Biological insights from 108 schizophrenia-associated genetic loci

Journal name:
Nature
Volume:
511,
Pages:
421–427
Date published:
DOI:
doi:10.1038/nature13595
Received
Accepted
Published online

Abstract

Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.

At a glance

Figures

  1. Manhattan plot showing schizophrenia associations.
    Figure 1: Manhattan plot showing schizophrenia associations.

    Manhattan plot of the discovery genome-wide association meta-analysis of 49 case control samples (34,241 cases and 45,604 controls) and 3 family based association studies (1,235 parent affected-offspring trios). The x axis is chromosomal position and the y axis is the significance (–log10 P; 2-tailed) of association derived by logistic regression. The red line shows the genome-wide significance level (5×10−8). SNPs in green are in linkage disequilibrium with the index SNPs (diamonds) which represent independent genome-wide significant associations.

  2. Enrichment in enhancers of credible SNPs.
    Figure 2: Enrichment in enhancers of credible SNPs.

    Cell and tissue type specific enhancers were identified using ChIP-seq data sets (H3K27ac signal) from 56 cell line and tissue samples (y axis). We defined cell and tissue type enhancers as the top 10% of enhancers with the highest ratio of reads in that cell or tissue type divided by the total number of reads. Enrichment of credible causal associated SNPs from the schizophrenia GWAS was compared with frequency matched sets of 1000 Genomes SNPs (Supplementary Methods). The x axis is the –log10 P for enrichment. P values are uncorrected for the number of tissues or cells tested. A –log10 P of roughly 3 can be considered significant after Bonferroni correction. Descriptions of cell and tissue types at the Roadmap Epigenome website (http://www.roadmapepigenomics.org).

  3. Odds ratio by risk score profile.
    Figure 3: Odds ratio by risk score profile.

    Odds ratio for schizophrenia by risk score profile (RPS) decile in the Sweden (Sw1-6), Denmark (Aarhus), and Molecular Genetics of Schizophrenia studies (Supplementary Methods). Risk alleles and weights were derived from ‘leave one out’ analyses in which those samples were excluded from the GWAS meta-analysis (Supplementary Methods). The threshold for selecting risk alleles was PT<0.05. The RPS were converted to deciles (1 = lowest, 10 = highest RPS), and nine dummy variables created to contrast deciles 2-10 to decile 1 as the reference. Odds ratios and 95% confidence intervals (bars) were estimated using logistic regression with PCs to control for population stratification.

  4. Homogeneity of effects across studies.
    Extended Data Fig. 1: Homogeneity of effects across studies.

    Plot of the first two principal components (PCs) from principal components analysis (PCA) of the logistic regression β coefficients for autosomal genome-wide significant associations. The input data were the β coefficients from 52 samples for 112 independent SNP associations (excluding 3 chrX SNPs and 13 SNPs with missing values in Asian samples). PCAs were weighted by the number of cases. Each circle shows the location of a study on PC1 and PC2. Circle size and colour are proportional to the number of cases in each sample (larger and darker red circles correspond to more cases). Most samples cluster. Outliers had either small numbers of cases (‘small’) or were genotyped on older arrays. Abbreviations: a500 (Affymetrix 500K); a5 (Affymetrix 5.0). Studies that did not use conventional research interviews are in the central cluster (CLOZUK, Sweden, and Denmark-Aarhus studies, see Supplementary Methods for sample descriptions).

  5. Quantile-quantile plot.
    Extended Data Fig. 2: Quantile-quantile plot.

    Quantile-quantile plot of the discovery genome-wide association meta-analysis of 49 case control samples (34,241 cases and 45,604 controls) and 3 family based association studies (1,235 parent affected-offspring trios). Expected –log10 P values are those expected under the null hypothesis. Observed are the GWAS association results derived by logistic regression (2-tailed) as in Fig. 1. For clarity, we avoided expansion of the y axis by setting the smallest association P values to 10−12. The shaded area surrounded by a red line indicates the 95% confidence interval under the null. λGC is the observed median χ2 test statistic divided by the median expected χ2 test statistic under the null hypothesis.

  6. Linkage disequilibrium score regression consistent with polygenic inheritance.
    Extended Data Fig. 3: Linkage disequilibrium score regression consistent with polygenic inheritance.

    The relationship between marker χ2 association statistics and linkage disequilibrium (LD) as measured by the linkage disequilibrium score. Linkage disequilibrium score is the sum of the r2 values between a variant and all other known variants within a 1cM window, and quantifies the amount of genetic variation tagged by that variant. Variants were grouped into 50 equal-sized bins based on linkage disequilibrium score rank. Linkage disequilibrium score bin and mean χ2 denotes mean linkage disequilibrium score and test statistic for markers each bin. a, b, We simulated (Supplementary Methods) test statistics under two scenarios: a, no true association, inflation due to population stratification; and b, polygenic inheritance (λ = 1.32), in which we assigned independent and identically distributed per-normalized-genotype effects to a randomly selected subset of variants. c, Results from the PGC schizophrenia GWAS (λ = 1.48). The real data are strikingly similar to the simulated data summarized in b but not a. The intercept estimates the inflation in the mean χ2 that results from confounding biases, such as cryptic relatedness or population stratification. Thus, the intercept of 1.066 for the schizophrenia GWAS suggests that ~90% of the inflation in the mean χ2 results from polygenic signal. The results of the simulations are also consistent with theoretical expectation (see Supplementary Methods). λ is the median χ2 test statistic from the simulations (a, b) or the observed data (c) divided by the median expected χ2 test statistic under the null hypothesis.

  7. Enrichment of associations in tissues and cells.
    Extended Data Fig. 4: Enrichment of associations in tissues and cells.

    Genes whose transcriptional start is nearest to the most associated SNP at each schizophrenia-associated locus were tested for enriched expression in purified brain cell subsets obtained from mouse ribotagged lines41 using enrichment analysis described in the Supplementary Methods. The red dotted line indicates P = 0.05.

  8. MGS risk profile score analysis.
    Extended Data Fig. 5: MGS risk profile score analysis.

    Polygenic risk profile score (RPS) analyses using the MGS18 sample as target, and deriving risk alleles from three published schizophrenia data sets (x axis): ISC (2,615 cases and 3,338 controls)10, PGC1 (excluding MGS, 9,320 cases and 10,228 controls)16, and the current meta-analysis (excluding MGS) with 32,838 cases and 44,357 controls. Samples sizes differ slightly from the original publications due to different analytical procedures. This shows the increasing RPS prediction with increasing training data set size reflecting improved precision of estimates of the SNP effect sizes. The proportion of variance explained (y axis; Nagelkerke’s R2) was computed by comparison of a full model (covariates + RPS) score to a reduced model (covariates only). Ten different P value thresholds (PT) for selecting risk alleles are denoted by the colour of each bar (legend above plot). For significance testing, see the bottom legend which denotes the P value for the test that R2 is different from zero. All numerical data and methods used to generate these plots are available in Supplementary Table 6 and Supplementary Methods.

  9. Risk profile score analysis.
    Extended Data Fig. 6: Risk profile score analysis.

    We defined 40 target subgroups of the primary GWAS data set and performed 40 leave-one-out GWAS analyses (see Supplementary Methods and Supplementary Table 7) from which we derived risk alleles for RPS analysis (x axis) for each target subgroup. a, The proportion of variance explained (y axis; Nagelkerke’s R2) was computed for each target by comparison of a full model (covariates + RPS) score to a reduced model (covariates only). For clarity, 3 different P value thresholds (PT) are presented denoted by the colour of each bar (legend above plot) as for Extended Data Fig. 5, but for clarity we restrict to fewer P value thresholds (PT of 5×10−8, 1×10−4 and 0.05) and removed the significance values. b, The proportion of variance on the liability scale from risk scores calculated at the PT 0.05 with 95% CI bar assuming baseline population disease risk of 1%. c, Area under the receiver operating curve (AUC). All numerical data and methods used to generate these plots are available in Supplementary Table 7 and Supplementary Methods.

  10. Pairwise epistasis analysis of significant SNPs.
    Extended Data Fig. 7: Pairwise epistasis analysis of significant SNPs.

    Quantile-quantile plot for all pair-wise (n = 7,750) combinations of the 125 independent autosomal genome-wide significant SNPs tested for non-additive effects on risk using case-control data sets of European ancestry (32,405 cases and 42,221 controls). We included as covariates the principal components from the main analysis as well as a study indicator. The interaction model is described by:

    and are genotypes at the two loci, is the interaction between the two genotypes modelled in a multiplicative fashion, is the vector of principal components, is the vector of study indicator variables. Each is the regression coefficient in the generalized linear model using logistic regression. The overall distribution of P values did not deviate from the null and the smallest P value (4.28×10−4) did not surpass the Bonferroni correction threshold (P = 0.05/7750 = 6.45×10−6). The line x = y indicates the expected null distribution with the grey area bounded by red lines indicating the expected 95% confidence interval for the null.

Tables

  1. ALIGATOR and INRICH
    Extended Data Table 1: ALIGATOR and INRICH
  2. de novo overlap
    Extended Data Table 2: de novo overlap

Introduction

Schizophrenia has a lifetime risk of around 1%, and is associated with substantial morbidity and mortality as well as personal and societal costs1, 2, 3. Although pharmacological treatments are available for schizophrenia, their efficacy is poor for many patients4. All available antipsychotic drugs are thought to exert their main therapeutic effects through blockade of the type 2 dopaminergic receptor5, 6 but, since the discovery of this mechanism over 60 years ago, no new antipsychotic drug of proven efficacy has been developed based on other target molecules. Therapeutic stasis is in large part a consequence of the fact that the pathophysiology of schizophrenia is unknown. Identifying the causes of schizophrenia is therefore a critical step towards improving treatments and outcomes for those with the disorder.

High heritability points to a major role for inherited genetic variants in the aetiology of schizophrenia7, 8. Although risk variants range in frequency from common to extremely rare9, estimates10, 11 suggest half to a third of the genetic risk of schizophrenia is indexed by common alleles genotyped by current genome-wide association study (GWAS) arrays. Thus, GWAS is potentially an important tool for understanding the biological underpinnings of schizophrenia.

To date, around 30 schizophrenia-associated loci10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 have been identified through GWAS. Postulating that sample size is one of the most important limiting factors in applying GWAS to schizophrenia, we created the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC). Our primary aim was to combine all available schizophrenia samples with published or unpublished GWAS genotypes into a single, systematic analysis24. Here we report the results of that analysis, including at least 108 independent genomic loci that exceed genome-wide significance. Some of the findings support leading pathophysiological hypotheses of schizophrenia or targets of therapeutic relevance, but most of the findings provide new insights.

