Table 2, ‘odds ratio excluded’ column).
Although our data provide little support for the previously
reported candidate genes, we recognize that if the real effect sizes
are small, these genes may not stand up to correction for multiple
testing (even when considering candidate genes as a set on their
own). Additionally, it should be noted that for some reported loci,
the Illumina SNP sets did not include the best-associated variants
from previous studies (see Table 2, ‘original SNPs without proxy’
column).
Finally, we developed a framework to assess how informative
our negative genome-wide association study is about the
cumulative contribution of common SNPs to schizophrenia. To
mimic our study design, we assumed that for a SNP to be detected
it must both obtain p,0.0003 in the initial GWAS and obtain a
joint p,1.6610
27
when combined over the initial GWAS and the
first subsequent replication stage (see Methods for details of power
calculations). We find that it is possible for a single causal SNP,
tagged at r
2
= 0.8 with a GWAS SNP and with MAF = 0.2, to be
undetected with a 20% probability if the allelic odds ratio is less
than 1.58. This means, not unexpectedly, that a single SNP with a
relatively large odds ratio could easily have been missed in our
study. But it is important to note that while one such SNP might
easily be missed, many such SNPs could not be missed, and alone,
such a SNP would contribute a locus specific to l
S
of only 1.04,
which amounts to only a tiny fraction of the sibling relative risk in
schizophrenia. To assess whether our negative data are consistent
with the hypothesis of common variants explaining most of the
sibling relative risk, we also investigated the relationship between
the number of causal SNPs that might exist in the genome, given
that all are undetected with 20% probability in our study, under a
simple assumption that that all SNP effect sizes are equal and
again assuming r
2
= 0.8 and MAF = 0.2 for each SNP. We find
that for 2, 10 and 100 SNPs the limits on allelic odds ratios are
1.50, 1.38 and 1.27 respectively, while the combined contribution
to total l
S
based on these SNPs (assuming they act independently
on risk) are 1.07, 1.22 and 2.85 respectively. Taking these
arguments to their logical conclusion, our data are also consistent
with the possibility that 274 SNPs each with an OR =1.22, r
2
= 0.8
and MAF = 0.2 lie undetected in the human genome, with 20%
probability. The total contribution of these SNPs to l
S
would be
10, consistent with observed estimates [30]. This calculation must
be viewed as hyper-conservative in terms of the contribution of
common variation since it assumes that all contributed variants
have an equal relative risk, while observations from other
conditions make clear that effect sizes fall off after the first several
that are discovered [31]. We also note that the pattern of reduction
in l
R
moving from first to second to third degree relatives is more
consistent with the presence of epistatic relationships among causal
SNPs, rather than the independent model considered here [32].
Copy Number Variation
For analysis of copy number variation, we used the three
cohorts with genome-wide SNP genotype data, namely Aberdeen,
Munich, and an American cohort that has not yet been studied
(for copy number variation) in any previous publications. All
samples that passed SNP-QC procedures (see Methods) were
entered into the CNV analysis, whereupon further QC was
performed to determine if accurate CNV calling could be expected
(see Methods). In Aberdeen, 12 samples (10 cases and 2 controls)
failed CNV QC, in Munich 39 samples (9 cases and 31 controls)
and in the American 49 samples (29 cases and 14 controls) failed
CNV QC. These samples were excluded from further analysis,
leaving a final dataset of 422 cases and 381 controls from Munich,
441 cases and 439 controls from Aberdeen and 150 cases and 264
controls from the US (European origin), a total of 1,013 cases and
1,084 controls. We also examined both previously implicated
regions, and regions newly implicated here in 60 African-
American schizophrenia patients and 64 African American
controls (after excluding 8 and 1 respectively for CNV QC failure).
Very large copy number variants. We first evaluated the
frequencies of copy number variants above 500 kb in cases and
controls to determine whether there are overall size thresholds
above which copy number variants would appear to have a
reasonably high prior likelihood of disease involvement based on
rarity in controls (Table 3). We excluded all CNVs that had #20
contributing SNPs as these were all spanning centromeres -
regions of very low SNP coverage, and likely to be false positives.
For deletions, we found as the size of the CNV increased, there
was a tendency towards increased frequency in cases compared to
controls. This was not the case for duplications until the 2 Mb size
threshold was reached. Above the 2 Mb size threshold (as depicted
on these particular genome-wide platforms), we found that
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 7 February 2009 | Volume 5 | Issue 2 | e1000373

Table 2. Twenty-five schizophrenia candidate genes were checked for association with schizophrenia in a cohort of 879 cases and 864 controls.
Gene
Previously associated SNPs
present on Illumina chip
Previously associated
SNP represented by
an LD proxy on
Illumina chip
Closest proxy (r
2
in CEU)
SNP
replication p OR excluded Lowest p
#SNPs tested
in gene
Gene-wide
correction
Set-wide
correction
Genome-wide
correction
AKT1
NA NA 0.416 4 1 1 1
CAPON/NOS1AP
NA NA 0.033 45 1 1 1
CHRNA7
NA NA 0.067 14 0.94 1 1
COMT
rs4680 0.94 1.05 0.090 12 1 1 1
DAO
NA NA 0.288 9 1 1 1
DAOA(G72)
rs3916971 rs778293 0.09 1.23 0.085 10 0.85 1 1
DISC1
rs2295959 rs3738401 0.621 1.14 0.082 64 1 1 1
DRD2
rs6277 rs6275 rs754672 (0.81)
rs2242592(1)
0.753 1.12 0.033 14 0.46 1 1
DRD3
rs6280 0.491 1.15 0.168 13 1 1 1
DTNBP1
rs760761 rs1474605 (1) 0.948 1.05 0.107 18 1 1 1
ERBB4
rs7598440 rs707284 rs839523 rs4673628 rs839517 (0.86)
rs1851169(1)
0.313 1.18 0.012 181 1 1 1
FEZ1
NA NA 0.002 13 0.026 11
GAD1
NA NA 0.289 9 1 1 1
GRIK4
NA NA 0.028 54 1 1 1
GRM3
rs1468412 rs2237562 (1) 0.495 1.15 0.075 27 1 1 1
HTR2A
rs6313 rs4941573 (1) 0.498 1.15 0.042 21 0.88 1 1
MRDS1(OFCC1)
NA NA 0.017 36 0.61 1 1
MUTED
NA NA 0.035 31 1 1 1
NOTCH4
rs175174 rs11089328(0.93) 0.872 1.09 0.0017 28 0.048 11
NRG1
NA NA 0.012 157 1 1 1
PPP3CC
NA NA 0.228 6 1 1 1
PRODH2
NA NA 0.163 7 1 1 1
RGS4
rs951439 rs6678136 (1) 0.186 1.20 0.186 2 0.372 1 1
TNF
NA NA 0.248 5 1 1 1
ZDHHC8
rs175174 rs11089328 (0.93) 0.872 1.09 0.794 2 1 1 1
Displayed are (1) gene symbol; (2) previously associated candidate SNPs in that gene, included in the Illumina 300K chip; (3) previously associated candidate SNPs in that gene, not included in the Illumina 300K chip but can be
represented by a LD proxy in the chip; (4) the closest proxy SNP present on the Illumina 300K chip for column 3; (5) the lowest p value for SNP-specific (column 2 and 4) replication; (6) Maximum odds ratio (allelic OR under
multiplicative genetic model) at the previously-implicated locus, based on observed p value at this SNP or a proxy. A real odds ratio of this size is expected to produce a p value as high as the one observed only 5% of the time,
based on the power formulae of Chapman et al [59]; For detailed review on the previous reports of these candidate genes and SNPs see reference [30]; (7) lowest p value of all the HumanHap300 SNPs located in that gene (lowest
p); (8) number of SNPs tested in each gene; (9) the lowest p value corrected for all the SNPs tested in that gene (gene-wide correction); (10) the lowest p value corrected for all the SNPs in the 25 candidate genes (set-wide
correction); (11) the lowest p value after correction for all HumanHap300 SNPs tested (genome-wide correction).
doi:10.1371/journal.pgen.1000373.t002
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 8 February 2009 | Volume 5 | Issue 2 | e1000373

