psychiatricdisorders

Articles www.thelancet.com Published online February 28, 2013 http://dx.doi.org/10.1016/S0140-6736(12)62129-1 1 Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis Cross-Disorder Group of the Psychiatric Genomics Consortium* Summary Background Findings from family and twin studies suggest that genetic contributions to psychiatric disorders do not in all cases map to present diagnostic categories. We aimed to identify specific variants underlying genetic effects shared between the five disorders in the Psychiatric Genomics Consortium: autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia. Methods We analysed genome-wide single-nucleotide polymorphism (SNP) data for the five disorders in 33 332 cases and 27 888 controls of European ancestory. To characterise allelic effects on each disorder, we applied a multinomial logistic regression procedure with model selection to identify the best-fitting model of relations between genotype and phenotype. We examined cross-disorder effects of genome-wide significant loci previously identified for bipolar disorder and schizophrenia, and used polygenic risk-score analysis to examine such effects from a broader set of common variants. We undertook pathway analyses to establish the biological associations underlying genetic overlap for the five disorders. We used enrichment analysis of expression quantitative trait loci (eQTL) data to assess whether SNPs with cross-disorder association were enriched for regulatory SNPs in post-mortem brain-tissue samples. Findings SNPs at four loci surpassed the cutoff for genome-wide significance (p<5×10–⁸) in the primary analysis: regions on chromosomes 3p21 and 10q24, and SNPs within two L-type voltage-gated calcium channel subunits, CACNA1C and CACNB2. Model selection analysis supported effects of these loci for several disorders. Loci previously associated with bipolar disorder or schizophrenia had variable diagnostic specificity. Polygenic risk scores showed cross-disorder associations, notably between adult-onset disorders. Pathway analysis supported a role for calcium channel signalling genes for all five disorders. Finally, SNPs with evidence of cross-disorder association were enriched for brain eQTL markers. Interpretation Our findings show that specific SNPs are associated with a range of psychiatric disorders of childhood onset or adult onset. In particular, variation in calcium-channel activity genes seems to have pleiotropic effects on psychopathology. These results provide evidence relevant to the goal of moving beyond descriptive syndromes in psychiatry, and towards a nosology informed by disease cause. Funding National Institute of Mental Health. Introduction Psychiatric nosology arose in central Europe towards the end of the 19th century, in particular with Kraepelin’s foundational distinction between dementia praecox (schizophrenia) and manic depressive insanity.1 The distinction between bipolar illness and unipolar (major) depression was first proposed in the late 1950s and became increasingly widely accepted. The major syn­ dromes—especially schizophrenia, bipolar disorder, and major depression—were differentiated on the basis of their symptom patterns and course of illness. At the same time, clinical features such as psychosis, mood dysregulation, and cognitive impairments were known to transcend diagnostic categories. Doubt remains about the boundaries between the syndromes and the degree to which they signify entirely distinct entities, disorders that have overlapping foundations, or different variants of one underlying disease. Such debates have inten si fied with syndromes described subsequently, including autism spectrum disorders and attention deficit­hyperactivity disorder. The pathogenic mechanisms of psychiatric disorders are largely unknown, so diagnostic boundaries are difficult to define. Genetic risk factors are important in the causation of all major psychiatric disorders,2 and genetic strategies are widely used to assess potential overlaps. The imminent revision of psychiatric classifi­ cations in the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classifi­ cation of Diseases (ICD) has reinvigorated debate about the validity of diagnostic boundaries. With increasing availability of large genome­wide genotype data for several psychiatric disorders, shared cause can now be examined at a molecular level. We formed the Psychiatric Genomics Consortium (PGC) in 2007, to undertake meta­analyses of genome­ wide association studies (GWAS) for psychiatric dis­ orders and, so far, the consortium has incorporated GWAS data from more than 19 countries for schizo­ phrenia, bipolar disorder, major depressive disorder, attention deficit­hyperactivity disorder, and