108 independent associated loci

We obtained genome-wide genotype data from which we constructed 49 ancestry matched, non-overlapping case-control samples (46 of European and three of east Asian ancestry, 34,241 cases and 45,604 controls) and 3 family-based samples of European ancestry (1,235 parent affected-offspring trios) (Supplementary Table 1 and Supplementary Methods). These comprise the primary PGC GWAS data set. We processed the genotypes from all studies using unified quality control procedures followed by imputation of SNPs and insertion-deletions using the 1000 Genomes Project reference panel25. In each sample, association testing was conducted using imputed marker dosages and principal components (PCs) to control for population stratification. The results were combined using an inverse-variance weighted fixed effects model26. After quality control (imputation INFO score0.6, MAF0.01, and successfully imputed in20 samples), we considered around 9.5 million variants. The results are summarized in Fig. 1. To enable acquisition of large samples, some groups ascertained cases via clinician diagnosis rather than a research-based assessment and provided evidence of the validity of this approach (Supplementary Information)11, 13. Post hoc analyses revealed the pattern of effect sizes for associated loci was similar across different assessment methods and modes of ascertainment (Extended Data Fig. 1), supporting our a priori decision to include samples of this nature.

Figure 1: Manhattan plot showing schizophrenia associations.
Manhattan plot showing schizophrenia associations.

Manhattan plot of the discovery genome-wide association meta-analysis of 49 case control samples (34,241 cases and 45,604 controls) and 3 family based association studies (1,235 parent affected-offspring trios). The x axis is chromosomal position and the y axis is the significance (–log10 P; 2-tailed) of association derived by logistic regression. The red line shows the genome-wide significance level (5×10−8). SNPs in green are in linkage disequilibrium with the index SNPs (diamonds) which represent independent genome-wide significant associations.

For the subset of linkage-disequilibrium-independent single nucleotide polymorphisms (SNPs) with P<1×10−6 in the meta-analysis, we next obtained results from deCODE genetics (1,513 cases and 66,236 controls of European ancestry). We define linkage-disequilibrium-independent SNPs as those with low linkage disequilibrium (r2<0.1) to a more significantly associated SNP within a 500-kb window. Given high linkage disequilibrium in the extended major histocompatibility complex (MHC) region spans ~8Mb, we conservatively include only a single MHC SNP to represent this locus. The deCODE data were then combined with those from the primary GWAS to give a data set of 36,989 cases and 113,075 controls. In this final analysis, 128 linkage-disequilibrium-independent SNPs exceeded genome-wide significance (P5×10−8) (Supplementary Table 2).

As in meta-analyses of other complex traits which identified large numbers of common risk variants27, 28, the test statistic distribution from our GWAS deviates from the null (Extended Data Fig. 2). This is consistent with the previously documented polygenic contribution to schizophrenia10, 11. The deviation in the test statistics from the null (λGC = 1.47, λ1000 = 1.01) is only slightly less than expected (λGC = 1.56) under a polygenic model given fully informative genotypes, the current sample size, and the lifetime risk and heritability of schizophrenia29.

Additional lines of evidence allow us to conclude the deviation between the observed and null distributions in our primary GWAS indicates a true polygenic contribution to schizophrenia. First, applying a novel method30 that uses linkage disequilibrium information to distinguish between the major potential sources of test statistic inflation, we found our results are consistent with polygenic architecture but not population stratification (Extended Data Fig. 3). Second, the schizophrenia-associated alleles at 78% of 234 linkage-disequilibrium-independent SNPs exceeding P<1×10−6 in the case-control GWAS were again overrepresented in cases in the independent samples from deCODE. This degree of consistency between the case-control GWAS and the replication data is highly unlikely to occur by chance (P = 6×10−19). The tested alleles surpassed the P<10−6 threshold in our GWAS before we added either the trios or deCODE data to the meta-analysis. This trend test is therefore independent of the primary case-control GWAS. Third, analysing the 1,235 parent-proband trios, we again found excess transmission of the schizophrenia-associated allele at 69% of the 263 linkage-disequilibrium-independent SNPs with P<1×10−6 in the case-control GWAS. This is again unlikely to occur by chance (P = 1×10−9) and additionally excludes population stratification as fully explaining the associations reaching our threshold for seeking replication. Fourth, we used the trios trend data to estimate the expected proportion of true associations at P<1×10−6 in the discovery GWAS, allowing for the fact that half of the index SNPs are expected to show the same allelic trend in the trios by chance, and that some true associations will show opposite trends given the limited number of trio samples (Supplementary Methods). Given the observed trend test results, around 67% (95% confidence interval: 64–73%) or n = 176 of the associations in the scan at P<1×10−6 are expected to be true, and therefore the number of associations that will ultimately be validated from this set of SNPs will be considerably more than those that now meet genome-wide significance. Taken together, these analyses indicate that the observed deviation of test statistics from the null primarily represents polygenic association signal and the considerable excess of associations at the tail of extreme significance largely correspond to true associations.

Independently associated SNPs do not translate to well-bounded chromosomal regions. Nevertheless, it is useful to define physical boundaries for the SNP associations to identify candidate risk genes. We defined an associated locus as the physical region containing all SNPs correlated at r2>0.6 with each of the 128 index SNPs. Associated loci within 250kb of each other were merged. This resulted in 108 physically distinct associated loci, 83 of which have not been previously implicated in schizophrenia and therefore harbour potential new biological insights into disease aetiology (Supplementary Table 3; regional plots in Supplementary Fig. 1). The significant regions include all but 5 loci previously reported to be genome-wide significant in large samples (Supplementary Table 3).

Characterization of associated loci

Of the 108 loci, 75% include protein-coding genes (40%, a single gene) and a further 8% are within 20 kb of a gene (Supplementary Table 3). Notable associations relevant to major hypotheses of the aetiology and treatment of schizophrenia include DRD2 (the target of all effective antipsychotic drugs) and many genes (for example, GRM3, GRIN2A, SRR, GRIA1) involved in glutamatergic neurotransmission and synaptic plasticity. In addition, associations at CACNA1C, CACNB2 and CACNA1I, which encode voltage-gated calcium channel subunits, extend previous findings implicating members of this family of proteins in schizophrenia and other psychiatric disorders11, 13, 31, 32. Genes encoding calcium channels, and proteins involved in glutamatergic neurotransmission and synaptic plasticity have been independently implicated in schizophrenia by studies of rare genetic variation33, 34, 35, suggesting convergence at a broad functional level between studies of common and rare genetic variation. We highlight in the Supplementary Discussion genes of particular interest within associated loci with respect to current hypotheses of schizophrenia aetiology or treatment (although we do not imply that these genes are necessarily the causal elements).

For each of the schizophrenia-associated loci, we identified a credible causal set of SNPs (for definition, see Supplementary Methods)36. In only 10 instances (Supplementary Table 4) was the association signal credibly attributable to a known non-synonymous exonic polymorphism. The apparently limited role of protein-coding variants is consistent both with exome sequencing findings33 and with the hypothesis that most associated variants detected by GWAS exert their effects through altering gene expression rather than protein structure37, 38 and with the observation that schizophrenia risk loci are enriched for expression quantitative trait loci (eQTL)39.

To try to identify eQTLs that could explain associations with schizophrenia, we merged the credible causal set of SNPs defined above with eQTLs from a meta-analysis of human brain cortex eQTL studies (n = 550) and an eQTL study of peripheral venous blood (n = 3,754)40 (Supplementary Methods). Multiple schizophrenia loci contained at least one eQTL for a gene within 1Mb of the locus (Supplementary Table 4). However, in only 12 instances was the eQTL plausibly causal (two in brain, and nine in peripheral blood, one in both). This low proportion suggests that if most risk variants are regulatory, available eQTL catalogues do not yet provide power, cellular specificity, or developmental diversity to provide clear mechanistic hypotheses for follow-up experiments.

The brain and immunity

To further explore the regulatory nature of the schizophrenia associations, we mapped the credible sets (n = 108) of causal variants onto sequences with epigenetic markers characteristic of active enhancers in 56 different tissues and cell lines (Supplementary Methods). Schizophrenia associations were significantly enriched at enhancers active in brain (Fig. 2) but not in tissues unlikely to be relevant to schizophrenia (for example, bone, cartilage, kidney and fibroblasts). Brain tissues used to define enhancers consist of heterogeneous populations of cells. Seeking greater specificity, we contrasted genes enriched for expression in neurons and glia using mouse ribotagged lines41. Genes with strong expression in multiple cortical and striatal neuronal lineages were enriched for associations, providing support for an important neuronal pathology in schizophrenia (Extended Data Fig. 4) but this is not statistically more significant than, or exclusionary of, contributions from other lineages42.

Figure 2: Enrichment in enhancers of credible SNPs.
Enrichment in enhancers of credible SNPs.

Cell and tissue type specific enhancers were identified using ChIP-seq data sets (H3K27ac signal) from 56 cell line and tissue samples (y axis). We defined cell and tissue type enhancers as the top 10% of enhancers with the highest ratio of reads in that cell or tissue type divided by the total number of reads. Enrichment of credible causal associated SNPs from the schizophrenia GWAS was compared with frequency matched sets of 1000 Genomes SNPs (Supplementary Methods). The x axis is the –log10 P for enrichment. P values are uncorrected for the number of tissues or cells tested. A –log10 P of roughly 3 can be considered significant after Bonferroni correction. Descriptions of cell and tissue types at the Roadmap Epigenome website (http://www.roadmapepigenomics.org).

Schizophrenia associations were also strongly enriched at enhancers that are active in tissues with important immune functions, particularly B-lymphocyte lineages involved in acquired immunity (CD19 and CD20 lines, Fig. 2). These enrichments remain significant even after excluding the extended MHC region and regions containing brain enhancers (enrichment P for CD20<10−6), demonstrating that this finding is not an artefact of correlation between enhancer elements in different tissues and not driven by the strong and diffuse association at the extended MHC. Epidemiological studies have long hinted at a role for immune dysregulation in schizophrenia, the present findings provide genetic support for this hypothesis43.

To develop additional biological hypotheses beyond those that emerge from inspection of the individual loci, we further undertook a limited mining of the data through gene-set analysis. However, as there is no consensus methodology by which such analyses should be conducted, nor an established optimal significance threshold for including loci, we sought to be conservative, using only two of the many available approaches44, 45 and restricting analyses to genes within genome-wide significant loci. Neither approach identified gene-sets that were significantly enriched for associations after correction for the number of pathways tested (Supplementary Table 5) although nominally significantly enrichments were observed among several predefined candidate pathways (Extended Data Table 1). A fuller exploratory analysis of the data will be presented elsewhere.