deletions were absent in controls. Based on their rarity in controls,
we went on to examine in detail all events greater than 2 Mb
(Table 4). In this category, we observed 17 events, comprising 8
deletions, all in cases, and 9 duplications, 6 in cases and 3 in
controls (Table 4). Fisher’s Exact test indicated that such very large
events were significantly more prevalent in cases than controls
(p = 0.006). However, when we separated these events into
deletions and duplications we found that although deletions
remained significantly more frequent in cases (p = 0.003),
duplications did not (p = 0.33).
To investigate the role of deletions .2 Mb in healthy samples
tested in an extensive battery of cognitive tests, we searched for
such events in a set of 1,547 ethnically-mixed cognitively normal
healthy controls. We were unable to find any sample with a
deletion .2 Mb - the largest was 1.5 Mb. This indicates that
deletions greater than 2 Mb are very rare (,0.04%) in the healthy,
cognitively normal population, and suggests that when such very
large deletions are found, they appear to have a high prior
probability of being disease associated (although not necessarily
predictive of schizophrenia [33]), even when occurring only in a
single individual. Further analysis of much larger sample sizes of
cognitively normal individuals will be required to validate this
conclusion.
Of these very large deletions, four of eight were in the
chromosome 22q11.2 region that has been previously associated
with schizophrenia [34]. All four of these were in the Aberdeen
cohort, giving a prevalence of approximately 1%. This is in
accordance with the previously reported frequency of 0.75%
(95%CI: 0.5%–1.2%) [35]. The Munich and US cohorts, however
did not show any large deletions in this region, although one US
case and one Munich control subject showed a duplication
spanning the same region. Of the remaining four very large
deletions, two, both in the Aberdeen cohort, spanned a 2.06 Mb
region on 1q21.1 that has been previously reported in two larger
studies using the same samples [22,24], and has also been found in
other populations [21,22,24]. We also found a 1.7 Mb deletion in
the US cohort that overlapped with this (chr1:144,612,035–
146,336,720), and ended at the same position.
Two of the 8 very large deletions are newly reported here as
possible contributors to schizophrenia (Figure 1). One, also in the
Aberdeen cohort, spanned a 2.69 Mb region on 16p13.11-p12.4.
This region includes the gene NDE1, which binds to DISC1 in
brain developmental processes [24,36]. DISC1 is a gene that is
disrupted in patients with schizophrenia and other severe
neuropsychiatric disorders in one Scottish family [37]. Interest-
ingly, in this region we also saw a 1.2 Mb deletion in a Munich
sample, and a 1.5 Mb deletion in an African American patient
(chr16:14,771,033–16,225,138). All three deletions included the
region chr16:15387380–16198600 (and the genes MPV17L,
c16orf45,KIAA0430,NDE1,MYH11,KIAA0866,c16orf63,ABCC1
and MRP6/AbCC6) indicating that a large deletion of this region
may be a recurrent schizophrenia risk factor. Further investigation
of this region in 755 US epilepsy patients genotyped on the
Illumina Human610-Quad BeadChip revealed a further 6
deletions .500 kb (Heinzen et al., in preparation), suggesting that
it is a risk factor for other neuropsychiatric conditions as well as
schizophrenia. Collectively, these data strongly suggest that
deletions in this regions contribute to schizophrenia and epilepsy,
providing another example of a CNV influencing different
neuropsychiatric conditions [38], and supporting the observation
that deletions greater than 2 Mb are likely to be disease-associated.
The second newly-reported very large deletion was in the
Munich cohort, spanning 3.25 Mb on 8p22 and includes a
number of promising candidate genes (Table 4). Inspection of the
region in other samples did not provide further support.
We also inspected each of the large duplications, although these
were not unique to cases in our analyses. This suggests that large
duplications can be compatible with normal cognitive function,
making it more difficult to suggest causality to any of the large
duplications in the cases. However, some of the duplications were
of interest nevertheless. In the US cohort, we have one patient who
has a 9.4 Mb duplication (reported as 9.04 by PennCNV, Table 4)
on chromosome 15q11.2-13.3, which extends across the Prader-
Willi/Angelman syndrome critical locus, a region known to
contain many segmental duplications and inverted repeats [39]
(the patient does not suffer from either Prader-Willi or Angelman
syndrome). This overlaps two other large duplications - a 5 Mb
duplication in a Munich case (Table 4), and a de novo 1.5 Mb
duplication involving APBA2 previously reported in a schizophre-
nia patient [40]. Additionally, it overlaps with a previously
reported schizophrenia-associated deletion event at chr15:28.72–
30.30 [22,24], and we also observed a 1.16 Mb deletion in this
region in a US patient. This evidence confirms a role for recurrent
mutation in this region in schizophrenia susceptibility and
indicates that duplications as well as deletions of this region can
lead to schizophrenia. Additionally, since no duplication greater
than 3 Mb was found in any control subject, there is evidence that
duplications of this size are detrimental in general.
Burden of rare CNVs greater than 100 kb. Next, we
investigated general CNV load between cases and controls. It was
recently shown that schizophrenia patients were more likely to
have rare CNVs greater than 100 kb that disrupted genes (that is,
began or ended within a gene) or that deleted or duplicated entire
genes [21,22], although this was not replicated in a Chinese
population [41]. Following Walsh et al.[21], we selected all CNVs
greater than 100 kb that had not been previously reported in the
DGV and compared the frequencies of those that did and did not
affect genes between cases and controls, separating cases and
controls, and deletions and duplications. Since our CNVs are
identified based on SNP data, we are not able to precisely
determine where each event begins and ends. Therefore, we did
not attempt to distinguish between ‘‘disrupted’’ (Walsh et al. define
this as a gene that is interrupted by a CNV [21]) and ‘‘included’’
genes (genes completely encompassed by a CNV) - instead we
Table 3. Frequency of deletions in duplications in cases and controls from 500 kb to greater than 2 Mb.
500 kb–1 Mb 1–1.5 Mb 1.5–2 Mb 2 Mb+
case control case control case control Case control
deletions 0.013 0.013 0.009 0.004 0.002 0.0009 0.007 0
duplications 0.054 0.073 0.014 0.013 0.004 0.005 0.006 0.003
doi:10.1371/journal.pgen.1000373.t003
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 9 February 2009 | Volume 5 | Issue 2 | e1000373

Table 4. All CNVs greater than 2 Mb.
Chr start end SNPs length state start SNP end SNP Status Cohort Notes
1 144106312 146293282 260 2,186,971 del rs10797649 rs2932454 case Aberdeen This deletion has been found in other schizophrenia samples [22,24].A
1.4 Mb deletion was also found in the US cohort at the same location
(chr1:144838594–146293282).
1 144106312 146292286 259 2,185,975 del rs10797649 rs2999617 case Aberdeen
8 15098795 18360001 536 3,261,207 del rs12680924 rs7000897 case Munich Includes TUSC3, associated with mental retardation[47], PCM1, which
has been associated with schizophrenia [48], ASAH1, whose deficiency
results in mental retardation [49]and NAT1 and NAT2 which have been
implicated in schizophrenia.
16 15387380 18072544 491 2,685,165 del rs251633 rs9284326 case Aberdeen Includes NDE1, which interacts with DISC1 to control
neurodevelopmental processes [25,34].
22 16389909 19792353 668 3,402,445 del rs174330 rs140392 case Aberdeen Region implicated in schizophrenia in multiple studies.
22 17257787 19792353 496 2,534,567 del rs2543958 rs140392 case Aberdeen
22 17257787 19792353 496 2,534,567 del rs2543958 rs140392 case Aberdeen
22 17257787 19792353 496 2,534,567 del rs2543958 rs140392 case Aberdeen
1 185089907 187703805 220 2,613,899 dup rs2076075 rs3860306 case Munich PLA2G4A is the only gene affected by this event. This gene has previously been
implicated in schizophrenia [63].
2 106190084 109173575* 455 2,983,492 dup rs13006272 rs11893469 case Aberdeen Includes many genes, none previously implicated in schizophrenia.
4 96866611 99167973 156 2,301,363 dup rs2865703 rs783919 case Munich PDHA2 in region, not good candidate gene.
4 179888441 182314469** 567 2,426,029 dup rs436324 rs12503294 control US No genes in region
5 37621606 39730156 490 2,108,551 dup rs665327 rs1499238 control Aberdeen Includes many genes, including good schizophrenia candidates, e.g. GDNF.
However subject is unaffected.
13 79334893 81402686 381 2,067,794 dup rs6563141 rs9531267 case Aberdeen Includes SPRY2, which shows decreased expression in brains of schizophrenia
patients[64].
15 21240037 26208861 556 4,968,825 dup rs4778531 rs1635168 case Munich Includes many genes, notably GABRA3 and GABRA5, overlaps with larger
duplication found in US cohort.
15 21260328 30302218*** 1600 9,041,890 dup rs17118751 rs4779984 case US Overlaps both with duplication in Munich cohort and also region of previously
reported schizophrenia-associated 1.4 Mb duplication encompassing APBA2 [40].
22 17343340 19792353 347 2,449,014 dup rs2019061 rs140392 control Munich Same region deleted in cases. A 1.5 Mb duplication was also seen in a US case
(chr22:17257787–18719310)
*
Inspection in BeadStudio indicates that this duplication is actually 4.06 Mb, extending from chr2: 106282170–110339905.
**
Inspection in BeadStudio indicates that this duplication is actually 2.91 Mb, extending to chr4: 182,802,687.
***
Inspection in BeadStudio indicates that this duplication is actually 9.41 Mb, extending to chr15: 30,544,756.
doi:10.1371/journal.pgen.1000373.t004
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 10 February 2009 | Volume 5 | Issue 2 | e1000373