autism spectrum disorders. Previous research has suggested Published Online February 28, 2013 http://dx.doi.org/10.1016/ S0140-6736(12)62129-1 See Online/Comment http://dx.doi.org/10.1016/ S0140-6736(13)60223-8 *Members listed at end of paper Correspondence to: Dr Jordan W Smoller, Simches Research Building, Massachusetts General Hospital, Boston, MA 02114, USA jsmoller@hms.harvard.edu Articles 2 www.thelancet.com Published online February 28, 2013 http://dx.doi.org/10.1016/S0140-6736(12)62129-1 varying degrees of overlap in familial and genetic liability for pairs of these disorders. For example, some findings3,4 from family and twin studies support diagnostic boun­ daries between schizophrenia and bipolar disorder and bipolar disorder and major depressive disorder, but also suggest correlations in familial and genetic liabili ties.3,5 Several molecular variants confer risk of both schizo­ phrenia and bipolar disorder.6–8 Autism was once known as childhood schizophrenia and the two disorders were not clearly differentiated until the 1970s. Findings from the past few years have emphasised phenotypic and genetic overlap between autism spec trum disorders and schizophrenia,9,10 including identifi cation of copy number variants conferring risk of both.11 Findings from family, twin, and molecular studies12–15 suggest some genetic overlap between autism spectrum disorder and attention deficit­hyperactivity disorder. In this first report from the PGC Cross­Disorder Group, we analyse data on genome­wide single­nucleotide poly­ morphism (SNP) for the five PGC dis orders to answer two questions. First, what information emerges when all five disorders are examined in one GWAS? When risk is correlated across disorders, pooled analyses will be better powered than individual­disorder analyses to detect risk loci. Second, what are the cross­disorder effects of variants already identified as being associated with a specific psychiatric disorder in previous PGC analyses? We aimed to examine the genetic relation between the five psychiatric disorders with the expectation that findings will ultimately inform psychiatric nosology, identify potential neuro­ biological mechanisms predisposing to specific clinical presentations, and generate new models for prevention and treatment. Methods Samples and genotypes The sample for these analyses consisted of cases, controls, and family­based samples assembled for previous genome­wide PGC mega­analyses of individual­ level data.6,7,16,17 Cases and controls were not related. For the family­based samples, we matched alleles transmitted to affected offspring (trio cases) with untransmitted alleles (pseudocontrols). We estimated the identity­by­ descent relation for all pairs of individuals to identify any duplicate individuals in the component datasets. When duplicates were detected, one member of each set was retained. We then randomly allocated these individuals, with a random number generator, to a disorder case­ control dataset. Sample sizes differ from previous reports because of this allocation of overlapping individuals. All patients were of European ancestory and met criteria from the DSM third edition revised or fourth edition for the primary disorder of interest. To ensure comparability between samples, raw geno­ type and phenotype data for each study were uploaded to a central server and processed through the same quality control, imputation, and analysis process (appendix).6,7 We analysed imputed SNP dosages from 1 250 922 autosomal SNPs. Statistical analysis In the primary analysis, we combined effects of each disease analysis by a meta­analytic approach that applied a weighted Z­score,18 in which weights equalled the inverse of the regression coefficient’s standard error. This strategy assumed a fixed­effects model, with weights indicating the sample size of the disease­specific studies. In a second analytical approach, we did a five­degree­of­ freedom test by summing the χ² values for each individual disease meta­analysis. Unlike our primary analysis, this model did not assume that all diseases had the same direction of effect and could detect allelic effects that increase risk for some diseases and decrease risk for others. The appendix describes statistical methods and results, including the handling of trios and population stratification. We also examined loci that previously achieved genome­wide significance in PGC meta­ analyses of schizophrenia and bipolar disorder.6,7 To characterise the specificity of the allelic effects for our main findings, we examined the association evidence in three ways: we generated forest plots of the disorder beta coefficients with 95% CIs; we calculated a hetero­ geneity p value for the disorder­specific effects con­ tributing to the overall statistics for meta­analytic association; and we undertook a multinomial logistic regression procedure with model selection19 for each main SNP for all five disorders to assess the pattern of phenotypic effects (appendix pp 8–11). To compare the fit of various models of genotype–phenotype associations, we applied established goodness­of­fit metrics (the Bayesian information criteria and the Akaike information criteria). We report the best­fitting model by Bayesian criteria and show results of both metrics for a range of models (appendix pp 38–45, 51–61). To examine shared polygenic risk at an aggregate level between pairs of diagnoses, we used risk­score profiling as previously described.8 For each pair, we selected one disorder as a discovery dataset and the other as a target dataset and calculated the proportion of variance in the target set explained by risk scores from the discovery set with a range of statistical cutoffs for SNP inclusion in the score (appendix p 13). To assess the role of specific biological systems in the pathogenesis of the five disorders, we did pathway and eQTL analyses. Pathway analysis was by interval­based enrichment analysis (INRICH) for the full dataset consisting of linkage disequilibrium segments containing signals with association p<10–³ in the primary meta­analysis. INRICH accounts for poten tial genomic confounding factors, such as variable gene and pathway sizes, SNP density, linkage disequilibrium, and physical clustering of biologically related genes (appendix pp 14–16). We did eQTL enrichment analysis20 to assess whether SNPs associated with five psychiatric disorders were enriched for regulatory SNPs in post­mortem brain tissue See Online for appendix For INRICH see http://atgu.mgh. harvard.edu/inrich Articles www.thelancet.com Published online February 28, 2013 http://dx.doi.org/10.1016/S0140-6736(12)62129-1 3 samples compared with those with no association.21,22 To assess the specificity of this finding, we also examined eQTL datasets from three non­brain­tissue types: liver,23 skin,24 and lymphoblastoid cell lines25 (appendix pp 17–21). Role of the funding source The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data and had final responsibility for the decision to submit for publication. Results The final dataset consisted of 33 332 cases and 27 888 con­ trols (including pseudocontrols formed from non­ transmitted alleles) distributed among the five disorder groups: autism spectrum disorders (4788 trio cases, 4788 trio pseudocontrols, 161 cases, 526 controls), attention deficit­hyperactivity disorder (1947 trio cases, 1947 trio pseudocontrols, 840 cases, 688 controls), bipolar disorder (6990 cases, 4820 con trols), major depressive disorder (9227 cases, 7383 con trols), and schizophrenia (9379 cases, 7736 controls). The results of the primary fixed­effects meta­analysis for all five disorders, incorp orating seven multidimensional scaling components as covariates, yielded a genomic control value of λ=1·167. The λ1000 (λ rescaled to a sample of 1000 cases and 1000 controls) was 1·005 (appendix p 22). In view of evidence for substantial polygenic con­ tributions to common psy chiatric disorders, this estimate probably shows the aggregate small effect of a large number of risk variants, although a moderate Figure 1: Manhattan plot of primary fixed-effects meta-analysis Horizontal line shows threshold for genome-wide significance (p<5×10⁻⁸). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 1 2 3 4 5 6 7 8 9 10 11 12 13 14 –lo g1 0 ( p va lu e) Chromosome MIR137 (+1) ITIH3 (+35) HINT1 (+7) MHC (369) ZFPM2 CACNB2 CACNA1C CPNE7 (+12) TCF4 NEURL (+25)SYNE1 FPR2 (+11) Chromosome Base-pair position* Nearest gene Alleles Frequency† Imputation quality score (INFO) p value OR (95% CI)‡ Heterogeneity p value Best-fit model (BIC)§ rs2535629 3 52808259 ITIH3 (+ many) G/A 0·651 0·942 2·54×10⁻¹² 1·10 (1·07–1·12) 0·27 Five disorder¶ rs11191454 10 104649994 AS3MT (+ many) A/G 0·910 1·01 1·39×10⁻⁸ 1·13 (1·08 –1·18) 0·32 Five disorder¶ rs1024582 12 2272507 CACNA1C A/G 0·337 0·98 1·87×10⁻⁸ 1·07 (1·05-1·10) 0·0057 BPD, schizophrenia rs2799573 10 18641934 CACNB2 T/C 0·715 0·825 4·29×10⁻⁸ 1·08 (1·05-1·12) 0·57 Five disorder¶ Most strongly associated single-nucleotide polymorphisms (SNP) in associated region after clumping—ie, grouping SNPs within 250 kb of the index SNP that have r²>0·2 with the index SNP as implemented in PLINK. OR=odds ratio. BIC=Bayesian information criteria. BPD=bipolar disorder. *Detected with University of California Santa Cruz Genome Browser (version hg18). †Risk allele frequency in controls. ‡Estimated OR from multinomial logistic regression used in the modelling analysis. §Best-fit multinomial logistic model by