Overlap with rare mutations

CNVs associated with schizophrenia overlap with those associated with autism spectrum disorder (ASD) and intellectual disability9, as do genes with deleterious de novo mutations34. Here we find significant overlap between genes in the schizophrenia GWAS associated intervals and those with de novo non-synonymous mutations in schizophrenia (P = 0.0061) (Extended Data Table 2), suggesting that mechanistic studies of rare genetic variation in schizophrenia will be informative for schizophrenia more widely. We also find evidence for overlap between genes in schizophrenia GWAS regions and those with de novo non-synonymous mutations in intellectual disability (P = 0.00024) and ASD (P = 0.035), providing further support for the hypothesis that these disorders have partly overlapping pathophysiologies9, 34.

Polygenic risk score profiling

Previous studies have shown that risk profile scores (RPS) constructed from alleles showing modest association with schizophrenia in a discovery GWAS can predict case-control status in independent samples, albeit with low sensitivity and specificity10, 11, 16. This finding was robustly confirmed in the present study. The estimate of Nagelkerke R2 (a measure of variance in case-control status explained) depends on the specific target data set and threshold (PT) for selecting risk alleles for RPS analysis (Extended Data Fig. 5 and 6a). However, using the same target sample as earlier studies and PT = 0.05, R2 is now increased from 0.03 (ref. 10) to 0.184 (Extended Data Fig. 5). Assuming a liability-threshold model, a lifetime risk of 1%, independent SNP effects, and adjusting for case-control ascertainment, RPS now explains about 7% of variation on the liability scale46 to schizophrenia across the samples (Extended Data Fig. 6b), about half of which (3.4%) is explained by genome-wide significant loci.

We also evaluated the capacity of RPS to predict case-control status using a standard epidemiological approach to a continuous risk factor. We illustrate this in three samples, each with different ascertainment schemes (Fig. 3). The Danish sample is population-based (that is, inpatient and outpatient facilities), the Swedish sample is based on all cases hospitalized for schizophrenia in Sweden, and the Molecular Genetics of Schizophrenia (MGS) sample was ascertained specially for genetic studies from clinical sources in the US and Australia. We grouped individuals into RPS deciles and estimated the odds ratios for affected status for each decile with reference to the lowest risk decile. The odds ratios increased with greater number of schizophrenia risk alleles in each sample, maximizing for the tenth decile in all samples: Denmark 7.8 (95% confidence interval (CI): 4.4–13.9), Sweden 15.0 (95% CI: 12.1–18.7) and MGS 20.3 (95% CI: 14.7–28.2). Given the need for measures that index liability to schizophrenia47, 48, the ability to stratify individuals by RPS offers new opportunities for clinical and epidemiological research. Nevertheless, we stress that the sensitivity and specificity of RPS do not support its use as a predictive test. For example, in the Danish epidemiological sample, the area under the receiver operating curve is only 0.62 (Extended Data Fig. 6c, Supplementary Table 6).

Figure 3: Odds ratio by risk score profile.
Odds ratio by risk score profile.

Odds ratio for schizophrenia by risk score profile (RPS) decile in the Sweden (Sw1-6), Denmark (Aarhus), and Molecular Genetics of Schizophrenia studies (Supplementary Methods). Risk alleles and weights were derived from ‘leave one out’ analyses in which those samples were excluded from the GWAS meta-analysis (Supplementary Methods). The threshold for selecting risk alleles was PT<0.05. The RPS were converted to deciles (1 = lowest, 10 = highest RPS), and nine dummy variables created to contrast deciles 2-10 to decile 1 as the reference. Odds ratios and 95% confidence intervals (bars) were estimated using logistic regression with PCs to control for population stratification.

Finally, seeking evidence for non-additive effects on risk, we tested for statistical interaction between all pairs of 125 autosomal SNPs that reached genome-wide significance. P values for the interaction terms were distributed according to the null, and no interaction was significant after correction for multiple comparisons. Thus, we find no evidence for epistatic or non-additive effects between the significant loci (Extended Data Fig. 7). It is possible that such effects could be present between other loci, or occur in the form of higher-order interactions.

Discussion

In the largest (to our knowledge) molecular genetic study of schizophrenia, or indeed of any neuropsychiatric disorder, ever conducted, we demonstrate the power of GWAS to identify large numbers of risk loci. We show that the use of alternative ascertainment and diagnostic schemes designed to rapidly increase sample size does not inevitably introduce a crippling degree of heterogeneity. That this is true for a phenotype like schizophrenia, in which there are no biomarkers or supportive diagnostic tests, provides grounds to be optimistic that this approach can be successfully applied to GWAS of other clinically defined disorders.

We further show that the associations are not randomly distributed across genes of all classes and function; rather they converge upon genes that are expressed in certain tissues and cellular types. The findings include molecules that are the current, or the most promising, targets for therapeutics, and point to systems that align with the predominant aetiological hypotheses of the disorder. This suggests that the many novel findings we report also provide an aetiologically relevant foundation for mechanistic and treatment development studies. We also find overlap between genes affected by rare variants in schizophrenia and those within GWAS loci, and broad convergence in the functions of some of the clusters of genes implicated by both sets of genetic variants, particularly genes related to abnormal glutamatergic synaptic and calcium channel function. How variation in these genes impact function to increase risk for schizophrenia cannot be answered by genetics, but the overlap strongly suggests that common and rare variant studies are complementary rather than antagonistic, and that mechanistic studies driven by rare genetic variation will be informative for schizophrenia.

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Acknowledgements

Core funding for the Psychiatric Genomics Consortium is from the US National Institute of Mental Health (U01 MH094421). We thank T. Lehner (NIMH). The work of the contributing groups was supported by numerous grants from governmental and charitable bodies as well as philanthropic donation. Details are provided in the Supplementary Notes. Membership of the Wellcome Trust Case Control Consortium and of the Psychosis Endophenotype International Consortium are provided in the Supplementary Notes.

Author information

Affiliations

  1. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.

    • Stephan Ripke,
    • Benjamin M. Neale,
    • Kai-How Farh,
    • Phil Lee,
    • Brendan Bulik-Sullivan,
    • Hailiang Huang,
    • Menachem Fromer,
    • Jacqueline I. Goldstein &
    • Mark J. Daly
  2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.

    • Stephan Ripke,
    • Benjamin M. Neale,
    • Phil Lee,
    • Brendan Bulik-Sullivan,
    • Richard A. Belliveau Jr,
    • Sarah E. Bergen,
    • Elizabeth Bevilacqua,
    • Kimberly D. Chambert,
    • Menachem Fromer,
    • Giulio Genovese,
    • Colm O’Dushlaine,
    • Edward M. Scolnick,
    • Jordan W. Smoller,
    • Steven A. McCarroll,
    • Jennifer L. Moran,
    • Aarno Palotie,
    • Tracey L. Petryshen &
    • Mark J. Daly
  3. Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.

    • Benjamin M. Neale,
    • Hailiang Huang,
    • Tune H. Pers,
    • Jacqueline I. Goldstein,
    • Joel N. Hirschhorn,
    • Alkes Price,
    • Eli A. Stahl,
    • Tõnu Esko &
    • Mark J. Daly
  4. Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.

    • Benjamin M. Neale,
    • Phil Lee,
    • Menachem Fromer,
    • Jordan W. Smoller &
    • Aarno Palotie
  5. Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin 8, Ireland.

    • Aiden Corvin,
    • Paul Cormican,
    • Gary Donohoe,
    • Derek W. Morris &
    • Michael Gill
  6. MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff CF24 4HQ, UK.

    • James T. R. Walters,
    • Peter A. Holmans,
    • Noa Carrera,
    • Nick Craddock,
    • Valentina Escott-Price,
    • Lyudmila Georgieva,
    • Marian L. Hamshere,
    • David Kavanagh,
    • Sophie E. Legge,
    • Andrew J. Pocklington,
    • Alexander L. Richards,
    • Douglas M. Ruderfer,
    • Nigel M. Williams,
    • George Kirov,
    • Michael J. Owen &
    • Michael C. O’Donovan
  7. National Centre for Mental Health, Cardiff University, Cardiff CF24 4HQ, UK.

    • Peter A. Holmans,
    • Nick Craddock,
    • Michael J. Owen &
    • Michael C. O’Donovan
  8. Eli Lilly and Company Limited, Erl Wood Manor, Sunninghill Road, Windlesham, Surrey GU20 6PH, UK.

    • David A. Collier &
    • Younes Mokrab
  9. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, London SE5 8AF, UK.

    • David A. Collier
  10. Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800, Denmark.

    • Tune H. Pers
  11. Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, Massachusetts 02115, USA.

    • Tune H. Pers,
    • Joel N. Hirschhorn &
    • Tõnu Esko
  12. Department of Clinical Neuroscience, Psychiatry Section, Karolinska Institutet, SE-17176 Stockholm, Sweden.

    • Ingrid Agartz,
    • Erik Söderman &
    • Erik G. Jönsson
  13. Department of Psychiatry, Diakonhjemmet Hospital, 0319 Oslo, Norway.

    • Ingrid Agartz
  14. NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway.

    • Ingrid Agartz,
    • Srdjan Djurovic,
    • Morten Mattingsdal,
    • Ingrid Melle,
    • Ole A. Andreassen &
    • Erik G. Jönsson
  15. Centre for Integrative Register-based Research, CIRRAU, Aarhus University, DK-8210 Aarhus, Denmark.

    • Esben Agerbo &
    • Preben B. Mortensen
  16. National Centre for Register-based Research, Aarhus University, DK-8210 Aarhus, Denmark.

    • Esben Agerbo &
    • Preben B. Mortensen
  17. The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark.

    • Esben Agerbo,
    • Ditte Demontis,
    • Thomas Hansen,
    • Manuel Mattheisen,
    • Ole Mors,
    • Line Olsen,
    • Henrik B. Rasmussen,
    • Anders D. Børglum,
    • Preben B. Mortensen &
    • Thomas Werge
  18. State Mental Hospital, 85540 Haar, Germany.

    • Margot Albus
  19. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California 94305, USA.

    • Madeline Alexander,
    • Claudine Laurent &
    • Douglas F. Levinson
  20. Department of Psychiatry and Behavioral Sciences, Atlanta Veterans Affairs Medical Center, Atlanta, Georgia 30033, USA.