counted all gene-including and apparently gene-disrupting CNVs
together as ‘‘gene-affecting’’.
First looking at the Aberdeen cohort, which has been included
in a previous study replicating the Walsh et al. effect [22] we found
that 91 of the 441 Aberdeen cases that passed QC (21%) contained
one or more rare deletions .100 kb that affected a gene,
compared to 66/439 controls (15%), and that 61/441 (14%)
cases contained rare, greater than 100 kb, gene-affecting duplica-
tions, compared to 49/439 (11%) of controls. Fisher’s exact test
indicated that this was a significant excess of deletions in cases
(p = 0.03), but only a trend for duplications (p = 0.26). In contrast,
neither the Munich nor the US cohort (which have not been
assessed in previous publications) showed an excess of deletions in
cases (Table 5), although the Munich cohort had significantly
more duplications in cases (p = 0.03) and US cohort showed a
trend in the same direction for deletions (p = 0.08).
It was also previously reported that there were no differences
between cases and controls for rare CNVs greater than 100 kb
that did not disrupt genes [21], however the International SNP
Consortium found an excess of rare CNVs greater than 100 kb
that do not disrupt genes in schizophrenia cases [22]. It should be
noted that this dataset also included the Aberdeen samples. Our
findings here, however, were similar to those of the gene-affecting
CNVs: Aberdeen had significantly more rare deletions greater
than 100 kb that did not affect a gene (two-tailed Fisher’s
p = 0.031), and no other comparisons were significantly different
(Aberdeen duplications, Munich and US duplications and
deletions). Overall, therefore, we cannot offer further support to
the hypothesis that rare CNVs greater than 100 kb are present in
excess in schizophrenia patients, although, as shown above, we
report a trend for increased deletions greater than 1 Mb, and a
significant excess of greater then 2 Mb deletions in cases.
The population used by Walsh et al. contained a large number
of young-onset and childhood-onset schizophrenia patients [21], a
population one might expect to be enriched for genetic rather than
environmental contributors. Like the Walsh et al., cohort, the
Aberdeen schizophrenia cohort seems to contain an unusually
large number of copy number variants both in comparison to the
Aberdeen controls and in comparison to the other schizophrenia
cohorts. However, the Aberdeen cohort was not enriched for
young-onset patients, nor was it in any other obvious way different
from the Munich patient cohort. The patients from both regions
were selected using a consistent clinical protocol and the
distribution of schizophrenia subtypes were similar. Further
examination of population differences with those that do and do
not carry an excess burden of large rare CNVs will be necessary to
elucidate the differences between cohorts in this respect.
Additionally, these differences seen in this study may in part
depend on the type of platform used to detect the CNVs. The
Aberdeen, Munich, and US cohorts were each genotyped using a
Figure 1. Novel .2 Mb deletions found in schizophrenia cases. Duplications and smaller deletions in region not shown. The chromosome 8
region is deleted in a single Munich patient. The chromosome 8 region is deleted in a patient from Aberdeen and has overlapping, smaller deletions
in a patient from Munich and an African American patient. (Adapted from UCSC browser: http://genome.ucsc.edu).
doi:10.1371/journal.pgen.1000373.g001
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 11 February 2009 | Volume 5 | Issue 2 | e1000373

different genotyping platform (Illumina HumanHap550, Human-
Hap300, Human-610 Quad respectively), using only partially
overlapping SNP sets. This could influence the CNV calls,
although it is difficult to see why the HumanHap550 platform
would detect an excess of CNVs in comparison to the Human-610
Quad chip which is designed to have improved CNV detection.
Common CNVs in schizophrenia. We then tested whether
any of the more common CNVs were significantly different in
frequency between cases and controls, using Fisher’s exact test.
This has not been investigated in any of these samples previously.
In this case common is a relative term rather than defined by a
particular frequency cut-off, since a common CNV was defined as
one that was present in three or more individuals in any particular
cohort. We did not attempt to classify different CNVs as identical
or different, as use of the different BeadChips can make the
beginning and endpoints of the CNVs unclear. Additionally, the
actual length of the CNV may be unimportant if it covers the same
critical region as a shorter or longer CNV. We therefore
performed this analysis by determining copy number for each
SNP on the Beadchip, and performing Fisher’s Exact test for each
SNP included in a CNV. Low confidence CNVs, those in some
telomeric and centromeric regions and those coding for
immunoglobulin genes were removed (see Methods) before
beginning the analysis, since these are particularly susceptible to
false positive calls.
We first tested the Aberdeen, Munich and US samples
separately, and also separately compared a) deletions, b)
duplications, and c) genomic regions affected by both types of
CNV. The Munich cohort had 1,299 SNPs affected by deletions
(with frequency ranging from 0.4% to 15%), 1,042 SNPs affected
by duplications (0.04%–9.3%) and 202 SNPs affected by both
deletions and duplications (in different subjects; frequency 0.5%–
9.4%). The equivalent values for Aberdeen were 3,879 deletions
(0.34%–18.9%), 2,634 duplications (0.34%–17.5%) and 1,016
SNPs affected by both (0.45%–62%) and for the US cohort, 2,702
deletions (0.72%–46.7%), 3,159 duplications (0.72%–21%) and
1,399 SNPs affected by both (1.0%–58%). It should be noted that
these CNVs have not been individually validated either by
inspection in BeadStudio or by any experimental means, and
many of these are likely to be false positives. The three cohorts
differed both in the CNVs that most strongly associated and in the
direction of the effects. There were no events that were
significantly associated (p,0.05) in more than one cohort.
To test for effects of common CNVs that may not be well
captured by PennCNV calling algorithms from Infinium Human-
Hap SNP data, we next examined a set of SNPs shown to tag copy
number polymorphisms (CNPs), as defined in McCarroll et al. [42]
and provided by Drs. McCarroll and Altshuler (personal
communication). Of 285 total HapMap tagging SNPs, we found
that 169 and 202 of them were either directly represented or had a
proxy of r
2
$0.8 on the HumanHap310 and Human610-quad
BeadChips, respectively. We then specifically examined the
association statistics for these CNP-tagging SNPs in Aberdeen
and Munich (combined) and in the US cohort. None of the
association p values stood up to correction for the total SNPs
tested, neither did any CNP-tagging SNP associate at p,0.05
(uncorrected) in both populations. We can therefore provide no
evidence for the role of common CNVs in schizophrenia.
However, it should be noted that many common CNVs cannot
be detected using the HumanHap300 and 550 genotyping
platforms [42], so it is not possible for us to conclusively rule out
effects of common CNVs on schizophrenia.
We then searched for very highly penetrant schizophrenia loci
that were present in one or more cohorts. We did this by
performing Fisher’s exact test on all three cohorts combined (see
Methods) and searching for significantly associated events that
were present only in cases, and that occurred in cases from at least
two cohorts. According to these criteria, we found 4 associated
regions of deletion and 3 associated regions of duplications. After
excluding regions that were common in the DGV, we were left
with just one region of interest: chr11:112772031–112778135,
with six deletions in the Aberdeen cohort (one 40 kb from
112,744,722–112,784,640, two 23 kb from 112761718–
112784640, two 7 kb from 112772032–112779220 and one
6.1 kb from 112772032–112778135) and one in the US cohort
(9.3 kb, from 112775371–112784640). These positions were
confirmed by visual inspection in BeadStudio. This region
included the 39end of the ANKK1 gene and was immediately
downstream of the DRD2 gene, a target of all antipsychotic drugs.
It is possible that this region could contain regulatory regions
relevant to the dopamine D2 receptor function, and the functional
effects of this CNV, as well as its presence in other cohorts, should
be further investigated. It should be noted, however, that 1/1,547
cognitively normal subject in the extra control cohort also carried
this deletion, so if it is a risk factor it has incomplete penetrance.
Inspection of previously reported schizophrenia-
associated CNVs. Using comparative genomic hybridization,
Kirov et al. reported 13 possible schizophrenia associated CNVs
[40]. One of these, a 1.4 Mb duplication including APBA2 and
other genes was also found in our cohort and has already been
discussed. The other CNV they designated as most likely to be
pathogenic was a 250 kb deletion of 2p16.3 that included the 59
end of NRXN1, a gene also implicated in two other recent studies
[43,44]. In our cohort we had three large deletions that
encompassed the 39end of NRXN1 (200 kb in US, 260 kb and
420 kb in Munich), providing further evidence for this region in
Table 5. Count of European-ancestry samples with one or more rare gene-affecting CNV that is greater than 100 kb and includes
20 or more SNPs.
Aberdeen Munich Meltzer/memory
deletion duplication deletion duplication deletion duplication
case control case control case control case control case control case control
Has 1 or more CNV that affects
a gene
91 66 61 49 30 29 61 36 13 11 20 43
Total 441 439 441 439 422 381 422 381 161 267 150 264
Fisher’s 1-tailed p value 0.034 0.112 0.788 0.030 0.079 0.897
doi:10.1371/journal.pgen.1000373.t005
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 12 February 2009 | Volume 5 | Issue 2 | e1000373

