HHS Public Access Author manuscript Nat Genet. Author manuscript; available in PMC 2020 January 13. Author Manuscript Published in final edited form as: Nat Genet. 2019 May ; 51(5): 793–803. doi:10.1038/s41588-019-0397-8. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms & Correspondence to: jkelsoe@ucsd.edu or eli.stahl@mssm.edu. †Equal contribution *Co-last authors Author Contributions: Writing group: E.A.S., G.B., A.J.F., A.McQuillin, S.R., J.R.I.C., N.M., N.R.W., A.D.F., H.J.E., S.C., R.A.O., L.J.S., O.A.A., J.K. PGC BD PI group: E.A.S., G.B., A.J.F., A.McQuillin, D.Curtis, R.H.P., R.A., I.A., M.A., L.B., B.T.B., F.B., W.H.B., J.M.B., D.H.R.B., M.Boehnke, A.D.B., A.C., N.C., U.D., T.Esko, B.E., M.Frye, J.M.F., E.S.G., M.G., F.G., M.G-S., J.H., D.M.H., C.M.H., Author Manuscript I.J., L.A.J., R.S.K., M.Landén, M.Leboyer, C.M.L., Q.S.L., J.Lissowska, N.G.M., S.L.M., A.M.M., F.J.M., I.M., A.Metspalu, P.B.Mitchell, G.M., O.M., P.B.Mortensen, B.M-M., R.M.M., B.M.N., V.N., M.N., M.M.N., M.C.O’D., K.J.O., M.J.O., S.A.P., C.P., M.T.P., J.A.R-Q., M.Ribasés, M.Rietschel, G.A.R., M.Schalling, P.R.S., T.G.S., A.S., J.W.S., H.S., K.S., E.Stordal, G.T., A.E.V., E.V., J.B.V., T.W., J.I.N., A.D.F., H.J.E., S.C., R.A.O., L.J.S., O.A.A., J.K., P.S. Bioinformatics: E.A.S., G.B., A.J.F., J.R.I.C., H.A.G., P.A.H., S.E.B., D.W.C., V.E-P., C.G., M.L.H., S.H., R.Karlsson, M.Leber, C.Liu, F.Meng, L.M.O.L., A.P.S.O., C.S.R., P.R., P.D.S., M.Steffens, S.Szelinger, T.E.T., S.X., P.Zandi, eQTLGen Consortium, BIOS Consortium, T.Esko, E.S.G., Q.S.L., G.A.R., H.S. Clinical: A.McQuillin, M.M., E.A., N.A-R., A.A., N.B., M.Bauer, C.B.P., E.B., M.P.B., M.Budde, M.Burmeister, W.Byerley, M.C., P.C., W.C., D.Curtis, P.M.C., J.R.D., T.Elvsåshagen, L.Forty, C.F., K.G., J.Garnham, M.G.P., K.G-S., M.J.G., J.Grove, J.G-P., M.Hautzinger, U.H., M.Hipolito, A.J., J.L.K., S.K-S., M.K., R.Kupka, C.Lavebratt, J.Lawrence, W.B.L., S.L., D.J.M., P.B.Mahon, W.M., L.Martinsson, P.M., M.G.M., H.M., A.N.A., E.A.N., C.O’D., L.O., U.Ö., R.H.P., A.Perry, A.Pfennig, J.B.P., E.J.R., A.R., J.P.R., F.R., M.Rivera, W.A.S., C.S.W., E.Sigurdsson, C.S., O.B.S., J.L.S., A.T.S., D.S.C., J.S.S., F.S., J.S., R.C.T., H.V., T.W.W., A.H.Y., R.A., I.A., M.A., B.T.B., F.B., D.H.R.B., A.D.B., A.C., N.C., U.D., B.E., M.Frye, E.S.G., M.G., F.G., M.G-S., J.H., I.J., L.A.J., R.S.K., G.K., M.Landén, J.Lissowska, N.G.M., F.Mayoral, S.L.M., A.M.M., F.J.M., I.M., P.B.Mitchell, G.M., O.M., P.B.Mortensen, V.N., M.C.O’D., K.J.O., M.J.O., C.P., M.T.P., J.A.R-Q., M.Rietschel, T.G.S., A.S., J.W.S., E.Stordal, A.E.V., E.V., J.I.N., A.D.F. Genomic assays/data generation: G.B., A.J.F., E.A., D.A., M.B-H., C.B.P., J.B-G., T-K.C., D.W.C., C.Cruceanu, F.D., J.D-F., S.D., S.B.F., J.F., M.G.P., E.K.G., P.H., S.J., R.Kandaswamy, A.C.K., S.E.L., A.Maaser, J.D.M., L.Milani, G.W.M., D.W.M., T.W.M., E.R., C.S-M., T.S., C.S.H., S.Szelinger, J.T., S.H.W., P.Zhang, A.C., T.Esko, J.M.F., E.S.G., M.G., D.M.H., R.M.M., M.M.N., M.Ribasés, Author Manuscript G.A.R., G.T., S.C. Obtained funding for BD samples: G.B., H.A., N.A-R., J.D.B., W.Bunney, J.R.D., N.B.F., L.Frisén, M.J.G., S.J., J.A.K., C.Lavebratt, S.L., P.M., G.W.M., U.