    • Farooq Amin
  21. Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia 30322, USA.

    • Farooq Amin
  22. Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia 23298, USA.

    • Silviu A. Bacanu,
    • Tim B. Bigdeli,
    • Bradley T. Webb &
    • Brandon K. Wormley
  23. Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen 37075, Germany.

    • Martin Begemann,
    • Christian Hammer,
    • Sergi Papiol &
    • Hannelore Ehrenreich
  24. Department of Medical Genetics, University of Pécs, Pécs H-7624, Hungary.

    • Judit Bene &
    • Bela Melegh
  25. Szentagothai Research Center, University of Pécs, Pécs H-7624, Hungary.

    • Judit Bene &
    • Bela Melegh
  26. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, Sweden.

    • Sarah E. Bergen,
    • Anna K. Kähler,
    • Patrik K. E. Magnusson,
    • Christina M. Hultman &
    • Patrick F. Sullivan
  27. Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA.

    • Donald W. Black
  28. University Medical Center Groningen, Department of Psychiatry, University of Groningen NL-9700 RB, The Netherlands.

    • Richard Bruggeman
  29. School of Nursing, Louisiana State University Health Sciences Center, New Orleans, Louisiana 70112, USA.

    • Nancy G. Buccola
  30. Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, Massachusetts 02129, USA.

    • Randy L. Buckner &
    • Joshua L. Roffman
  31. Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA.

    • Randy L. Buckner
  32. Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.

    • Randy L. Buckner &
    • Joshua L. Roffman
  33. Department of Psychiatry, University of California at San Francisco, San Francisco, California 94143, USA.

    • William Byerley
  34. University Medical Center Utrecht, Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, 3584 Utrecht, The Netherlands.

    • Wiepke Cahn,
    • René S. Kahn,
    • Eric Strengman &
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  35. Department of Human Genetics, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.

    • Guiqing Cai &
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  36. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.

    • Guiqing Cai,
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    • Vahram Haroutunian,
    • Elena Parkhomenko,
    • Abraham Reichenberg,
    • Jeremy M. Silverman &
    • Joseph D. Buxbaum
  37. Centre Hospitalier du Rouvray and INSERM U1079 Faculty of Medicine, 76301 Rouen, France.

    • Dominique Campion
  38. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA.

    • Rita M. Cantor &
    • Roel A. Ophoff
  39. Schizophrenia Research Institute, Sydney NSW 2010, Australia.

    • Vaughan J. Carr,
    • Stanley V. Catts,
    • Frans A. Henskens,
    • Carmel M. Loughland,
    • Patricia T. Michie,
    • Christos Pantelis,
    • Ulrich Schall,
    • Rodney J. Scott &
    • Assen V. Jablensky
  40. School of Psychiatry, University of New South Wales, Sydney NSW 2031, Australia.

    • Vaughan J. Carr
  41. Royal Brisbane and Women’s Hospital, University of Queensland, Brisbane, St Lucia QLD 4072, Australia.

    • Stanley V. Catts
  42. Institute of Psychology, Chinese Academy of Science, Beijing 100101, China.

    • Raymond C. K. Chan
  43. Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.

    • Ronald Y. L. Chen,
    • Eric Y. H. Chen,
    • Miaoxin Li,
    • Hon-Cheong So,
    • Emily H. M. Wong &
    • Pak C. Sham
  44. State Key Laboratory for Brain and Cognitive Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.

    • Eric Y. H. Chen,
    • Miaoxin Li &
    • Pak C. Sham
  45. Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina 27514, USA.

    • Wei Cheng
  46. Castle Peak Hospital, Hong Kong, China.

    • Eric F. C. Cheung
  47. Institute of Mental Health, Singapore 539747, Singapore.

    • Siow Ann Chong,
    • Jimmy Lee Chee Keong,
    • Kang Sim &
    • Mythily Subramaniam
  48. Department of Psychiatry, Washington University, St. Louis, Missouri 63110, USA.

    • C. Robert Cloninger &
    • Dragan M. Svrakic
  49. Department of Child and Adolescent Psychiatry, Assistance Publique Hopitaux de Paris, Pierre and Marie Curie Faculty of Medicine and Institute for Intelligent Systems and Robotics, Paris 75013, France.

    • David Cohen
  50. Blue Note Biosciences, Princeton, New Jersey 08540, USA

    • Nadine Cohen
  51. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7264, USA.

    • James J. Crowley,
    • Martilias S. Farrell,
    • Paola Giusti-Rodríguez,
    • Yunjung Kim,
    • Jin P. Szatkiewicz,
    • Stephanie Williams &
    • Patrick F. Sullivan
  52. Department of Psychological Medicine, Queen Mary University of London, London E1 1BB, UK.

    • David Curtis
  53. Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London WC1E 6JJ, UK.

    • David Curtis,
    • Jonathan Pimm,
    • Hugh Gurling &
    • Andrew McQuillin
  54. Sheba Medical Center, Tel Hashomer 52621, Israel.

    • Michael Davidson &
    • Mark Weiser
  55. Department of Genomics, Life and Brain Center, D-53127 Bonn, Germany.

    • Franziska Degenhardt,
    • Stefan Herms,
    • Per Hoffmann,
    • Andrea Hofman,
    • Sven Cichon &
    • Markus M. Nöthen
  56. Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany.

    • Franziska Degenhardt,
    • Stefan Herms,
    • Per Hoffmann,
    • Andrea Hofman,
    • Sven Cichon &
    • Markus M. Nöthen
  57. Applied Molecular Genomics Unit, VIB Department of Molecular Genetics, University of Antwerp, B-2610 Antwerp, Belgium.

    • Jurgen Del Favero
  58. Centre for Integrative Sequencing, iSEQ, Aarhus University, DK-8000 Aarhus C, Denmark.

    • Ditte Demontis,
    • Manuel Mattheisen,
    • Ole Mors &
    • Anders D. Børglum
  59. Department of Biomedicine, Aarhus University, DK-8000 Aarhus C, Denmark.

    • Ditte Demontis,
    • Manuel Mattheisen &
    • Anders D. Børglum
  60. First Department of Psychiatry, University of Athens Medical School, Athens 11528, Greece.

    • Dimitris Dikeos &
    • George N. Papadimitriou
  61. Department of Psychiatry, University College Cork, Co. Cork, Ireland.

    • Timothy Dinan
  62. Department of Medical Genetics, Oslo University Hospital, 0424 Oslo, Norway.

    • Srdjan Djurovic
  63. Cognitive Genetics and Therapy Group, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Co. Galway, Ireland.

    • Gary Donohoe &
    • Derek W. Morris
  64. Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois 60637, USA.

    • Jubao Duan,
    • Alan R. Sanders &
    • Pablo V. Gejman
  65. Department of Psychiatry and Behavioral Sciences, NorthShore University HealthSystem, Evanston, Illinois 60201, USA.

    • Jubao Duan,
    • Alan R. Sanders &
    • Pablo V. Gejman
  66. Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.

    • Frank Dudbridge
  67. Department of Child and Adolescent Psychiatry, University Clinic of Psychiatry, Skopje 1000, Republic of Macedonia.

    • Naser Durmishi
  68. Department of Psychiatry, University of Regensburg, 93053 Regensburg, Germany.

    • Peter Eichhammer
  69. Department of General Practice, Helsinki University Central Hospital, University of Helsinki P.O. Box 20, Tukholmankatu 8 B, FI-00014, Helsinki, Finland

    • Johan Eriksson
  70. Folkhälsan Research Center, Helsinki, Finland, Biomedicum Helsinki 1, Haartmaninkatu 8, FI-00290, Helsinki, Finland.

    • Johan Eriksson
  71. National Institute for Health and Welfare, P.O. Box 30, FI-00271 Helsinki, Finland.

    • Johan Eriksson &
    • Veikko Salomaa
  72. Translational Technologies and Bioinformatics, Pharma Research and Early Development, F. Hoffman-La Roche, CH-4070 Basel, Switzerland.

    • Laurent Essioux
  73. Department of Psychiatry, Georgetown University School of Medicine, Washington DC 20057, USA.

    • Ayman H. Fanous
  74. Department of Psychiatry, Keck School of Medicine of the University of Southern California, Los Angeles, California 90033, USA.

    • Ayman H. Fanous
  75. Departmentof Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, Virginia 23298, USA.

    • Ayman H. Fanous
  76. Mental Health Service Line, Washington VA Medical Center, Washington DC 20422, USA.

    • Ayman H. Fanous
  77. Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg , D-68159 Mannheim, Germany.

    • Josef Frank,
    • Sandra Meier,
    • Thomas G. Schulze,
    • Jana Strohmaier,
    • Stephanie H. Witt &
    • Marcella Rietschel
  78. Department of Genetics, University of Groningen, University Medical Centre Groningen, 9700 RB Groningen, The Netherlands.

    • Lude Franke &
    • Juha Karjalainen
  79. Department of Psychiatry, University of Colorado Denver, Aurora, Colorado 80045, USA.

    • Robert Freedman &
    • Ann Olincy
  80. Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, California 90095, USA.

    • Nelson B. Freimer &
    • Roel A. Ophoff
  81. Department of Psychiatry, University of Halle, 06112 Halle, Germany.

    • Marion Friedl,
    • Ina Giegling,
    • Annette M. Hartmann,
    • Bettina Konte &
    • Dan Rujescu
  82. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, New York 10029, USA.

    • Menachem Fromer,
    • Shaun M. Purcell,
    • Panos Roussos,
    • Douglas M. Ruderfer,
    • Eli A. Stahl &
    • Pamela Sklar
  83. Department of Psychiatry, University of Munich, 80336, Munich, Germany.

    • Ina Giegling &
    • Dan Rujescu
  84. Departments of Psychiatry and Human and Molecular Genetics, INSERM, Institut de Myologie, Hôpital de la Pitiè-Salpêtrière, Paris 75013, France.

    • Stephanie Godard
  85. Mental Health Research Centre, Russian Academy of Medical Sciences, 115522 Moscow, Russia.

    • Vera Golimbet
  86. Neuroscience Therapeutic Area, Janssen Research and Development, Raritan, New Jersey 08869, USA.

    • Srihari Gopal,
    • Dai Wang &
    • Qingqin S. Li
  87. Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, QLD 4072, Australia.

    • Jacob Gratten,
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    • Naomi R. Wray,
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    • Bryan J. Mowry
  88. Academic Medical Centre University of Amsterdam, Department of Psychiatry, 1105 AZ Amsterdam, The Netherlands.

    • Lieuwe de Haan &
    • Carin J. Meijer
  89. Illumina, La Jolla, California, California 92122, USA.