schizophrenia pathology. (It should be noted, however, that we
also observed NRXN1 deletions in the cognitive normal extra
control cohort, so all deletions in this gene should not be assumed
to be pathogenic.) They also reported a 240 kb duplication
encompassing the EFCAB2 and KIF26B genes, and we found a
575 kb duplication of this region in one African-American patient.
Thirdly, we also found two large duplications encompassing a
reported 640 kb non-genic duplicated region on 4q35.2: 1.26 Mb
in Munich and 1.28 Mb in Aberdeen. In summary, of 13 reported
schizophrenia associated CNVs, we were able to find supporting
evidence for 4 in our cohorts, including both of their highest-
confidence regions [40]. Another study reported deletions of
CNTNAP2 in schizophrenic patients [45], but we did not find any
large events in this gene, and smaller deletions were present in
both cases and controls. We next searched our cohorts for any of
the 21 novel autosomic schizophrenia events reported by Walsh et
al. [21]. We have already reported 2 Aberdeen and 1 US subject
with the 1.4 Mb deletion on chromosome 1, which is now a known
recurrent schizophrenia risk factor [22,24]. Of the remaining 20,
we found only 1 event that reoccurred in our samples: 2 Aberdeen
cases had a 664 kb duplication at chr2:48625109–49290093. In a
fourth study, Stone et al. (also using the Aberdeen cohort,
genotyped with a different platform) reported the presence of
duplications in the NOTCH1 and PAK7 genes in cases only [22].
We did not find any duplications in NOTCH1, however we found
one deletion in an Aberdeen sample and three deletions in cases
from the US cohort, and none in controls. We also found one
duplication in PAK7 in a US case, and none in controls. Finally, a
small study using genome-wide SNP discovery on 54 patients with
deficit schizophrenia found and validated four rare schizophrenia-
associated CNVs [44], two deletion affecting NRXN1 (see above),
and ASTN2, and two duplications affecting 4 and 7 genes
respectively, at 2p16.3 and 5p15.2. In ASTN2 we saw three very
small deletions in Munich cases and two small deletions in a
Munich and an Aberdeen control. In the 2p16.3 region
(chr2:859,616–1,826,716), we found a 441 kb deletion in a
Munich case, and two large duplications in a control (250 kb
and 237 kb), from 836164–1086540 and 1589418–1826014. This
indicates that rare duplications in this area are not highly-
penetrant schizophrenia risk factors. At 5p15.2 (chr5:10,270,604–
11,200,814) we observed only 4 very small deletions, all in Munich
controls.
Analysis of pathways affected by gene-disrupting CNVs in
cases and controls. After finding an excess of rare gene-
disrupting CNVs greater than 100 kb in their schizophrenia
patients, Walsh et al. [21] went on to investigate the genes using the
Ingenuity Pathway Analysis (IPA) classification system to check in
cases for an excess of disrupted genes from any particular
functionally defined molecular pathway. They found a
significant over-representation of genes in pathways important
for brain development. We performed the same analysis on the
genes that were disrupted by rare CNVs greater than 100 kb in
our cohorts (Table S3). We did not find a strong overlap with the
pathways enriched in Walsh et al. [21], nor did we find common
pathways disrupted in cases across the three cohorts we examined.
Discussion
While our genome-scan identified no definitive associations
between SNPs and schizophrenia risk, SNPs in the ADAMTSL3
gene were the most strongly associated. One of the ADAMTSL3
SNPs is clearly functional and influences the proportion of two
alternative transcript species. Nevertheless, study of additional
cohorts strongly suggested that ADAMTLS3 is not related to
schizophrenia risk and that functional evidence should not be used
to strengthen claims for modestly associated variants.
We also failed to replicate any of the SNPs previously identified
in either genome-wide association studies or candidate gene
studies as schizophrenia risk factors. Notably, not only did none of
these show genome-wide significance, none showed significant
evidence even if we corrected only for the 782 SNPs from
previously associated candidate genes.
We have calculated here that by using genome-wide genotyping
in the discovery cohorts and then typing the top 100 SNPs in the
first replication cohort, we have 80% power to detect an allele with
MAF$0.1 at an odds ratio of 1.8 or larger. This is one of the
largest whole genome association studies reported for schizophre-
nia and is similar in size or larger than studies that have
successfully identified risk factors for common diseases [46,47].
While our sample size is not large enough to identify risk factors of
small effect in a genome-wide context, we have introduced an
approach for assessing whether our negative results are consistent
with a model in which common SNPs explain most of the
heritability of schizophrenia. While this analysis shows that we
cannot rule out such a possibility, it would require a large number
of SNPs and an implausible genetic model.
There are a number of possible explanations for the lack of
compelling genetic associations in schizophrenia GWAS to date.
Firstly, it has been postulated that schizophrenia is not an
homogenous condition, but is in fact a group of several different
genetically heterogeneous syndromes that are classed together due
to overlap of particular diagnostic symptoms [48]. This hypothesis
may be tested in the near future with the publication of
collaborative datasets combining several thousand cases and
controls, which will presumably include reasonable sample sizes
of each subtype. However, in order to distinguish between the
presence of reasonable effect sizes in subgroups of the patients and
very small effect sizes common to all, it will be necessary to
examine more detailed information about the patients including
symptoms, putative disease subtypes, and perhaps measures of
cognition and other endophenotypes.
Similarly, large datasets would also be necessary to investigate
the possibility that common variants exert their effects only
through interaction with other genetic variants, a plausible
hypothesis that it has not yet been possible to investigate in any
powerful way. Alternatively, genetic risk factors may interact so
strongly with the environment that their marginal effects over all
environments are too low to detect in a sample of this size. Further
investigation of this will require much larger, longitudinal studies
of patients and controls.
The final possibility is that much of schizophrenia risk is due to
rare, moderate-to-high penetrance variants whose population
frequencies reside somewhere below the threshold of detection of
genome-wide screens. Due to the complex patterns of schizophre-
nia heredity, and the relative lack of families with Mendelian
schizophrenia syndromes, this cannot account fully for schizo-
phrenia susceptibility. On balance, however, the data presented
here are most consistent with this interpretation. First, we find no
evidence of association for common SNPs, but clear evidence that
a fraction of cases are due to very rare, very highly penetrant
structural variants. One interpretation of this pattern is that
selection for reliable cognitive function has been sufficiently strong
to keep the genome free of common variants that predispose to
schizophrenia, and that it is only rare deleterious variants that
influence risk [49]. This model for schizophrenia genetics presents
clear challenges to the hope that genetics will rapidly reveal new
therapeutic opportunities or partition patients up into a small
number of clinically manageable subgroups.
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 13 February 2009 | Volume 5 | Issue 2 | e1000373

More encouragingly, however, despite the rarity of these events,
we nonetheless replicated several specific regions from previous
studies that were not known to be recurrent schizophrenia-
associated CNVs, including those affecting APBA2 and the
surrounding region [40]. Additionally, we have implicated new
regions, including a large deletion at 16p13.11-p12.4 that may be
an important risk factor for other neuropsychiatric conditions.
This region also intimates at the possibility that while patients may
have different genetic contributors, some of the different events
may point towards the same pathway, given that the 16p region
includes a gene known to encode a binding partner of DISC1, a
gene with confirmed involvement in schizophrenia. These
observations suggest that a full catalogue of rare determinants of
schizophrenia could identify a number of specific genomic regions
or events that unite a fraction of patients as having the same or
similar underlying causes. These subgroups of patients can be
further investigated to see if the genetic contributors can elucidate
the molecular mechanisms underlying particular symptoms or
drug-response phenotypes.
Finally, even if most of the schizophrenia risk is due to rare
relatively highly penetrant causes, it seems unlikely this would all
be structural. It is likely that rare single site changes disrupting
gene function must contribute as well, and will likely only be
determined through full genome resequencing, which must be
considered a goal for future schizophrenia genomic research.
Methods
Ethics Statement
All cases and controls gave informed consent. The study was
approved by both local and multiregional academic ethical
committees.
Discovery Cohort
The SNP discovery cohort consisted of two distinct sub-cohorts.
The first cohort comprised 439 schizophrenia patients (age
39.2610.4 yr, range 19–70) and 418 healthy controls (age
48.8614.7 yr, range 22–75), all self-identifying as of German or
central European ancestry and collected in Munich. The second
cohort comprised 461 schizophrenia patients and 459 controls, all
self-identifying as of Scottish or north European ancestry, collected
in Aberdeen, Scotland. Critically, patients for the two cohorts were
selected using a consistent clinical protocol. To be enrolled as a
case, participants must have had both a DSM-IV and an ICD-10
diagnosis of schizophrenia [50]. In the Munich and Aberdeen
cohorts respectively, subtypes were observed in the following
proportions: paranoid 77.6% and 86.2%, disorganized 15.6% and
7.5%, catatonic 2.2% and 2.1% and undifferentiated 4.6% and
4.2%. Detailed medical and psychiatric histories were collected,
including a clinical interview using the Structured Clinical
Interview for DSM-IV (SCID), to evaluate lifetime Axis I and II
diagnoses. Cohen’s Kappa (Cohen, 1960 [51]) of 0.80 indicated
good inter-rater reliability. Exclusion criteria included a history of
head injury or neurological diseases. All case participants were
outpatients or stable in-patients. Further details of the Munich
cohort and protocol are available in Van den Oord et al. (2006)
[52]. All cases and controls gave informed consent. The study was
approved by both local and multiregional academic ethical
committees.
Healthy volunteers were randomly selected from the general
population both for the Munich and Aberdeen cohorts (ascer-
tained by mail for Munich, and by general practitioners for
Aberdeen). In the Aberdeen study volunteers were screened for
absence of psychiatric disorders and only those with no major
psychiatric episodes or major mental illness in a first degree
relative were included in the study. In the Munich cohort several
screenings were conducted before the volunteers were enrolled in
the study in order to exclude subjects with central neurological
diseases and psychotic disorders or subjects who had first-degree
relatives with psychotic disorders. First, subjects who responded
were screened by phone for the absence of neuropsychiatric
disorders. Second, detailed medical and psychiatric histories were
assessed for the volunteers and their first-degree relatives using
systematic forms. Third, if no exclusion criteria were fulfilled, they
were invited to a comprehensive interview including the SCID
[52] to validate the absence of psychotic disorders. Finally, a
neurological examination was conducted to exclude subjects with
current CNS impairment. In the case that the volunteers were
older than 60 years, the Mini Mental Status Test [53] was
performed to exclude subjects with possible cognitive impairment.
The enrolment procedure was similar for the Aberdeen controls,
although a formal SCID was not undertaken.
First Replication Cohort
The first replication cohort comprised 298 schizophrenia
patients (age 37.3611.8 yr, range 18–66) and 713 healthy controls
(age 45.5616.1 yr, range 19–72). The recruitment protocol is
identical to that used for the Munich discovery sample.
Schizophrenia subtypes were observed in the following propor-
tions: paranoid 75.5%, disorganized 16.1%, catatonic 4.7% and
undifferentiated 3.7%.
Second Replication Cohort
The sample comprised a total of 918 subjects of whom 394
(mean age6SD 43.5612.8 years, range 19–80) had a DSM-IV-
TR [50] diagnosis of schizophrenia and 524 (mean age6SD
47.3629.7 years, range 19–87) were healthy controls. Patients and
controls were of Caucasian ancestry for at least two generations,
lived in northern Italy, were unrelated to other participants, and
fulfilled predefined group-specific inclusion and exclusion criteria.
The different subtypes of schizophrenia were observed as follows:
paranoid 61.9%, undifferentiated 17.0%, residual 10.4%, disor-
ganized 9.4%, catatonic 1.3%. The patients were enrolled from
those voluntarily admitted to the Brescia IRCCS Fatebenefratelli.
The inclusion criteria were a DSM-IV-TR diagnosis of schizo-
phrenia [54] and a level of understanding and attention judged
sufficient to give true informed consent; a lifetime comorbidity
with other DSM-IV-TR Axis I disorders was an exclusion
criterion. All participants underwent detailed clinical interviews,
implemented, when required, by DSM-IV-TR adjusted versions of
the Structural Clinical Interview for DSM-IV Axis I Disorders.
Moreover, to attribute the schizophrenia subtype, a checklist of the
symptoms dominating the clinical picture at the screening visit and
in the previous 4 weeks was used.
All patients and controls enrolled in the study provided a written
informed consent approved by local Ethical Committee (CEIOC,
Brescia, Italy). A concise, but unequivocal explanation about the
aims of the study was included on the written consent form.
The healthy unrelated participants were recruited through
different sources (randomly selected among university, consenting
doctors, nurses, employees and attendants of Brescia IRCCS
Fatebenefratelli and elderly association). All participants under-
went a psychiatric interview to exclude Axis I disorders and Axis I
diagnosis of first-degree relatives. Absence of relevant neurological
diseases was mandatory for the inclusion in the study. The Mini
Mental Status Test 29 was performed to subjects older than 65
years, to exclude possible cognitive impairment.
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 14 February 2009 | Volume 5 | Issue 2 | e1000373