Ö., J.B.P., M.Rivera, A.F.S., C.S.W., S.J.W., T.W.W., A.H.Y., M.A., B.T.B., W.H.B., J.M.B., M.Boehnke, A.D.B., A.C., N.C., M.Frye, J.M.F., E.S.G., M.G., M.G-S., I.J., L.A.J., M.Landén, M.Leboyer, C.M.L., N.G.M., A.Metspalu, P.B.Mitchell, O.M., P.B.Mortensen, B.M.N., M.N., M.M.N., M.C.O’D., M.J.O., C.P., M.T.P., M.Rietschel, G.A.R., P.R.S., T.G.S., J.W.S., G.T., J.B.V., T.W., J.I.N., H.J.E., R.A.O., P.S. Statistical analysis: E.A.S., G.B., S.R., V.T., M.M., Y.W., J.R.I.C., H.A.G., C.A.d.L., S.Steinberg, J.M.W.P., M.T., E.M.B., T.H.P., P.A.H., A.L.R., L.A., N.A-R., T.D.A., V.A., S.A., J.A.B., R.B., S.E.B., J.B., F.C., K.C., A.W.C., D.Chen, C.Churchhouse, A.M.D., S.d.J., A.L.D., A.D., V.E-P., C.C.F., M.Flickinger, T.M.F., D.G., C.G., J.Goldstein, S.D.G., T.A.G., J.Grove, W.G., M.L.H., D.H., L.H., J.S.J., R.Karlsson, M.Leber, P.H.L., J.Z.L., S.M., S.E.M., D.W.M., N.M., H.N., C.M.N., L.M.O.L., A.P.S.O., S.M.P., C.S.R., P.R., D.M.R., N.J.S., O.B.S., T.E.T., W.W., W.X., P.Zandi, P.Zhang, S.Z., eQTLGen Consortium, BIOS Consortium, J.M.B., A.D.B., M.J.D., E.S.G., F.G., Q.S.L., B.M-M., D.P., H.S., P.F.S., N.R.W., P.S. Competing Interest: T.E. Thorgeirsson, S. Steinberg, H. Stefansson and K. Stefansson are employed by deCODE Genetics/Amgen. Multiple additional authors work for pharmaceutical or biotechnology companies in a manner directly analogous to academic co-authors and collaborators. A.H. Young has given paid lectures and is on advisory boards for the following companies with drugs used in affective and related disorders: Astra Zeneca, Eli Lilly, Janssen, Lundbeck, Sunovion, Servier, Livanova. A.H. Young is Lead Investigator for Embolden Study (Astra Zeneca), BCI Neuroplasticity study and Aripiprazole Mania Study, which are investigator-initiated studies Author Manuscript from Astra Zeneca, Eli Lilly, Lundbeck, and Wyeth. J. Nurnberger is an investigator for Janssen. P.F. Sullivan reports the following potentially competing financial interests: Lundbeck (advisory committee), Pfizer (Scientific Advisory Board member), and Roche (grant recipient, speaker reimbursement). G. Breen reports consultancy and speaker fees from Eli Lilly and Illumina and grant funding from Eli Lilly. O.A. Andreassen has received speaker fees from Lundbeck. J.Antoni Ramos-Quiroga was on the speakers’ bureau and/or acted as consultant for Eli-Lilly, Janssen-Cilag, Novartis, Shire, Lundbeck, Almirall, Braingaze, Sincrolab, and Rubió in the last 5 years. He also received travel awards (air tickets + hotel) for taking part in psychiatric meetings from Janssen-Cilag, Rubió, Shire, and Eli- Lilly. The Department of Psychiatry chaired by him received unrestricted educational and research support from the following companies in the last 5 years: Eli-Lilly, Lundbeck, Janssen- Cilag, Actelion, Shire, Ferrer, Oryzon, Roche, Psious, and Rubió. Dr. E. Vieta has received grants and served as consultant, advisor or CME speaker for the following entities: AB-Biotics, Abbott, Allergan, Angelini, AstraZeneca, Bristol-Myers Squibb, Dainippon Sumitomo Pharma, Farmindustria, Ferrer, Forest Research Institute, Gedeon Richter, Glaxo-Smith-Kline, Janssen, Lundbeck, Otsuka, Pfizer, Roche, SAGE, Sanofi-Aventis, Servier, Shire, Sunovion, Takeda, the Brain and Behaviour Foundation, the Catalan Government (AGAUR and PERIS), the Spanish Ministry of Science, Innovation, and Universities (AES and CIBERSAM), the Seventh European Framework Programme and Horizon 2020, and the Stanley Medical Research Institute. T. Elvsåshagen has received speaker fees from Lundbeck. All other authors declare no financial interests or potential conflicts of interest. Stahl et al. Page 2 Author Manuscript Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder A full list of authors and affiliations appears at the end of the article. Abstract Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P<1×10−4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p < 5×10−8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but Author Manuscript also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD. Bipolar disorder (BD) is a severe neuropsychiatric disorder