BIC criteria; appendix pp 38–45 provide a comparison of BIC and Akaike information criteria across models. ¶Best-fit model supports an effect on all five disorders. Table 1: Five disorder meta-analysis results for regions with p<5×10⁻⁸ Articles 4 www.thelancet.com Published online February 28, 2013 http://dx.doi.org/10.1016/S0140-6736(12)62129-1 degree of population stratification or technical bias cannot be excluded. Figure 1 shows the Manhattan plot of the primary results. Four independent regions contained SNPs with p<5×10–⁸ (table 1; appendix pp 34–35, 25–33). The strongest association signal was on chromosome 3 at an intronic SNP within ITIH3 (table 1). This SNP is in linkage disequilibrium with SNPs encompassing several Figure 2: Association results and forest plots showing effect size for genome-wide significant loci by disorder Data in parentheses are numbers of cases or controls. Het_p=p value for the heterogeneity test. Het_I=heterogeneity test statistic. IQS=imputation quality score (INFO). ln(OR)=log of the odds ratio (OR). F=frequency. SE=standard error of the log OR. ADHD=attention deficit-hyperactivity disorder. ASD=autism spectrum disorders. BPD=bipolar disorder. MDD=major depressive disorder. *Number of studies in which the variant was directly genotyped. rs2535629 G/A 3:52808259 ADHD ASD BPD MDD Schizophrenia All Studies* 0 0 3 1 6 10 In(OR) 0·0535 0·0495 0·139 0·0913 0·0993 0·0908 SE 0·0418 0·0306 0·0308 0·0247 0·0249 0·013 F (cases) 0·339 (2787) 0·334 (4949) 0·325 (6990) 0·336 (9227) 0·332 (9379) 0·333 (33 332) F (controls) 0·350 (2635) 0·347 (5314) 0·348 (4820) 0·351 (7383) 0·350 (7736) 0·349 (27 888) IQS 0·94 0·95 0·90 0·97 0·94 0·94 p value 0·201 0·196 6·61×10–6 0·000216 6·71×10–5 2·54×10–12 A het_p: het_I: –15·70·27 rs11191454 A/G 10:104649994 ADHD ASD BPD MDD Schizophrenia All Studies* 1 1 7 1 12 22 In(OR) 0·0649 0·0733 0·127 0·098 0·19 0·12 SE 0·0698 0·05 0·0495 0·0406 0·0409 0·0212 F (cases) 0·918 (2787) 0·915 (4949) 0·920 (6990) 0·916 (9227) 0·921 (9379) 0·918 (33 332) F (controls) 0·914 (2635) 0·910 (5314) 0·912 (4820) 0·909 (7383) 0·908 (7736) 0·910 (27 888) IQS 0·99 1·01 1·01 1·00 1·03 1·01 p value 0·355 0·143 0·0107 0·0156 3·48×10–6 1·39×10–8 B het_p: het_I: –27·10·32 rs1024582 A/G 12:2272507 ADHD ASD BPD MDD Schizophrenia All Studies* 0 2 0 0 0 2 In(OR) 0·0639 0·00399 0·144 0·0383 0·103 0·0714 SE 0·0418 0·0301 0·0296 0·0244 0·0244 0·0127 F (cases) 0·342 (2787) 0·331 (4949) 0·362 (6990) 0·344 (9227) 0·357 (9379) 0·349 (33 332) F (controls) 0·328 (2635) 0·333 (5314) 0·335 (4820) 0·341 (7383) 0·340 (7736) 0·337 (27 888) IQS 0·96 0·99 0·98 0·98 0·98 0·98 p value 0·127 0·892 1·12×10–6 0·12 2·84×10–5 1·87×10–8 C het_p: het_I: 58·80·01 0 0·05–0·05 0·10 0·15 0·20 0·25 In(OR), 95% CI rs2799573 T/C 10:18641934 ADHD ASD BPD MDD Schizophrenia All Studies* 2 6 3 6 4 21 In(OR) 0·132 0·0402 0·0667 0·088 0·0935 0·0807 SE 0·0489 0·0337 0·0356 0·0268 0·0296 0·0147 F (cases) 0·745 (2787) 0·739 (4949) 0·723 (6990) 0·725 (9227) 0·724 (9379) 0·728 (33 332) F (controls) 0·726 (2635) 0·734 (5314) 0·709 (4820) 0·707 (7383) 0·711 (7736) 0·715 (27 888) IQS 0·82 0·91 0·74 0·92 0·73 0·82 p value 0·00691 0·238 0·0617 0·00108 0·00161 4·29×10–8 D het_p: het_I: 0·00·56 Articles www.thelancet.com Published online February 28, 2013 http://dx.doi.org/10.1016/S0140-6736(12)62129-1 5 genes across a 1 Mb region (appendix p 22). The second strongest signal was in an intron of AS3MT on chromo­ some 10q24 (table 1). Linkage disequilibrium around this associated region encompasses several genes including CNNM2. We also recorded genome­wide significant association within CACNA1C, and finally detected significant association to a second locus on chromo­ some 10 in an intron of CACNB2 (table 1). We undertook conditional analyses to assess evidence for multirisk loci in a region. In these analyses, we included the most strongly associated or peak SNP plus any SNPs within 1·5 Mb of the peak SNP with association p values less than 10–⁴ and r² less than 0·2 with the peak SNP based on HapMap 3 CEU data. For the chromosome 3p21 region, and regions CACNA1C and CACNB2, no additional independent association signals were de tected. For the chromosome 10q24 region, an additional SNP (rs11191732), about 600 kb from the peak SNP, showed association after conditioning on the peak SNP (rs11191454) with a p value of 6·60×10–⁶ before con­ ditioning and 3·88×10–⁵ after conditioning. Several loci previously implicated in PGC analyses of schizophrenia and bipolar disorder6,7 showed evidence for association in the cross­disorder analysis, despite not exceeding the cutoff for genome­wide significance (appendix pp 23–24). These loci include one near MIR137, TCF4, the MHC region on chromosome 6, and SYNE1 (appendix pp 23–24).