Third Replication Cohort
For the third replication, we used information provided by Dr.
Hreinn Stefa´nsson by personal communication, with reference to
the deCODE samples used in previous publications, e.g. [24].
US Cohort Used for CNV Analysis and as Fourth SNP
Replication Cohort
The US patients were part of an NIMH-funded Clinical
Research Center at Case Western Reserve University and
prospective clinical trials at Vanderbilt University. Information
about recruitment and assessment has been previously reported
[55,56]. The healthy controls were recruited as part of the
Genetics of Memory/ Genetics of Epilepsy studied at Duke. All
subjects were cognitively normal and free of neuropsychiatric
disorders.
Extra Control Cohort (used to Search for Deletions
.2 Mb)
The extra 1,547 controls were also part of the Genetics of
Memory/ Genetics of Epilepsy studied at Duke and genotyped in
the same facility using the Illumina Infinium HumanHap 550K,
and subject to identical quality control procedures. They
comprised healthy controls who performed normally in a series
of cognitive tests (age range = 18–85, mean = 25.5, median = 22).
The majority were of European origin but also included were
approximately 10% each of African-American, East Asian (mostly
Chinese) and South Asian (mostly Indian) as well as 5% Hispanic.
Genotyping and Quality Control
The Munich cohort was genotyped using the Illumina
HumanHap300 chip with a total of 317,503 SNPs and the
Aberdeen cohort was genotyped using the Illumina Human-
Hap550 chip with a total of 555,352 SNPs. We carried out a series
of quality control (QC) checks and tests of cryptic relatedness,
ultimately excluding a total of 15 and 28 participants in Munich
and Aberdeen respectively (Text S1). We also employed a ‘‘one
percent rule’’ that discarded from analysis any SNP that had more
than 1% of samples that could not be reliably scored, to reduce the
scope for spurious association. After employing this rule the
average success rate of genotyping was 98.4% and the concor-
dance rate for duplicate genotyping was 99.997%. The US cohort
was genotyped using the Human-610 Quad Beadchip at the
Institute for Genome Sciences and Policy Genotyping Core, and
the same quality control procedures were applied as those used for
the discovery cohorts.
Association Analyses and Correction for Population
Structure
Our core association analyses to identify schizophrenia risk
factors focused on single-marker tests of the 312,565 QC-passed
SNPs that were genotyped in both cohorts. To control for the
possibility of spurious associations resulting from population
stratification we used the EIGENSTRAT approach of Price et al
[57]. This method derives the principal components of the
correlations among gene variants and corrects for those correla-
tions in the association tests. In principle, therefore the principal
components in the analyses should reflect population ancestry. We
have noticed however that some of the leading axes appear to
depend on other sources of correlation, such as sets of variants
near one another that show extended association. We have
documented the potential for inversions to create this effect and it
may be created by other causes of extended linkage disequilibrium
as well (Text S1). For this reason we inspected the SNP ‘loadings’
for each of the leading axes to determine if they depended on
many or relatively few SNPs, as would be expected if the given axis
reflected population ancestry or a more localized linkage
disequilibrium effect respectively. This analysis identified several
axes clearly due to inversions and suggested that four axes should
be retained for ancestry adjustment (Text S1). We therefore
assessed significance using four principal components emerging
from the EIGENSTRAT analyses as covariates in a logistic
regression model which also incorporated sex as a covariate and
combined samples from Munich and Aberdeen (a division which
clearly drove the first EIGENSTRAT axis).
Bayesian Analysis of Posterior Odds of Association
Following Wakefield [29], we found the estimated log-odds for
association, h
_
, under a multiplicative genetic model for rs2135551,
together with its estimated variance V, from standard logistic
regression of each dataset. Given a prior odds of PO for the
association being true, and a prior distribution of ,N(m,W) for h
under the hypothesis of true association, we found the posterior
odds having observed new data at each stage as
PO|ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
V=VzWðÞ
pexp h
_22V{h
_
{m

22VzWðÞ

,and
updated the posterior distribution of hunder the hypothesis of true
association as *NVmzWh
_
.
VzWðÞ,VW =VzWðÞ

.We
then entered these posteriors as priors into the analysis of the next set
of data. To start, we set PO = 1/100000 following the Wellcome
Trust Case Control Consortium [58] (i.e., assuming a million
independent regions of the genome and 10 detectible causal loci for
schizophrenia), and following Wakefield, 2007 [29] we set m=0 and
W = (log(1.5)/1.96)
2
(i.e., assuming that 95% of all casual effects fall
between 2/3 and 3/2 per allele under a multiplicative genetic model).
Alternative Splicing
Alternative transcripts were identified searching ExonHit
Therapeutics SpliceArray portal (http://portal.splicearray.com)
and blasting exon-intron boundary sequences against human
cDNA libraries. For semi-quantitative evaluation of transcript
ratio differences, primers flanking the common 59splice donor site
in exon 29 (forward primer: 59-TTGGGCCCTCCTGTGATA-
39, location shown in Figure S1A) and the alternative 39splice
acceptor site in exon 30 (reverse primer: 59-TGGCAG-
CACCTTTGTTTGTA-39, location shown in Figure S1A) were
used to simultaneously amplify all four transcript forms (Figure
S1A). The fragments were separated on a 3.5% NuSieve agarose
gel and direct sequencing was used to confirm expected transcript
forms. Taqman-based real time PCR was used to quantitatively
determine ratios of alternative transcripts in human brain tissue.
Assays were custom designed through Applied Biosystems by
targeting unique exon-exon boundaries (for primer and probe
sequences see Text S1). b-actin mRNA expression level was
quantified using a commercially available Taqman assay (Applied
Biosystems). Fluorescence outputs were quantified in real time
using a 7900HT Fast Real Time PCR System and the data were
analyzed using SDS software v.2.2.2 (Applied Biosystems). One
way analysis of variance was used to determine the correlation of
alternative transcript abundance with the rs950169 and rs2135551
genotypes in human brain tissue. Statistical analyses were
performed both separately in control and Alzheimer’s disease
prefrontal cortex samples, and as a combined subject analysis. A
genomic DNA fragment of 4028 bp from the ADAMTSL3 gene
that included exons 29 and 30 with flanking intron sequences was
PCR-amplified from a reference genomic DNA using the
following primers: gggaattcAAGGGCAGATACCCCAAAGT
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 15 February 2009 | Volume 5 | Issue 2 | e1000373