characterized by recurrent episodes of mania and depression that affect thought, perception, emotion, and social behaviour. A lifetime prevalence of 1-2%, elevated morbidity and mortality, onset in young adulthood, and a frequently chronic course make BD a major public health problem and a Author Manuscript leading cause of the global burden of disease 1. Clinical, twin and molecular genetic data all strongly suggest that BD is a multifactorial disorder 2. Based on twin studies, the overall heritability of BD has been estimated to be more than 70% 3,4, suggesting a substantial involvement of genetic factors in the development of the disorder, although non-genetic factors also influence risk. BD can be divided into two main clinical subtypes 5,6: bipolar I disorder (BD1) and bipolar II disorder (BD2). In BD1, manic episodes typically alternate with depressive episodes during the course of illness. Diagnosis of BD2 is based on the lifetime occurrence of at least one depressive and one hypomanic (but no manic) episode. Although modern diagnostic systems retain the Kraepelinian dichotomy 7 between BD and schizophrenia (SCZ), the distinction between the two disorders is not always clear-cut, and patients who display Author Manuscript clinical features of both disorders may receive a diagnosis of schizoaffective disorder-bipolar type (SAB). Likewise, in genetic studies BD and SCZ are usually treated separately, although recent epidemiological and molecular genetic studies provide strong evidence for some overlap between the genetic contributions to their etiology 2,8. Recent genome-wide association studies (GWAS) in BD have identified a number of significant associations between disease status and common genetic variants 9–23. The first large collaborative BD GWAS by the multinational Psychiatric Genomics Consortium Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 3 (PGC) Bipolar Disorder Working Group comprised 7,481 BD patients and 9,250 controls and identified four genome-wide significant loci 9. Three subsequent meta-analyses that Author Manuscript included the PGC BD data 10,12,18 identified an additional 5 loci. Estimates of the proportion of variance in liability attributable to common variants genome- wide (SNP-heritability) indicate that ~30% of the heritability for BD is due to common genetic variants 8. To date, only a small fraction of this heritability is explained by associated loci, but results from other human complex traits suggest that many more will be identified by increasing the sample size of GWAS 24. Here, we report the second GWAS of the PGC Bipolar Disorder Working Group, comprising 20,352 cases and 31,358 controls of European descent in a single, systematic analysis, with follow up of top findings in an independent sample of 9,412 cases and 137,760 controls. Some of our findings reinforce specific hypotheses regarding BD neurobiology; however, the majority of the findings suggest new biological insights. Author Manuscript RESULTS GWAS of bipolar disorder (BD) We performed a GWAS meta-analysis of 32 cohorts from 14 countries in Europe, North America and Australia (Supplementary Table 1A), totaling 20,352 cases and 31,358 controls of European descent (effective sample size 46,582). This is a large GWAS of BD, a 2.7-fold increase in the number of cases compared to our previous GWAS 9, and includes 6,328 case and 7,963 control samples not previously reported. We imputed variant dosages using the 1,000 Genomes reference panel, retaining association results for 9,372,253 autosomal variants with imputation quality score INFO > 0.3 and minor allele frequency ≥ 1% in both cases and