    • Mark Hansen
  90. Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, DK-4000, Denmark.

    • Thomas Hansen,
    • Line Olsen,
    • Henrik B. Rasmussen &
    • Thomas Werge
  91. Friedman Brain Institute, Icahn School ofMedicine at Mount Sinai, New York, New York 10029, USA.

    • Vahram Haroutunian,
    • Joseph D. Buxbaum &
    • Pamela Sklar
  92. J. J. Peters VA Medical Center, Bronx, New York, New York 10468, USA.

    • Vahram Haroutunian
  93. Priority Research Centre for Health Behaviour, University of Newcastle, Newcastle NSW 2308, Australia.

    • Frans A. Henskens
  94. School of Electrical Engineering and Computer Science, University of Newcastle, Newcastle NSW 2308, Australia.

    • Frans A. Henskens
  95. Division of Medical Genetics, Department of Biomedicine, University of Basel, Basel CH-4058, Switzerland.

    • Stefan Herms,
    • Per Hoffmann &
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  96. Department of Genetics, Harvard Medical School, Boston, Massachusetts, Massachusetts 02115, USA.

    • Joel N. Hirschhorn,
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  97. Section of Neonatal Screening and Hormones, Department of Clinical Biochemistry, Immunology and Genetics, Statens Serum Institut, Copenhagen DK-2300, Denmark.

    • Mads V. Hollegaard &
    • David M. Hougaard
  98. Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Aichi,470-1192, Japan.

    • Masashi Ikeda &
    • Nakao Iwata
  99. Regional Centre for Clinical Research in Psychosis, Department of Psychiatry, Stavanger University Hospital, 4011 Stavanger, Norway.

    • Inge Joa
  100. Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona 08035, Spain.

    • Antonio Julià &
    • Sara Marsal
  101. Centre for Medical Research, The University of Western Australia, Perth WA6009, Australia.

    • Luba Kalaydjieva
  102. The Perkins Institute for Medical Research, The University of Western Australia, Perth WA6009, Australia.

    • Luba Kalaydjieva &
    • Assen V. Jablensky
  103. Department of Medical Genetics, Medical University, Sofia 1431, Bulgaria.

    • Sena Karachanak-Yankova &
    • Draga Toncheva
  104. Department of Psychology, University of Colorado Boulder, Boulder, Colorado 80309, USA.

    • Matthew C. Keller
  105. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada.

    • James L. Kennedy,
    • Clement C. Zai &
    • Jo Knight
  106. Department of Psychiatry, University of Toronto, Toronto, Ontario M5T 1R8, Canada.

    • James L. Kennedy,
    • Clement C. Zai &
    • Jo Knight
  107. Institute of Medical Science, University of Toronto, Toronto, Ontario M5S 1A8, Canada.

    • James L. Kennedy &
    • Jo Knight
  108. Institute of Molecular Genetics, Russian Academy of Sciences, Moscow 123182, Russia.

    • Andrey Khrunin,
    • Svetlana Limborska &
    • Petr Slominsky
  109. Latvian Biomedical Research and Study Centre, Riga, LV-1067, Latvia.

    • Janis Klovins &
    • Liene Nikitina-Zake
  110. Department of Psychiatry and Zilkha Neurogenetics Institute, Keck School of Medicine at University of Southern California, Los Angeles, California 90089, USA.

    • James A. Knowles,
    • Michele T. Pato &
    • Carlos N. Pato
  111. Faculty of Medicine, Vilnius University, LT-01513 Vilnius, Lithuania.

    • Vaidutis Kucinskas &
    • Zita Ausrele Kucinskiene
  112. Department of Biology and Medical Genetics, 2nd Faculty of Medicine and University Hospital Motol, 150 06 Prague, Czech Republic.

    • Hana Kuzelova-Ptackova &
    • Milan Macek Jr
  113. Department of Child and Adolescent Psychiatry, Pierre and Marie Curie Faculty of Medicine, Paris 75013, France.

    • Claudine Laurent
  114. Duke-NUS Graduate Medical School, Singapore 169857.

    • Jimmy Lee Chee Keong
  115. Department of Psychiatry, Hadassah-Hebrew University Medical Center, Jerusalem 91120, Israel.

    • Bernard Lerer
  116. Centre for Genomic Sciences, The University of Hong Kong, Hong Kong, China.

    • Miaoxin Li &
    • Pak C. Sham
  117. Mental Health Centre and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, 610041 Sichuan, China.

    • Tao Li &
    • Qiang Wang
  118. Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.

    • Kung-Yee Liang
  119. Department of Psychiatry, Columbia University, New York, New York 10032, USA.

    • Jeffrey Lieberman &
    • T. Scott Stroup
  120. Priority Centre for Translational Neuroscience and Mental Health, University of Newcastle, Newcastle NSW 2300, Australia.

    • Carmel M. Loughland &
    • Ulrich Schall
  121. Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University in Szczecin, 70-453 Szczecin, Poland.

    • Jan Lubinski
  122. Department of Mental Health and Substance Abuse Services; National Institute for Health and Welfare, P.O. BOX 30, FI-00271 Helsinki, Finland.

    • Jouko Lönnqvist &
    • Jaana Suvisaari
  123. Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, USA.

    • Brion S. Maher
  124. Department of Psychiatry, University of Bonn, D-53127 Bonn, Germany.

    • Wolfgang Maier
  125. Centre National de la Recherche Scientifique, Laboratoire de Génétique Moléculaire de la Neurotransmission et des Processus Neurodégénératifs, Hôpital de la Pitié Salpêtrière, 75013 Paris, France.

    • Jacques Mallet
  126. Department of Genomics Mathematics, University of Bonn, D-53127 Bonn, Germany.

    • Manuel Mattheisen
  127. Research Unit, Sørlandet Hospital, 4604 Kristiansand, Norway.

    • Morten Mattingsdal
  128. Department of Psychiatry, Harvard Medical School, Boston, Massachusetts 02115, USA.

    • Robert W. McCarley,
    • Raquelle I. Mesholam-Gately,
    • Larry J. Seidman &
    • Tracey L. Petryshen
  129. VA Boston Health Care System, Brockton, Massachusetts 02301, USA.

    • Robert W. McCarley
  130. Department of Psychiatry, National University of Ireland Galway, Co. Galway, Ireland.

    • Colm McDonald
  131. Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH16 4SB, UK.

    • Andrew M. McIntosh
  132. Division of Psychiatry, University of Edinburgh, Edinburgh EH16 4SB, UK.

    • Andrew M. McIntosh &
    • Douglas H. R. Blackwood
  133. Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway.

    • Ingrid Melle &
    • Ole A. Andreassen
  134. Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical Center, Boston, Massachusetts 02114, USA.

    • Raquelle I. Mesholam-Gately &
    • Larry J. Seidman
  135. Estonian Genome Center, University of Tartu, Tartu 50090, Estonia.

    • Andres Metspalu,
    • Lili Milani,
    • Mari Nelis &
    • Tõnu Esko
  136. School of Psychology, University of Newcastle, Newcastle NSW 2308, Australia.

    • Patricia T. Michie
  137. First Psychiatric Clinic, Medical University, Sofia 1431, Bulgaria.

    • Vihra Milanova
  138. Department P, Aarhus University Hospital, DK-8240 Risskov, Denmark.

    • Ole Mors &
    • Anders D. Børglum
  139. Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin 2, Ireland.

    • Kieran C. Murphy
  140. King’s College London, London SE5 8AF, UK.

    • Robin M. Murray &
    • John Powell
  141. Maastricht University Medical Centre, South Limburg Mental Health Research and Teaching Network, EURON, 6229 HX Maastricht, The Netherlands.

    • Inez Myin-Germeys &
    • Jim Van Os
  142. Institute of Translational Medicine, University of Liverpool, Liverpool L69 3BX, UK.

    • Bertram Müller-Myhsok
  143. Max Planck Institute of Psychiatry, 80336 Munich, Germany.

    • Bertram Müller-Myhsok
  144. Munich Cluster for Systems Neurology (SyNergy), 80336 Munich, Germany.

    • Bertram Müller-Myhsok
  145. Department of Psychiatry and Psychotherapy, Jena University Hospital, 07743 Jena, Germany.

    • Igor Nenadic
  146. Department of Psychiatry, Queensland Brain Institute and Queensland Centre for Mental Health Research, University of Queensland, Brisbane, Queensland, St Lucia QLD 4072, Australia.

    • Deborah A. Nertney
  147. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.

    • Gerald Nestadt &
    • Ann E. Pulver
  148. Department of Psychiatry, Trinity College Dublin, Dublin 2, Ireland.

    • Kristin K. Nicodemus
  149. Eli Lilly and Company,Lilly Corporate Center, Indianapolis, 46285 Indiana, USA.

    • Laura Nisenbaum
  150. Department of Clinical Sciences, Psychiatry, Umeå University, SE-901 87 Umeå, Sweden.

    • Annelie Nordin &
    • Rolf Adolfsson
  151. DETECT Early Intervention Service for Psychosis, Blackrock, Co. Dublin, Ireland.

    • Eadbhard O’Callaghan
  152. Centre for Public Health, Institute of Clinical Sciences, Queen’s University Belfast, Belfast BT12 6AB, UK.

    • F. Anthony O’Neill
  153. Lawrence Berkeley National Laboratory, University of California at Berkeley, Berkeley, California 94720, USA.

    • Sang-Yun Oh
  154. Institute of Psychiatry, King’s College London, London SE5 8AF, UK.

    • Jim Van Os
  155. A list of authors and affiliations appear in the Supplementary Information.

    • Psychosis Endophenotypes International Consortium
  156. Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Melbourne, Vic 3053, Australia.

    • Christos Pantelis
  157. Department of Psychiatry, University of Helsinki, P.O. Box 590, FI-00029 HUS, Helsinki, Finland.

    • Tiina Paunio
  158. Public Health Genomics Unit, National Institute for Health and Welfare, P.O. BOX 30, FI-00271 Helsinki, Finland

    • Tiina Paunio &
    • Olli Pietiläinen
  159. Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia.

    • Milica Pejovic-Milovancevic
  160. Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina 27599-7160, USA.

    • Diana O. Perkins &
    • Patrick F. Sullivan
  161. Institute for Molecular Medicine Finland, FIMM, University of Helsinki, P.O. Box 20FI-00014, Helsinki, Finland

    • Olli Pietiläinen &
    • Aarno Palotie
  162. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA.

    • Alkes Price
  163. Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK.

    • Digby Quested
  164. Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia 23298, USA.