and taggatccCGCTTGCTCTTCCAACTACC. Subsequently,
the PCR fragment was subcloned into pSPL3 (GibcoBRL) as a
minigene. The minor allele of rs950169 was generated in the
minigene by mutagenesis (QuikChange Mutagenesis kit, Strata-
gene) and the sequences were confirmed by DNA sequencing. The
minigenes were transfected into HEK293 cells using Lipofecta-
mine2000 (Invitrogen). After the 48 h transfection, RNA was
extracted using RNeasy kit (Qiagen) and converted into cDNA
using High-Capacity cDNA Archive Kit (Applied Biosystems).
Alternative splicing of exon 29 and exon 30 was detected by
Taqman assays and agarose gel.
Power Calculations
Following Chapman et al. [59], we assumed that the test statistic
from a case-control trend test of association follows a non-central
chi-square distribution with 1 d.f. and non-centrality parameter
g=(n21)r
2
H, where nis the sample size, r
2
is the LD between the
causal SNP and it’s tag SNP on the GWAS genotyping panel, and
His the proportion of variation explained by the SNP if it were
typed directly. In a case-control setting,
H~2p1{pðÞp’{pðÞ
2.p1{pðÞ½, where Pis the proportion of
cases in the total sample, pis the frequency of causal alleles in
controls and in the general population (assuming a rare disease), p9
is the frequency of causal alleles in cases (p’~hp=1{pzhpðÞ
where his the allelic relative risk or odds ratio assuming a rare
disease), and pis the causal allele frequency in the study as a
whole. We simulated sets of 100,000 X
1
values from a Normal
distribution with mean = !g
1
and variance = 1, where g
1
is the
presumed non-centrality parameter from the GWAS study
(n= 1734, P= 0.506), and an additional set of 100,000 X
2
values
from a Normal distribution with mean = !g
2
and variance = 1,
where g
2
is the presumed non-centrality parameter from the first
replication study (n= 1011, P= 0.295). To score a ‘‘hit’’, we
required both that X
1
exceeded the upper critical value for a two-
tailed test at a= 0.0003 (to mimic being passed to the 1
st
replication stage), and that both X
1
and X
2
had the same sign and
had a joint P,1.6610
27
when combined using Stouffer’s
weighted-Z method [60] (to mimic achieving a Bonferroni-
corrected genome-wide significance level after both stages). Power
was defined as the number of hits divided by 10,000. Solutions to h
based on fixed values of the other parameters were found by an
iterative root-finding procedure (function ‘‘uniroot’’ in the R
statistical package, http://www.r-project.org/). The Total Lamb-
da-s expected based on kindependent SNPs each with a given OR
and MAF was found using equations in Camp et al. [61].
Generation of CNV Calls and QC
All subjects that passed SNP QC procedures were entered into
the CNV analysis. This comprised 892 samples from Aberdeen
(441 controls, 451 cases), 842 samples from Munich (412 controls,
430 cases) and 443 samples from the US (267 controls, 176 cases).
The CNV calls were generated using the PennCNV software
(version 2008jun26 version [62]) using the Log R ratio (LRR) and
B allele frequency (BAF) measures automatically computed from
the signal intensity files by BeadStudio, and the standard hg18
‘‘all’’ PennCNV hidden Markov model (hmm) and population
frequency of B allele (pfb) files for the 317 and 550 BeadChips. For
the samples genotyped on the 610-Quad BeadChips, we used the
hh550_610.hg18 pfb and gc model files separately provided by Dr.
Kai Wang to ensure inclusion of all CNV-specific markers.
Because many of the samples had below optimal genomic wave
QC values, for Aberdeen and Munich we implemented the gc
model wave adjustment procedure. We used the PennCNV checks
to exclude samples that failed quality control. These included
samples that had a LRR standard deviation .0.28, BAF
median.0.55 or ,0.45, BAF drift .0.002 or WF.0.04 or
,20.04. For the US cohort we found an excess of CNVs in
samples with LRR_SD values between 0.25 and 0.28, so the
LRR_SD cut-off was reduced to 0.25 for both cases and controls
from the US. All samples that failed QC after the wave adjustment
procedure were removed. Due to the complications of hemizy-
gosity in males and X-chromosome inactivation in females, all
analyses were restricted to autosomes. Additionally, to ensure that
we were working with high-confidence CNVs, we excluded any
CNV for which the difference of the log likelihood of the most
likely copy number state and the less likely copy number state was
less than 10 (generated using the -conf function in PennCNV).
Finally, some centromeric and telomeric regions are not well
mapped, and this can potentially result in CNV-calling errors in
these regions (Dr. Kai Wang, personal communication). Also,
genomic regions coding for immunoglobulin genes have previously
been shown to be potential sites of false-positive PennCNV calls
[62]. Our own research has shown that calls in both of these types
of region differed significantly depending on the sample type used
for DNA extraction (significant difference p,10
210
for deletion
and/or duplication frequencies between samples genotyped on
DNA extracted from blood or saliva, data not shown). We
therefore excluded any CNV that overlapped any of the following
regions by 50% or more of its length: chr2: 87.0–92.0, chr14: 18–
23.6 Mb, chr14: 104.5–106.5 Mb, chr15: 17.0–21.0, chr16: 31.8–
36.0 Mb, chr22: 20.5–21.8 Mb (immunoglobin regions); chr1:0–
4 Mb, 240–247 Mb; chr2: 87.0–92.0 Mb; chr4: 0–1.43 Mb,
48.75–49 Mb, 190.7–191.3 Mb; chr7:0–200 kb, 56.5–62.5 Mb;
chr8: 39–45 Mb, 145–146.3 Mb; chr9: 44.5–70.1 Mb; 138–
140.2 Mb; chr10: 38.5–42 Mb, 134–135.4 Mb; chr11: 0–
1.8 Mb; chr14: 18–23.6 Mb, 104.5–106.5 Mb; chr15: 17.0–
21.100–100.3 Mb; chr16: 0–2.1 Mb, 31.8–36.0 Mb, 86.6–
88.9 Mb; chr17:0–1 Mb, 76.5–78.8 Mb; chr18: 14–16 Mb,
75.5–76 Mb; chr19: 0–2.1 Mb, 25.7–28.3 Mb, 61.5–62.5 Mb;
chr20: 25.7–28.3 Mb, 61.5–62.5 Mb; chr21:9.7–14.3 Mb;
chr22:14.4–14.7 Mb, 20.5–21.8 Mb (centromeric and telomeric
regions, some overlapping immunoglobin regions as above). We
also removed CNVs that spanned centromeres by searching for
those larger than 1 Mb with fewer than 50 SNPs and checking
their genomic locations.
Analysis of Rare CNVs Greater than 100 kb
Following Walsh et al., we defined rare copy number variations
as those with at least 100 kb in size, at least 20 SNPs and not
previously described the Database of Genomic Variants (DGV,
http://projects.tcag.ca/variation/; dgv18v6). Any previously de-
scribed event that had at least a 60% overlap with a newly
discovered event was considered ‘not rare’ and excluded from
further evaluation (for details see [21]). We then looked to see if
there was an increase in particular types of rare CNVs between
cases and controls using a 2-tailed Fisher’s Exact test to compare
number of cases versus number of controls with and without the
event.
Common CNV Analysis
In order to implement a genome-wide screen for the effect of
common CNVs on schizophrenia predisposition, the number of
deletions and duplications affecting each SNP was counted up and
compared between cases and controls using Fisher’s exact test. For
each population, separate analyses were done for deletions,
duplications and loci affected by both deletions and duplications.
To enter the deletion analysis, a SNP had to be deleted in 3 or
more samples and duplicated in fewer than 2 samples, for the
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 16 February 2009 | Volume 5 | Issue 2 | e1000373