controls. We performed logistic regression of case status on imputed variant Author Manuscript dosage using genetic ancestry covariates. The resulting genomic inflation factor (λGC) was 1.23, 1.01 when scaled to 1,000 cases and 1,000 controls (λ1000) (Supplementary Figure 1). The LD Score regression intercept was 1.021 (se=0.010), and the attenuation ratio of 0.053 (se=0.027) was non-significant, indicating that the observed genomic inflation is indicative of polygenicity rather than stratification or cryptic population structure 25. The LD-score regression SNP-heritability estimates for BD were 0.17-0.23 on the liability scale assuming population prevalence of 0.5-2%. See Supplementary Table 1A, Online Methods and Supplementary Note for sample and method details. We find a marked increase in phenotypic variance explained by genome-wide polygenic risk scores (PRS) compared to previous publications (sample size weighted mean observed Nagelkerke’s R2 = 0.08 across datasets, liability scale R2=0.04, for p-threshold 0.01; Author Manuscript Supplementary Figure 2 and Supplementary Table 2). Among the different datasets, we observed no association between the PRS R2 and: (i) the gender distribution of the BD cases (p=0.51); (ii) the proportion of cases with psychosis (p=0.61); (iii) the proportion with a family history of BD (p=0.82); or (iv) the median age of onset for BD (p=0.64). In our primary genome-wide analysis, we identified 19 loci exceeding genome-wide significance (P< 5×10−8; Table 1). Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 4 Follow-up of suggestive loci in additional samples Author Manuscript We tested lead variants that were significant at P<1×10−4 in our discovery GWAS meta- analysis, a total of 794 autosomal and 28 X chromosome variants, for association in follow- up samples totaling 9,412 cases and 137,760 controls of European ancestry (effective sample size 23,005; Supplementary Note and Supplementary Table 1B). We first compared discovery and follow-up sample summary statistics using LD score regression, and estimated their genetic correlation to be 0.98 (se=0.07), consistent with homogeneous genetic effects between the two samples. Discovery and follow-up samples also show similar patterns of significant genetic correlations with a range of other human diseases and traits in the LD Hub database 26 (Supplementary Table 3; correlation of 0.93, p = 8.3×10−14, Supplementary Figure 3). Thirty autosomal loci achieved genome-wide significance (P< 5×10−8) in fixed-effect meta- Author Manuscript analysis of our GWAS and follow-up samples (Figure 1, Table 1A, Supplementary Data 1–3, Supplementary Table 4). In Supplementary Table 5, we present detailed descriptions of the associated loci and genes, with bioinformatic and literature evidence for their potential roles in BD. Of the 30 genome-wide significant loci from our combined analysis, 20 are novel BD risk loci. These include 19 loci that were significant only in the combined analysis, of which three were reported to have genome-wide significant SNPs in previous studies (ADCY2 18, POU3F2 18, ANK3 12,18), and 11 that were significant in our primary GWAS. We refer to loci by the gene name attributed in previous BD GWAS publications, or by the name of the closest gene for novel loci, without implication that the named gene is causal. Results for all variants tested in the follow-up study are presented in Supplementary Table 4. Of the 19 variants that were genome-wide significant in the discovery GWAS, 8 were not genome-wide significant in the combined analysis (Table 1B), and 11 were non-significant Author Manuscript in one-tailed