    • Mark A. Reimers &
    • Aaron R. Wolen
  165. Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.

    • Panos Roussos &
    • Pamela Sklar
  166. PharmaTherapeutics Clinical Research, Pfizer Worldwide Research and Development, Cambridge, Massachusetts 02139, USA.

    • Christian R. Schubert &
    • Jens R. Wendland
  167. Department of Psychiatry and Psychotherapy, University of Gottingen, 37073 Göttingen, Germany.

    • Thomas G. Schulze
  168. Psychiatry and Psychotherapy Clinic, University of Erlangen, 91054 Erlangen, Germany.

    • Sibylle G. Schwab
  169. Hunter New England Health Service, Newcastle NSW 2308, Australia.

    • Rodney J. Scott
  170. School of Biomedical Sciences, University of Newcastle, Newcastle NSW 2308, Australia.

    • Rodney J. Scott
  171. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA.

    • Jianxin Shi
  172. University of Iceland, Landspitali, National University Hospital, 101 Reykjavik, Iceland.

    • Engilbert Sigurdsson
  173. Department of Psychiatry and Drug Addiction, Tbilisi State Medical University (TSMU), N33, 0177 Tbilisi, Georgia.

    • Teimuraz Silagadze
  174. Research and Development, Bronx Veterans Affairs Medical Center, New York, New York 10468, USA.

    • Jeremy M. Silverman
  175. Wellcome Trust Centre for Human Genetics, Oxford OX3 7BN, UK.

    • ChrisC. A. Spencer
  176. deCODE Genetics, 101 Reykjavik, Iceland.

    • Hreinn Stefansson,
    • Stacy Steinberg &
    • Kari Stefansson
  177. Department of Clinical Neurology, Medical University of Vienna, 1090 Wien, Austria.

    • Elisabeth Stogmann &
    • Fritz Zimprich
  178. Lieber Institute for Brain Development, Baltimore, Maryland 21205, USA.

    • Richard E. Straub &
    • Daniel R. Weinberger
  179. Department of Medical Genetics, University Medical Centre Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands.

    • Eric Strengman
  180. Berkshire Healthcare NHS Foundation Trust, Bracknell RG12 1BQ, UK.

    • Srinivas Thirumalai
  181. Section of Psychiatry, University of Verona, 37134 Verona, Italy.

    • Sarah Tosato
  182. Department of Psychiatry, University of Oulu, P.O. Box 5000, 90014, Finland.

    • Juha Veijola
  183. University Hospital of Oulu, P.O. Box 20, 90029 OYS, Finland.

    • Juha Veijola
  184. Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin 2, Ireland.

  185. Health Research Board, Dublin 2, Ireland.

    • Dermot Walsh
  186. School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Perth WA6009, Australia.

    • Dieter B. Wildenauer &
    • Assen V. Jablensky
  187. Computational Sciences CoE, Pfizer Worldwide Research and Development, Cambridge, Massachusetts 02139, USA.

    • Hualin Simon Xi
  188. Human Genetics, Genome Institute of Singapore, A*STAR, Singapore 138672.

    • Jianjun Liu
  189. A list of authors and affiliations appear in the Supplementary Information.

    • Wellcome Trust Case-Control Consortium
  190. University College London, London WC1E 6BT, UK.

  191. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.

    • Joseph D. Buxbaum
  192. Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, 52428 Juelich, Germany.

    • Sven Cichon
  193. Department of Genetics, The Hebrew University of Jerusalem, 91905 Jerusalem, Israel.

    • Ariel Darvasi
  194. Neuroscience Discovery and Translational Area, Pharma Research and Early Development, F. Hoffman-La Roche, CH-4070 Basel, Switzerland.

    • Enrico Domenici
  195. Centre for Clinical Research in Neuropsychiatry, School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Medical Research Foundation Building, Perth WA6000, Australia.

    • Assen V. Jablensky
  196. Virginia Institute for Psychiatric and Behavioral Genetics, Departments of Psychiatry and Human and Molecular Genetics, Virginia Commonwealth University, Richmond, Virginia 23298, USA.

    • Kenneth S. Kendler &
    • Brien P. Riley
  197. The Feinstein Institute for Medical Research, Manhasset, New York 11030, USA.

    • Todd Lencz &
    • Anil K. Malhotra
  198. The Hofstra NS-LIJ School of Medicine, Hempstead, New York 11549, USA.

    • Todd Lencz &
    • Anil K. Malhotra
  199. The Zucker Hillside Hospital, Glen Oaks, New York 11004, USA.

    • Todd Lencz &
    • Anil K. Malhotra
  200. Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597, Singapore.

    • Jianjun Liu
  201. Queensland Centre for Mental Health Research, University of Queensland, Brisbane 4076, Queensland, Australia.

    • Bryan J. Mowry
  202. Center for Human Genetic Research and Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.

    • Tracey L. Petryshen
  203. Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam 3000, The Netherlands.

    • Danielle Posthuma
  204. Department of Complex Trait Genetics, Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam 1081, The Netherlands.

    • Danielle Posthuma
  205. Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, Amsterdam 1081, The Netherlands.

    • Danielle Posthuma
  206. University of Aberdeen, Institute of Medical Sciences, Aberdeen AB25 2ZD, UK.

    • David St Clair
  207. Departments of Psychiatry, Neurology, Neuroscience and Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA.

    • Daniel R. Weinberger
  208. Department of Clinical Medicine, University of Copenhagen, Copenhagen 2200, Denmark.

    • Thomas Werge

Consortia

  1. Schizophrenia Working Group of the Psychiatric Genomics Consortium

    • Stephan Ripke,
    • Benjamin M. Neale,
    • Aiden Corvin,
    • James T. R. Walters,
    • Kai-How Farh,
    • Peter A. Holmans,
    • Phil Lee,
    • Brendan Bulik-Sullivan,
    • David A. Collier,
    • Hailiang Huang,
    • Tune H. Pers,
    • Ingrid Agartz,
    • Esben Agerbo,
    • Margot Albus,
    • Madeline Alexander,
    • Farooq Amin,
    • Silviu A. Bacanu,
    • Martin Begemann,
    • Richard A. Belliveau Jr,
    • Judit Bene,
    • Sarah E. Bergen,
    • Elizabeth Bevilacqua,
    • Tim B. Bigdeli,
    • Donald W. Black,
    • Richard Bruggeman,
    • Nancy G. Buccola,
    • Randy L. Buckner,
    • William Byerley,
    • Wiepke Cahn,
    • Guiqing Cai,
    • Dominique Campion,
    • Rita M. Cantor,
    • Vaughan J. Carr,
    • Noa Carrera,
    • Stanley V. Catts,
    • Kimberly D. Chambert,
    • Raymond C. K. Chan,
    • Ronald Y. L. Chen,
    • Eric Y. H. Chen,
    • Wei Cheng,
    • Eric F. C. Cheung,
    • Siow Ann Chong,
    • C. Robert Cloninger,
    • David Cohen,
    • Nadine Cohen,
    • Paul Cormican,
    • Nick Craddock,
    • James J. Crowley,
    • David Curtis,
    • Michael Davidson,
    • Kenneth L. Davis,
    • Franziska Degenhardt,
    • Jurgen Del Favero,
    • Ditte Demontis,
    • Dimitris Dikeos,
    • Timothy Dinan,
    • Srdjan Djurovic,
    • Gary Donohoe,
    • Elodie Drapeau,
    • Jubao Duan,
    • Frank Dudbridge,
    • Naser Durmishi,
    • Peter Eichhammer,
    • Johan Eriksson,
    • Valentina Escott-Price,
    • Laurent Essioux,
    • Ayman H. Fanous,
    • Martilias S. Farrell,
    • Josef Frank,
    • Lude Franke,
    • Robert Freedman,
    • Nelson B. Freimer,
    • Marion Friedl,
    • Joseph I. Friedman,
    • Menachem Fromer,
    • Giulio Genovese,
    • Lyudmila Georgieva,
    • Ina Giegling,
    • Paola Giusti-Rodríguez,
    • Stephanie Godard,
    • Jacqueline I. Goldstein,
    • Vera Golimbet,
    • Srihari Gopal,
    • Jacob Gratten,
    • Lieuwe de Haan,
    • Christian Hammer,
    • Marian L. Hamshere,
    • Mark Hansen,
    • Thomas Hansen,
    • Vahram Haroutunian,
    • Annette M. Hartmann,
    • Frans A. Henskens,
    • Stefan Herms,
    • Joel N. Hirschhorn,
    • Per Hoffmann,
    • Andrea Hofman,
    • Mads V. Hollegaard,
    • David M. Hougaard,
    • Masashi Ikeda,
    • Inge Joa,
    • Antonio Julià,
    • René S. Kahn,
    • Luba Kalaydjieva,
    • Sena Karachanak-Yankova,
    • Juha Karjalainen,
    • David Kavanagh,
    • Matthew C. Keller,
    • James L. Kennedy,
    • Andrey Khrunin,
    • Yunjung Kim,
    • Janis Klovins,
    • James A. Knowles,
    • Bettina Konte,
    • Vaidutis Kucinskas,
    • Zita Ausrele Kucinskiene,
    • Hana Kuzelova-Ptackova,
    • Anna K. Kähler,
    • Claudine Laurent,
    • Jimmy Lee Chee Keong,
    • S. Hong Lee,
    • Sophie E. Legge,
    • Bernard Lerer,
    • Miaoxin Li,
    • Tao Li,
    • Kung-Yee Liang,
    • Jeffrey Lieberman,
    • Svetlana Limborska,
    • Carmel M. Loughland,
    • Jan Lubinski,
    • Jouko Lönnqvist,
    • Milan Macek Jr,
    • Patrik K. E. Magnusson,
    • Brion S. Maher,
    • Wolfgang Maier,
    • Jacques Mallet,
    • Sara Marsal,
    • Manuel Mattheisen,
    • Morten Mattingsdal,
    • Robert W. McCarley,
    • Colm McDonald,
    • Andrew M. McIntosh,
    • Sandra Meier,
    • Carin J. Meijer,
    • Bela Melegh,
    • Ingrid Melle,
    • Raquelle I. Mesholam-Gately,
    • Andres Metspalu,
    • Patricia T. Michie,
    • Lili Milani,
    • Vihra Milanova,
    • Younes Mokrab,
    • Derek W. Morris,
    • Ole Mors,
    • Kieran C. Murphy,
    • Robin M. Murray,
    • Inez Myin-Germeys,
    • Bertram Müller-Myhsok,
    • Mari Nelis,
    • Igor Nenadic,
    • Deborah A. Nertney,
    • Gerald Nestadt,
    • Kristin K. Nicodemus,
    • Liene Nikitina-Zake,
    • Laura Nisenbaum,
    • Annelie Nordin,
    • Eadbhard O’Callaghan,
    • Colm O’Dushlaine,
    • F. Anthony O’Neill,
    • Sang-Yun Oh,
    • Ann Olincy,
    • Line Olsen,
    • Jim Van Os,
    • Psychosis Endophenotypes International Consortium,
    • Christos Pantelis,
    • George N. Papadimitriou,
    • Sergi Papiol,
    • Elena Parkhomenko,
    • Michele T. Pato,
    • Tiina Paunio,
    • Milica Pejovic-Milovancevic,
    • Diana O. Perkins,
    • Olli Pietiläinen,
    • Jonathan Pimm,
    • Andrew J. Pocklington,
    • John Powell,
    • Alkes Price,
    • Ann E. Pulver,
    • Shaun M. Purcell,
    • Digby Quested,
    • Henrik B. Rasmussen,
    • Abraham Reichenberg,
    • Mark A. Reimers,
    • Alexander L. Richards,
    • Joshua L. Roffman,
    • Panos Roussos,
    • Douglas M. Ruderfer,
    • Veikko Salomaa,
    • Alan R. Sanders,
    • Ulrich Schall,
    • Christian R. Schubert,
    • Thomas G. Schulze,
    • Sibylle G. Schwab,
    • Edward M. Scolnick,
    • Rodney J. Scott,
    • Larry J. Seidman,
    • Jianxin Shi,
    • Engilbert Sigurdsson,
    • Teimuraz Silagadze,
    • Jeremy M. Silverman,
    • Kang Sim,
    • Petr Slominsky,
    • Jordan W. Smoller,
    • Hon-Cheong So,
    • ChrisC. A. Spencer,
    • Eli A. Stahl,
    • Hreinn Stefansson,
    • Stacy Steinberg,
    • Elisabeth Stogmann,
    • Richard E. Straub,
    • Eric Strengman,
    • Jana Strohmaier,
    • T. Scott Stroup,
    • Mythily Subramaniam,
    • Jaana Suvisaari,
    • Dragan M. Svrakic,
    • Jin P. Szatkiewicz,
    • Erik Söderman,
    • Srinivas Thirumalai,
    • Draga Toncheva,
    • Sarah Tosato,
    • Juha Veijola,
    • John Waddington,
    • Dermot Walsh,
    • Dai Wang,
    • Qiang Wang,
    • Bradley T. Webb,
    • Mark Weiser,
    • Dieter B. Wildenauer,
    • Nigel M. Williams,
    • Stephanie Williams,
    • Stephanie H. Witt,
    • Aaron R. Wolen,
    • Emily H. M. Wong,
    • Brandon K. Wormley,
    • Hualin Simon Xi,
    • Clement C. Zai,
    • Xuebin Zheng,
    • Fritz Zimprich,
    • Naomi R. Wray,
    • Kari Stefansson,
    • Peter M. Visscher,
    • Wellcome Trust Case-Control Consortium,
    • Rolf Adolfsson,
    • Ole A. Andreassen,
    • Douglas H. R. Blackwood,
    • Elvira Bramon,
    • Joseph D. Buxbaum,
    • Anders D. Børglum,
    • Sven Cichon,
    • Ariel Darvasi,
    • Enrico Domenici,
    • Hannelore Ehrenreich,
    • Tõnu Esko,
    • Pablo V. Gejman,
    • Michael Gill,
    • Hugh Gurling,
    • Christina M. Hultman,
    • Nakao Iwata,
    • Assen V. Jablensky,
    • Erik G. Jönsson,
    • Kenneth S. Kendler,
    • George Kirov,
    • Jo Knight,
    • Todd Lencz,
    • Douglas F. Levinson,
    • Qingqin S. Li,
    • Jianjun Liu,
    • Anil K. Malhotra,
    • Steven A. McCarroll,
    • Andrew McQuillin,
    • Jennifer L. Moran,
    • Preben B. Mortensen,
    • Bryan J. Mowry,
    • Markus M. Nöthen,
    • Roel A. Ophoff,
    • Michael J. Owen,
    • Aarno Palotie,
    • Carlos N. Pato,
    • Tracey L. Petryshen,
    • Danielle Posthuma,
    • Marcella Rietschel,
    • Brien P. Riley,
    • Dan Rujescu,
    • Pak C. Sham,
    • Pamela Sklar,
    • David St Clair,
    • Daniel R. Weinberger,
    • Jens R. Wendland,
    • Thomas Werge,
    • Mark J. Daly,
    • Patrick F. Sullivan &
    • Michael C. O’Donovan