duplication analysis a SNP had to be duplicated in 3 or more
samples and deleted in fewer than 2 samples and the third analysis
included all SNPs that are deleted in 2 or more samples and
duplicated in 2 or more samples. Events that only occurred in one
or two individuals were not analyzed. For the screen for
schizophrenia-specific recurring events, we performed the same
statistical test, and again stipulated that the event must occur in at
least three individuals, but this time we did not filter out sites that
were affected by duplications from the deletion analysis nor those
affected by deletions from the duplication analysis, in order to
maximize the search space for each test.
Pathway Analysis
Firstly, we screened the genes that are affected by the rare
CNVs greater than 100 kb (described above). To do this we
mapped all genomic coordinates for SNPs used in defining CNVs
into the most updated human genome variation build (Ensembl
variation build 50_36l, dbSNP build 129). We then aligned the
genomic coordinates of the rare CNVs with the most updated
human genome build (Ensembl core build 50_36l, human genome
build 36). Any protein-coding gene that was either broken by or
fully included in a rare CNV was considered ‘‘affected’’. We
detected the following gene counts that were affected by deletions
in cases and controls respectively in the different populations:
Aberdeen: 407, 210; Munich: 109,55; US: 70,159;and for
duplications, Aberdeen: 294,180; Munich: 217,127; US: 34,17.
We then used Ingenuity Pathway Analysis (IPA, see [21]) to
perform a pathway enrichment (over-presenting) analysis sepa-
rately for the four groups of genes we detected. The statistical
significance was evaluated using Fisher’s exact test. To avoid false
positives, we further stipulated that at least two genes in a pathway
must be disrupted for that pathway to be considered enriched, in
addition to the P values from Fisher’s exact test.
Further information
Data availability. The results of this genome-scan are
released temporarily for this submission at URL:
http://people.genome.duke.edu/,dg48/samba/prjx89z/
DUKE_IGSP_PG2_SCHIZO.zip
These results can be directly loaded and annotated using the
custom-designed WGA (whole genome annotator) software,
WGAViewer:
http://www.genome.duke.edu/centers/pg2/downloads/
wgaviewer.php
These results will be publicly released through Mart for IGSP
Data from Association Studies (MIDAS) once published. MIDAS
can be directly accessed using the WGAViewer software.
Supporting Information
Figure S1 Schematic of exons 29–30 of the ADAMTSL3 gene and
evidence of the presence of four alternative transcripts. A. Exons 29
and 30 are depicted as blue boxes and the PLAC domain coding part
of gene is striped. The alternative splice donor site (ASD) and
alternative splice acceptor site (ASA) are indicated by the black lines,
while the locations of ADAMTSL3 rs950169 and rs2135551 are
indicated by the red stars. We also resequenced the indicated region
and found no new candidate causal polymorphisms. We did however
find a new rare variant (IVS29+5G.A, indicated by a red star) in the
reference splice donor site (in the plus five position) which influences
the usage of the reference donor site (data not shown). This variant
showed a frequency of less than one percent in our cohort and was
therefore too rare to properly assess any possible contribution of this
new splicing variant to schizophrenia risk, although a protective trend
was observed (data not shown). The black arrows represent location
of primers used for semi-quantitative evaluation of alternative
transcript ratios. Schema is not to scale. B. The amino acid sequence
of ADAMTSL3 proteinPLACdomainandpredictedeffectof
alternative splicing of exons 29 and 30. PLAC domain characteristic
cysteines are highlighted. C. Evidence of the presence of four
alternative ADAMTSL3 transcripts in human brain tissue samples
with different ADAMTSL3 rs950169 and IVS29+5G.A genotypes.
Lane 1: 100 bp ladder; Lanes 2 and 3: rs950169: CC,
IVS29+5G.A: GG; Lanes 4 and 6: rs950169: CT, IVS29+5G.A:
GG; Lanes 5 and 7: rs950169: TT, IVS29+5G.A: GG; Lanes 8 and
9: rs950169: CT, IVS29+5G.A: GA.
Found at: doi:10.1371/journal.pgen.1000373.s001 (1.85 MB TIF)
Figure S2 Evidence of the genetic control of ADAMTSL3 exon
30 alternative splicing in human brain tissue. Correlation of the
ADAMTSL3 rs950169 genotype with the relative abundance of
transcripts containing shorter exon 30 due to usage of alternative
splice acceptor site (RSD-ASA) and full reference exon 30 (RSD-
RSA) in the prefrontal cortex of control (N) and Alzheimer’s disease
brain tissue (e). Bars indicate means of combined controls and
Alzheimer’s disease patients transcript abundance values.
p,0.0001, ANOVA, combined controls and Alzheimer’s disease
patients p,0.0001, ANOVA, separate analyses of controls and
Alzheimer’s disease patients.
Found at: doi:10.1371/journal.pgen.1000373.s002 (0.48 MB TIF)
Figure S3 Evidence of the genetic control of ADAMTSL3 exon 30
alternative splicing in human brain tissue. Correlation of the
ADAMTSL3 rs950169 genotype with the relative abundance of
transcripts in the prefrontal cortex of control (N) and Alzheimer’s
disease brain tissue (e). Bars indicate the means of combined controls
and Alzheimer’s disease patients transcript abundance values. A.
Effect on alternative splice acceptor site in relation to alternative
splice donor site. B. Effect on alternative splice donor site in relation
to alternative splice acceptor site. C. No effect on alternative splice
donor site in relation to reference splice acceptor site.
Found at: doi:10.1371/journal.pgen.1000373.s003 (0.79 MB TIF)
Figure S4 The expression of ADAMTSL3 in the mouse forebrain
as depicted in the Allen Brain Atlas. Highlighted is high expression
in the pyramidal cell layer of the hippocampal formation
(including CA1 and CA3 regions). We used information provided
by the Allen Mouse Brain Atlas (http://www.brain-map.org) to
determine the likelihood that a gene classified as showing
expression in the brain at some point in development would show
the same pattern of expression in the mouse brain as found in
ADAMTSL3. Only 1.4% of all such genes (893/20598) showed
clustered expression in the hippocampus.
Found at: doi:10.1371/journal.pgen.1000373.s004 (7.32 MB TIF)
Table S1 Association results in this dataset presented for the
Aberdeen cohort for loci implicated in O’Donovan et al. [1].
Found at: doi:10.1371/journal.pgen.1000373.s005 (0.04 MB
DOC)
Table S2 Association results in this dataset for SNPs previously
implicated in schizophrenia GWAS studies.
Found at: doi:10.1371/journal.pgen.1000373.s006 (0.15 MB
DOC)
Table S3 Pathway analysis for genes disrupted by large rare
copy number variations in schizophrenia patients and controls.
Found at: doi:10.1371/journal.pgen.1000373.s007 (0.12 MB
DOC)
Text S1 Supplementary methods and results.
Found at: doi:10.1371/journal.pgen.1000373.s008 (0.09 MB PDF)
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 17 February 2009 | Volume 5 | Issue 2 | e1000373