association tests in the follow-up samples (p>0.05 in Table 1). Still, the follow- up results for these 19 variants are clearly non-null in aggregate: all 19 had consistent directions of effect between discovery GWAS and follow-up (9.5 expected by chance, binomial test p=4×10−6), and eight of the 19 had follow-up 1-tailed p<0.05 (1 expected by chance, sign test p=2×10−6). Using effect sizes corrected for winner’s curse 27,28 for each of the 19 variants that were genome-wide significant in the GWAS, we calculated power to achieve significant results (1-tailed p<0.05) in the follow-up samples or genome-wide significance in combined analysis (Supplementary Note, Supplementary Table 6, Supplementary Figure 4). We found that the number of variants significant in follow-up is close to expectation (8 observed with follow-up p<0.05, 8.26 expected, Poisson binomial p = 0.57), and that 11 variants achieving genome-wide significance in the combined analysis is also within the expected range (p = 0.29). As an alternative to winner’s curse correction, we Author Manuscript conducted a polygenic inference analysis using a mixture of Gaussian effect size distributions to model BD genetic architecture and estimate the variants’ true effect sizes 29 (Supplementary Note, Supplementary Figure 5). Under this model, we found that only two variants were nominally significantly weaker in follow-up than expected by chance (TRANK1 rs9834970 p = 0.012, and rs13821 p = 0.026; Supplementary Table 7), and none were Bonferroni significant (p>0.05/19=0.0026). Thus, the overall replication rate is within the expected range given the polygenic architecture of BD. Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 5 We next asked if the variants tested in the follow-up samples were, in aggregate, consistent Author Manuscript with the presence of additional sub genome-wide significant BD association signals. After excluding 47 variants that were genome-wide significant in our GWAS, our combined analysis or previous BD GWAS, 775 variants remained in our follow-up experiment. 551 variants had the same direction of effect in the discovery GWAS and follow-up (71%, compared to a null expectation of 50%, sign test p = 1.3×10−32), and 110 variants had the same direction of effect and were nominally significant (p<0.05) in the follow-up (14%, compared to an expected value of 5%, binomial test p = 2.1×10−22). This consistency between our GWAS and follow-up results suggests that many more true BD associations exist among these variants. To identify additional independent signals, we conducted conditional analyses across each of the 30 significant BD loci (Supplementary Table 8). We used the effective number of independent variants based on LD structure within loci 30 to calculate a multiple test- Author Manuscript corrected significance threshold (p=1.01×10−5, see Supplementary Note). Only one locus showed evidence for an independent association signal (rs114534140 in locus #8, FSTL5; pconditional = 2×10−6). At another locus (#30,STK4 on chr 20), we found two SNPs with genome-wide significance in low LD (R2 < 0.1); however, conditional analysis showed that their associations were not independent. Shared loci and genetic correlations with other traits We next examined the genetic relationships of BD to other psychiatric disorders and traits. Of the 30 genome-wide significant BD loci, 8 also harbor schizophrenia (SCZ) associations 31,32. Based on conditional analyses, the BD and SCZ associations appear to be independent at 3 of the 8 shared loci (NCAN, TRANK1 and chr7q22.3:105Mb) (Supplementary Table 9). No genome-wide significant BD locus overlapped