Contributions

The individual studies or consortia contributing to the GWAS meta-analysis were led by R.A., O.A.A., D.H.R.B., A.D.B., E. Bramon, J.D.B., A.C., D.A.C., S.C., A.D., E. Domenici, H.E., T.E., P.V.G., M.G., H.G., C.M.H., N.I., A.V.J., E.G.J., K.S.K., G.K., J. Knight, T. Lencz, D.F.L., Q.S.L., J. Liu, A.K.M., S.A.M., A. McQuillin, J.L.M., P.B.M., B.J.M., M.M.N., M.C.O’D., R.A.O., M.J.O., A. Palotie, C.N.P., T.L.P., M.R., B.P.R., D.R., P.C.S, P. Sklar. D.St.C., P.F.S., D.R.W., J.R.W., J.T.R.W. and T.W. Together with the core statistical analysis group led by M.J.D. comprising S.R., B.M.N. and P.A.H., this group comprised the management group led by M.C.O’D. who were responsible for the management of the study and the overall content of the manuscript. Additional analyses and interpretations were contributed by E.A., B.B.-S., D.K., K.-H.F., M. Fromer, H.H., P.L., P.B.M., S.M.P., T.H.P., N.R.W. and P.M.V. The phenotype supervisory group comprised A.C., A.H.F., P.V.G., K.K.K. and B.J.M. D.A.C. led the candidate selected genes subgroup comprised of M.J.D., E. Dominici, J.A.K., A.M.H., M.C.O’D, B.P.R., D.R., E.M.S. and P. Sklar. Replication results were provided by S.S., H.S. and K.S. The remaining authors contributed to the recruitment, genotyping, or data processing for the contributing components of the meta-analysis. A.C., M.J.D., B.M.N., S.R., P.F.S. and M.C.O’D. took responsibility for the primary drafting of the manuscript which was shaped by the management group. All other authors saw, had the opportunity to comment on, and approved the final draft.

Competing financial interests

CFI statement–Several of the authors are employees of the following pharmaceutical companies; Pfizer (C.R.S., J.R.W., H.S.X.), F.Hoffman-La Roche (E.D., L.E.), Eli Lilly (D.A.C., Y.M., L.N.) and Janssen (S.G., D.W., Q.S.L.; also N.C. an ex-employee). Others are employees of deCODE genetics (S.S, H.S., K.S.). None of these companies influenced the design of the study, the interpretation of the data, or the amount of data reported, or financially profit by publication of the results which are pre-competitive. The other authors declare no competing interests.

Corresponding author

Correspondence to:

Results can be downloaded from the Psychiatric Genomics Consortium website (http://pgc.unc.edu) and visualized using Ricopili (http://www.broadinstitute.org/mpg/ricopili). Genotype data for the samples where the ethics permit deposition are available upon application from the NIMH Genetics Repository (https://www.nimhgenetics.org).

Author details

    Extended data figures and tables

    Extended Data Figures

    1. Extended Data Figure 1: Homogeneity of effects across studies. (87 KB)

      Plot of the first two principal components (PCs) from principal components analysis (PCA) of the logistic regression β coefficients for autosomal genome-wide significant associations. The input data were the β coefficients from 52 samples for 112 independent SNP associations (excluding 3 chrX SNPs and 13 SNPs with missing values in Asian samples). PCAs were weighted by the number of cases. Each circle shows the location of a study on PC1 and PC2. Circle size and colour are proportional to the number of cases in each sample (larger and darker red circles correspond to more cases). Most samples cluster. Outliers had either small numbers of cases (‘small’) or were genotyped on older arrays. Abbreviations: a500 (Affymetrix 500K); a5 (Affymetrix 5.0). Studies that did not use conventional research interviews are in the central cluster (CLOZUK, Sweden, and Denmark-Aarhus studies, see Supplementary Methods for sample descriptions).

    2. Extended Data Figure 2: Quantile-quantile plot. (111 KB)

      Quantile-quantile plot of the discovery genome-wide association meta-analysis of 49 case control samples (34,241 cases and 45,604 controls) and 3 family based association studies (1,235 parent affected-offspring trios). Expected –log10 P values are those expected under the null hypothesis. Observed are the GWAS association results derived by logistic regression (2-tailed) as in Fig. 1. For clarity, we avoided expansion of the y axis by setting the smallest association P values to 10−12. The shaded area surrounded by a red line indicates the 95% confidence interval under the null. λGC is the observed median χ2 test statistic divided by the median expected χ2 test statistic under the null hypothesis.

    3. Extended Data Figure 3: Linkage disequilibrium score regression consistent with polygenic inheritance. (273 KB)

      The relationship between marker χ2 association statistics and linkage disequilibrium (LD) as measured by the linkage disequilibrium score. Linkage disequilibrium score is the sum of the r2 values between a variant and all other known variants within a 1cM window, and quantifies the amount of genetic variation tagged by that variant. Variants were grouped into 50 equal-sized bins based on linkage disequilibrium score rank. Linkage disequilibrium score bin and mean χ2 denotes mean linkage disequilibrium score and test statistic for markers each bin. a, b, We simulated (Supplementary Methods) test statistics under two scenarios: a, no true association, inflation due to population stratification; and b, polygenic inheritance (λ = 1.32), in which we assigned independent and identically distributed per-normalized-genotype effects to a randomly selected subset of variants. c, Results from the PGC schizophrenia GWAS (λ = 1.48). The real data are strikingly similar to the simulated data summarized in b but not a. The intercept estimates the inflation in the mean χ2 that results from confounding biases, such as cryptic relatedness or population stratification. Thus, the intercept of 1.066 for the schizophrenia GWAS suggests that ~90% of the inflation in the mean χ2 results from polygenic signal. The results of the simulations are also consistent with theoretical expectation (see Supplementary Methods). λ is the median χ2 test statistic from the simulations (a, b) or the observed data (c) divided by the median expected χ2 test statistic under the null hypothesis.