Acknowledgments
We would like to thank Dr. Hreinn Stefa´ nsson of DeCODE genetics for
making available association data for the top ADAMTSL3 SNPs. Dr Kai
Wang was extremely helpful in answering questions about the PennCNV
software. We thank Drs. David Altshuler and Steve McCarroll for sending
us the CNP tagging SNPs. We also thank Dr. James S. Albert for assessing
expression patterns in mouse brain, Dr. Suneel Apte for comments on the
manuscript and advice, and the IGSP and Duke Medical Center for
support for this work. Finally, we thank all co-workers at the Department of
Psychiatry, LMU Munich for their excellent contribution to the clinical
characterization of the subjects and the laboratory work. Recruitment of
the patients from Munich was partially supported by GlaxoSmithKline
(GSK). We are grateful to the Genetics Research Centre GmbH, an
initiative by GSK and LMU, for genotyping the Munich replication
samples. We thank Edward and Helen Hintz, Robert and Lee Peterson,
and Robert and Fran Weissman (Ritter Foundation) for support of the US
patient component.
Author Contributions
Conceived and designed the experiments: ACN DG DBG. Performed the
experiments: ACN DG ELH KVS WY DK. Analyzed the data: ACN DG
MEW SF. Contributed reagents/materials/analysis tools: DG JM SF MG
WJS CB GR KJ PAC JPM RSEK EMCF PLSJ IG AMH HJM AR GF
CC LTM DSC ADR PM CF DR HYM. Wrote the paper: ACN DG
MEW HYM DBG.
References
1. Milev P, Ho B-C, Arndt S, Andreasen NC (2005) Predictive Values of
Neurocognition and Negative Symptoms on Functional Outcome in Schizo-
phrenia: A Longitudinal First-Episode Study With 7-Year Follow-Up.
Am J Psychiatry 162: 495–506.
2. Green MF, Kern RS, Heaton RK (2004) Longitudinal studies of cognition and
functional outcome in schizophrenia: implications for MATRICS. Schizophre-
nia Research 72: 41.
3. Fleischhacker WW, Widschwendter CG (2006) Treatment of schizophrenia
patients: comparing new-generation antipsychotics to each other. Curr Opin
Psychiatry 19: 128–134.
4. Selten JP, Cantor-Graae E, Kahn RS (2007) Migration and schizophrenia. Curr
Opin Psychiatry 20: 111–115.
5. Pedersen CB, Mortensen PB (2001) Evidence of a dose-response relationship
between urbanicity during upbringing and schizophrenia risk. Arch Gen
Psychiatry 58: 1039–1046.
6. Weiser M, Noy S (2005) Interpreting the association between cannabis use and
increased risk for schizophrenia. Dialogues Clin Neurosci 7: 81–85.
7. Susser E, Neugebauer R, Hoek HW, Brown AS, Lin S, et al. (1996) Schizophrenia
after prenatal famine. Further evidence. Arch Gen Psychiatry 53: 25–31.
8. Davies G, Welham J, Chant D, Torrey EF, McGrath J (2003) A systematic
review and meta-analysis of Northern Hemisphere season of birth studies in
schizophrenia. Schizophr Bull 29: 587–593.
9. Sipos A, Rasmussen F, Harrison G, Tynelius P, Lewis G, et al. (2004) Paternal
age and schizophrenia: a population based cohort study. Bmj 329: 1070.
10. Takei N, Mortensen PB, Klaening U, Murray RM, Sham PC, et al. (1996)
Relationship between in utero exposure to influenza epidemics and risk of
schizophrenia in Denmark. Biol Psychiatry 40: 817–824.
11. Cardno AG, Gottesman II (2000) Twin studies of schizophrenia: from bow-and-
arrow concordances to star wars Mx and functional genomics. Am J Med Genet
97: 12–17.
12. Sullivan PF, Kendler KS, Neale MC (2003) Schizophrenia as a complex trait:
evidence from a meta-analysis of twin studies. Arch Gen Psychiatry 60:
1187–1192.
13. Sanders AR, Duan J, Levinson DF, Shi J, He D, et al. (2008) No significant
association of 14 candidate genes with schizophrenia in a large European ancestry
sample: implications for psychiatric genetics. Am J Psychiatry 165: 497–506.
14. Sullivan PF, Lin D, Tzeng JY, van den Oord E, Perkins D, et al. (2008)
Genomewide association for schizophrenia in the CATIE study: results of stage
1. Mol Psychiatry 13: 570–584.
15. Burmeister M, McInnis MG, Zollner S (2008) Psychiatric genetics: progress
amid controversy. Nat Rev Genet 9: 527–540.
16. Barrett JC, Cardon LR (2006) Evaluating coverage of genome-wide association
studies. Nat Genet 38: 659–662.
17. Lencz T, Morgan TV, Athanasiou M, Dain B, Reed CR, et al. (2007)
Converging evidence for a pseudoautosomal cytokine receptor gene locus in
schizophrenia. Mol Psychiatry 12: 572–580.
18. Shifman S, Johannesson M, Bronstein M, Chen SX, Collier DA, et al. (2008)
Genome-wide association identifies a common variant in the reelin gene that
increases the risk of schizophrenia only in women. PLoS Genet 4: e28.
19. Kirov G, Zaharieva I, Georgieva L, Moskvina V, Nikolov I, et al. (2008) A
genome-wide association study in 574 schizophrenia trios using DNA pooling.
Mol Psychiatry.
20. O’Donovan MC, Craddock N, Norton N, Williams H, Peirce T, et al. (2008)
Identification of loci associated with schizophrenia by genome-wide association
and follow-up. Nat Genet advanced online publication.
21. Walsh T, McClellan JM, McCarthy SE, Addington AM, Pierce SB, et al. (2008)
Rare structural variants disrupt multiple genes in neurodevelopmental pathways
in schizophrenia. Science 320: 539–543.
22. Stone JL, O’Donovan MC, Gurling H, Kirov GK, Blackwood DH, et al. (2008)
Rare chromosomal deletions and duplications increase risk of schizophrenia.
Nature.
23. Xu B, Roos JL, Levy S, van Rensburg EJ, Gogos JA, et al. (2008) Strong
association of de novo copy number mutations with sporadic schizophrenia. Nat
Genet.
24. Stefansson H, Rujescu D, Cichon S, Pietilainen OP, Ingason A, et al. (2008)
Large recurrent microdeletions associated with schizophrenia. Nature.
25. Mayo O (2007) The rise and fall of the common disease-common variant (CD-
CV) hypothesis: how the sickle cell disease paradigm led us all astray (or did it?).
Twin Res Hum Genet 10: 793–804.
26. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007)
PLINK: a tool set for whole-genome association and population-based linkage
analyses. Am J Hum Genet 81: 559–575.
27. Ge D, Zhang K, Need AC, Martin O, Fellay J, et al. (2008) WGAViewer:
Software for genomic annotation of whole genome association studies. Genome
Res.
28. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, et al. (2008)
Genome-wide association studies for complex traits: consensus, uncertainty and
challenges. Nat Rev Genet 9: 356–369.
29. Wakefield J (2007) A Bayesian measure of the probability of false discovery in
genetic epidemiology studies. Am J Hum Genet 81: 208–227.
30. Owen MJ, Craddock N, O’Donovan MC (2005) Schizophrenia: genes at last?
Trends Genet 21: 518–525.
31. Goldstein DB (2009) Common Variation and Human Traits. N Engl J Med in
press.
32. Risch N (1990) Genetic linkage and complex diseases, with special reference to
psychiatric disorders. Genet Epidemiol 7: 3–16; discussion 17–45.
33. Weiss LA, Shen Y, Korn JM, Arking DE, Miller DT, et al. (2008) Association
between microdeletion and microduplication at 16p11.2 and autism.
N Engl J Med 358: 667–675.
34. Bassett AS, Chow EW (2008) Schizophren ia and 22q11.2 deletion syndrome.
Curr Psychiatry Rep 10: 148–157.
35. Hoogendoorn ML, Vorstman JA, Jalali GR, Selten JP, Sinke RJ, et al. (2008)
Prevalence of 22q11.2 deletions in 311 Dutch patients with schizophrenia.
Schizophr Res 98: 84–88.
36. Burdick KE, Kamiya A, Hodgkinson CA, Lencz T, Derosse P, et al. (2008)
Elucidating the Relationship between DISC1, NDEL1, and NDE1 and the Risk
for Schizophrenia: Evidence of Epistasis and Competitive Binding. Hum Mol
Genet.
37. St Clair D, Blackwood D, Muir W, Carothers A, Walker M, et al. (1990)
Association within a family of a balanced autosomal translocation with major
mental illness. Lancet 336: 13–16.
38. O’Donovan MC, Kirov G, Owen MJ (2008) Phenotypic variations on the theme
of CNVs. Nat Genet 40: 1392–1393.
39. Makoff AJ, Flomen RH (2007) Detailed analysis of 15q11-q14 sequence corrects
errors and gaps in the public access sequence to fully reveal large segmental
duplications at breakpoints for Prader-Willi, Angelman, and inv dup(15)
syndromes. Genome Biol 8: R114.
40. Kirov G, Gumus D, Chen W, Norton N, Georgieva L, et al. (2008) Comparative
genome hybridization suggests a role for NRXN1 and APBA2 in schizophrenia.
Hum Mol Genet 17: 458–465.
41. Shi YY, He G, Zhang Z, Tang W, Zhang J Jr, et al. (2008) A study of rare
structural variants in schizophrenia patients and normal controls from Chinese
Han population. Mol Psychiatry 13: 911–913.
42. McCarroll SA, Kuruvilla FG, Korn JM, Cawley S, Nemesh J, et al. (2008)
Integrated detection and population-genetic analysis of SNPs and copy number
variation. Nat Genet.
43. Rujescu D, Ingason A, Cichon S, Pietilainen OP, Barnes MR, et al. (2008)
Disruption of the neurexin 1 gene is associated with schizophrenia. Hum Mol
Genet.
44. Vrijenhoek T, Buizer-Voskamp JE, van der Stelt I, Strengman E, Sabatti C, et
al. (2008) Recurrent CNVs disrupt three candidate genes in schizophrenia
patients. Am J Hum Genet 83: 504–510.
45. Friedman JI, Vrijenhoek T, Markx S, Janssen IM, van der Vliet WA, et al.
(2008) CNTNAP2 gene dosage variation is associated with schizophrenia and
epilepsy. Mol Psychiatry 13: 261–266.
46. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, et al. (2007) A genome-
wide association study of type 2 diabetes in Finns detects multiple susceptibility
variants. Science 316: 1341–1345.
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 18 February 2009 | Volume 5 | Issue 2 | e1000373

47. Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, et al. (2007) A genome-
wide association study identifies alleles in FGFR2 associated with risk of sporadic
postmenopausal breast cancer. Nat Genet 39: 870–874.
48. Fanous AH, Kendler KS (2008) Genetics of clinical features and subtypes of
schizophrenia: a review of the recent literature. Curr Psychiatry Rep 10:
164–170.
49. Gibson G, Goldstein DB (2007) Human genetics: the hidden text of genome-
wide associations. Curr Biol 17: R929–932.
50. (1994) Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and/or
International Classification of Diseases ICD-10. Washington DC: American
Psychiatric Association.
51. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol
Meas 20: 37–46.
52. Van den Oord EJ, Rujescu D, Robles JR, Giegling I, Birrell C, et al. (2006)
Factor structure and external validity of the PANSS revisited. Schizophr Res 82:
213–223.
53. Folstein MF, Folstein SE, McHugh PR (1990) Mini-Mental Status Test (German
version by Kessler J, Markowitsch HJ, Denzler P). Weinheim: Beltz.
54. (2000) Diagnostic and Statistical Manual of Mental Disorders, fourth ed., text
revised. Washington, DC: American Psychiatric Association.
55. Hagger C, Buckley P, Kenny JT, Friedman L, Ubogy D, et al. (1993)
Improvement in cognitive functions and psychiatric symptoms in treatment-
refractory schizophrenic patients receiving clozapine. Biol Psychiatry 34:
702–712.
56. Meltzer HY, Bobo WV, Roy A, Jayathilake K, Chen Y, et al. (2008) A
randomized, double-blind comparison of clozapine and high-dose olanzapine in
treatment-resistant patients with schizophrenia. J Clin Psychiatry 69: 274–285.
57. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, et al. (2006)
Principal components analys is corrects fo r stratification in genome-wide
association studies. Nat Genet 38: 904–909.
58. (2007) Genome-wide association study of 14,000 cases of seven common diseases
and 3,000 shared controls. Nature 447: 661–678.
59. Chapman JM, Cooper JD, Todd JA, Clayton DG (2003) Detecting disease
associations due to linkage disequilibrium using haplotype tags: a class of tests
and the determinants of statistical power. Hum Hered 56: 18–31.
60. Whitlock MC (2005) Combining probability from independent tests: the
weighted Z-method is superior to Fisher’s approach. J Evol Biol 18: 1368–1373.
61. Camp NJ (1997) Genomewide transmission/disequilibrium testing–consider-
ation of the genotypic relative risks at disease loci. Am J Hum Genet 61:
1424–1430.
62. Wang K, Li M, Hadley D, Liu R, Glessner J, et al. (2007) PennCNV: an
integrated hidden Markov model designed for high-resolution copy number
variation detection in whole-genome SNP genotyping data. Genome Res 17:
1665–1674.
SNPs and CNVs in Schizophrenia
PLoS Genetics | www.plosgenetics.org 19 February 2009 | Volume 5 | Issue 2 | e1000373