    4. Extended Data Figure 4: Enrichment of associations in tissues and cells. (259 KB)

      Genes whose transcriptional start is nearest to the most associated SNP at each schizophrenia-associated locus were tested for enriched expression in purified brain cell subsets obtained from mouse ribotagged lines41 using enrichment analysis described in the Supplementary Methods. The red dotted line indicates P = 0.05.

    5. Extended Data Figure 5: MGS risk profile score analysis. (110 KB)

      Polygenic risk profile score (RPS) analyses using the MGS18 sample as target, and deriving risk alleles from three published schizophrenia data sets (x axis): ISC (2,615 cases and 3,338 controls)10, PGC1 (excluding MGS, 9,320 cases and 10,228 controls)16, and the current meta-analysis (excluding MGS) with 32,838 cases and 44,357 controls. Samples sizes differ slightly from the original publications due to different analytical procedures. This shows the increasing RPS prediction with increasing training data set size reflecting improved precision of estimates of the SNP effect sizes. The proportion of variance explained (y axis; Nagelkerke’s R2) was computed by comparison of a full model (covariates + RPS) score to a reduced model (covariates only). Ten different P value thresholds (PT) for selecting risk alleles are denoted by the colour of each bar (legend above plot). For significance testing, see the bottom legend which denotes the P value for the test that R2 is different from zero. All numerical data and methods used to generate these plots are available in Supplementary Table 6 and Supplementary Methods.

    6. Extended Data Figure 6: Risk profile score analysis. (448 KB)

      We defined 40 target subgroups of the primary GWAS data set and performed 40 leave-one-out GWAS analyses (see Supplementary Methods and Supplementary Table 7) from which we derived risk alleles for RPS analysis (x axis) for each target subgroup. a, The proportion of variance explained (y axis; Nagelkerke’s R2) was computed for each target by comparison of a full model (covariates + RPS) score to a reduced model (covariates only). For clarity, 3 different P value thresholds (PT) are presented denoted by the colour of each bar (legend above plot) as for Extended Data Fig. 5, but for clarity we restrict to fewer P value thresholds (PT of 5×10−8, 1×10−4 and 0.05) and removed the significance values. b, The proportion of variance on the liability scale from risk scores calculated at the PT 0.05 with 95% CI bar assuming baseline population disease risk of 1%. c, Area under the receiver operating curve (AUC). All numerical data and methods used to generate these plots are available in Supplementary Table 7 and Supplementary Methods.

    7. Extended Data Figure 7: Pairwise epistasis analysis of significant SNPs. (106 KB)

      Quantile-quantile plot for all pair-wise (n = 7,750) combinations of the 125 independent autosomal genome-wide significant SNPs tested for non-additive effects on risk using case-control data sets of European ancestry (32,405 cases and 42,221 controls). We included as covariates the principal components from the main analysis as well as a study indicator. The interaction model is described by:

      and are genotypes at the two loci, is the interaction between the two genotypes modelled in a multiplicative fashion, is the vector of principal components, is the vector of study indicator variables. Each is the regression coefficient in the generalized linear model using logistic regression. The overall distribution of P values did not deviate from the null and the smallest P value (4.28×10−4) did not surpass the Bonferroni correction threshold (P = 0.05/7750 = 6.45×10−6). The line x = y indicates the expected null distribution with the grey area bounded by red lines indicating the expected 95% confidence interval for the null.

    Extended Data Tables

    1. Extended Data Table 1: ALIGATOR and INRICH (182 KB)
    2. Extended Data Table 2: de novo overlap (213 KB)

    Supplementary information

    PDF files

    1. Supplementary Information (1.7 MB)

      This file contains Supplementary Text, Supplementary Tables 1-3, Supplementary References and Supplementary Notes (including a list of consortium members and acknowledgements) – see contents page for details.

    2. Supplementary Figure (2.1 MB)

      This file contains Supplementary Figure 1.

    Excel files

    1. Supplementary Table 4 (106 KB)

      Credible causal schizophrenia SNPs, coding variants, and eQTLs. Worksheet 1: Coding variants: Index SNP is the schizophrenia associated SNP defining the schizophrenia associated region. Coding variant, R2, and gene denotes a coding credible SNP and the R2 with the index SNP, and the gene containing the coding variant. CHR (chromosome), BP (base position), A1A2 (alleles 1 and 2), frequencies of allele 1 (FRQ_A1), INFO (imputation quality) and P (P-value) refer to the index SNP in the discovery GWAS. P (incl rep) refers to replication P value for index SNP. Worksheets 2 and 3: Brain and blood eQTL: Credible SNP denotes a SNP within the schizophrenia credible set (defined in supplementary material) that is also a cis eQTL (transcript within 1Mb, PeQTL<1x10-4). P(cSCZ) is the schizophrenia (discovery) GWAS association P-value for the credible SNP. The Prob(cSCZ) is the normalized probability of the credible variant being causal for schizophrenia. N(cSCZ) is the number of variants in the credible set of schizophrenia variants within a region spanned by eQTLs at P<10-4. eQTL SNP is the most significant expression associated SNP in the region for the gene in next column (N.B., many regions have an eQTL for more than 1 gene). eQTLgene is the gene that is linked to the eQTL SNP. P(eQTL) is the association P-value between the eQTL SNP and the eQTLgene in the previous two columns. Prob(eQTL) is the normalized probability that the eQTL SNP is also the causal SNP for schizophrenia (high values mean higher probability of being causal). eQTLcumsum is the cumulative sum of the probability of all SNPs into the region, up to the inclusion of the max eQTL in locus ordered by probability of being the functional SNP. PeQTL(SCZ) is the schizophrenia association P-value for the eQTL SNP. R2 (cSCZ/ eQTL) is the R2 between the credibleSNP and eQTL SNP. Associations to schizophrenia that are plausibly explained by an eQTL are in bold. Separate worksheets provide information on brain and blood eQTL analyses. Distinct loci are alternately shaded/unshaded.

    2. Supplementary Table 5 (173 KB)

      Pathway analyses by ALIGATOR and INRICH. Enrichment analyses using ALIGATOR and INRICH were performed as described in Supplementary Text. Pathway ID denotes the pathway source: GO (Gene ontology; http://www.geneontology.org), KEGG (Kyoto Encyclopaedia of Genes and Genomes; http://www.genome.jp/kegg), PAN-PW (PANTHER; http://www.pantherdb.org/pathway), Reactome (http://www.reactome.org/download), BioCarta (downloaded from the Molecular Signatures Database v4.0 http://www.broadinstitute.org/gsea/msigdb/index.jsp), MGI (Mouse Genome Informatics; http://www.informatics.jax.org), and NCI pathways (NCI: http://pid.nci.nih.gov).

    3. Supplementary Table 7 (96 KB)

      Risk Profile Score Analyses. Risk Profile Score (RPS) analysis was performed as described in supplementary text. RPS datasets tab provides the name given for sample in which RPS was performed (target label) and the datasets included (defined in Supplementary Table 1). The GWAS data used to define the risk alleles for RPS analysis represents the remaining GWAS samples. For various GWAS P-value thresholds (denoted PT), we calculated: 1) the significance of the case-control score difference was analyzed (P tab), 2) the proportion of variance explained (Nagelkerke’s R2, R2 tab), 3) the proportion of variance on the liability scale explained by RPS (h2I tab) with standard error in brackets, 4) area under the receiver operator characteristic curve (AUC tab), and 5) odds ratio for the 10th RPS decile group compared with lowest decile with confidence interval in brackets. Ncases tab denotes number of cases in each target set.

    4. Supplementary Table 6 (32 KB)

      RPS analysis of MGS sample. Risk Profile Score (RPS) analyses was performed using the MGS dataset as target, using three distinct published results for SCZ GWAS, from the (1) ISC (2009) study of 2615 cases and 3338 controls11 (denoted ISC columns) (2) PGC1 (excluding MGS, denoted PGC1 columns) with 9320 cases and 10228 controls22, (3) current meta analysis (excluding MGS, denoted Current columns) with 32838 cases and 44357 controls. For various GWAS P value thresholds (denoted PT), we calculated 1) the significance of the case-control score difference was analyzed (P tab) 2) The proportion of variance explained (Nagelkerke’s R2, R2 tab) 3) The proportion of variance on the liability scale explained by RPS (h2I tab) with standard error in brackets 4) Area under the receiver operator characteristic curve (AUC tab) and 5) Odds ratio for 10th RPS decile group compared with lowest decile with confidence interval in brackets. Ncases tab denotes number of cases in each target set.

    Comments

    1. Report this comment #63705

      Hugh Fletcher said:

      This suggests two things: 1/ That studies of neurotransmission are easier than studies of the immune system, and the known involvement of the immune system in autism, OCD and schizophrenia has been largely ignored, just as APOE was neglected for amyloid precursor in studies of Alzheimer's. Stoner shows evidence of focal damage to layers of the cortex of brains of children with autism (N Engl J Med 2014; 370:1209-1219 March 27, 2014). These look similar to viral plaques and could easily involve the immune system, either failing to prevent infection or locally causing autoimune damage to specific cell types. Overgrowth could be a response to specific deficiencies. 2/ That mental health is subject to chaos theory. None of these alleles are necessary or sufficient. Particular combinations of tiny factors, many unpredictable, produce deviations from the norm. When these exceed a threshold they can be described as illness. If you think of human brains distributed as a pile of sand, most are in the central heap and are in the normal range. Near the edge is a line around which the difference from normal becomes noticable and diagnosis of a disorder becomes more likely. The line is nearer the centre in countries with a well developed health system and poor support for individuals. The designation of the disorder depends on where on the rim of the mound it falls; the exact location and severity of the lesions. This huge collection of data may be distracting. In type II diabetes, any loci affecting the immune system, insulin production or response, or glucose metabolism are risk factors. When a system approaches a state of failure any little thing that agrevates the situation may appear as a cause of collapse. Hopefully a unified picture of the primary system failure will emerge. It may be cell loss, rather than synaptic problems.

    2. Report this comment #63843

      Michael Lerman said:

      The CALL/CHL1 gene on 3p26 (first gene on 3p discovered by MH Wei and myself) was found strongly associated with schizophrenia in Japanese (Sakurai et al Mol Psychiatry, 7,412, 2002) and Chinese populations (Chen QY et al, Schizophrenia Research, 73, 269, 2005). However a brief survey of Table 3 of the Supplementary Information did not show the CALL/CHL1 gene. Does this mean this gene is specific for the Chinese and Japanese populations? Michael Lerman, Ph.D., M.D.

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