with those identified for depression Author Manuscript (DEPR), including 44 risk loci identified in the most recent PGC GWAS of major depression 33, and those reported in a large study of depressive symptoms or subjective well-being 34. As previously reported 35, we found substantial and highly significant genetic correlations between BD and SCZ 31 (LD-score regression estimated genetic correlation rg = 0.70, se = 0.020) and between BD and DEPR 33 (rg = 0.35, se = 0.026). The BD and DEPR genetic correlation was similar to that observed for SCZ and DEPR (rg = 0.34, se = 0.025) (Supplementary Table 10A). We found significant genetic correlations between BD and other psychiatric-relevant traits (Supplementary Table 10B), including autism spectrum disorder 8 (rg = 0.18, P=2×10−4), anorexia nervosa 36 (rg = 0.23, P=9×10−8), and subjective well-being 34 (rg = −0.22, P=4×10−7). There was suggestive positive overlap with anxiety disorders (rg=0.21, P=0.04) Author Manuscript 37 and neuroticism (r =0.12, P=0.002) 38. Significant r s were seen with measures of g g education: college attendance 39 (rg = 0.21, P=1=x10−7) and education years 40 (rg=0.20, P=6×10−14), but not with childhood IQ 41 (rg=0.05, P=0.5) or intelligence 42 (rg=−0.05, P=0.08). Among a large number of variants in BD risk loci that were associated with additional traits in the GWAS catalog 43, we found a handful of loci with non-independent associations (in one overlapping locus with each of educational attainment, biliary atresia, bone mineral density, lipid-related biomarkers) (Supplementary Table 9). Biliary atresia and Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 6 lipid- related biomarkers, however, did not show significant genetic correlation with BD Author Manuscript (Supplementary Table 10B). BD subtypes We performed a secondary GWAS focusing on three clinically recognized subtypes of bipolar disorder: BD1 (n=14,879 cases), BD2 (n=3,421 cases), and SAB (n=977 cases) (Supplementary Note, Supplementary Tables 1A & 11, Supplementary Figure 6). We observed variants in 14 loci with genome-wide significance for BD1, 10 of which were in genome-wide significant loci in the combined BD GWAS analysis. Not surprisingly given the sample overlap, 3 of the 4 remaining loci genome-wide significant for BD1 have P < 10−6 in either our discovery GWAS or combined analysis. The remaining locus (MAD1L1, chr7:1.9Mb, discovery GWAS p = 2.4×10−6) was recently published in two BD GWAS that included Asian samples 44,45. We did not observe genome-wide significant results for the Author Manuscript smaller BD2 and SAB analyses. BD1, BD2 and SAB all have significant common variant heritabilities (BD1 h2snp = 0.25, se = 0.014, p = 3.2×10−77; BD2 h2snp = 0.11, se = 0.028, p = 5.8×10−5; SAB h2snp = 0.25, se = 0.10, p = 0.0071). Genetic correlations among BD subtypes show that these represent closely related, yet partially distinct, phenotypes (Supplementary Table 12). We conducted polygenic risk score (PRS) analyses to explore the relationship between genetic risk of SCZ and DEPR, and BD subtypes and psychosis (Figure 2, Supplementary Table 13). PRS calculated from SCZ 31 were significantly higher in BD1 cases than in BD2 cases (p=5.6×10−17, P threshold = 0.1) and in cases with psychosis compared to those without psychosis (p=2.12×10−6, P threshold =0.1). Conversely, PRS calculated from DEPR 33 were significantly higher in BD2 cases than in BD1 cases (P=8.5×10−10, P threshold = 0.01), independent of psychosis. Genetic correlations from LD-score regression support Author Manuscript these results; genetic correlations were greater for SCZ with BD1 (rg = 0.71, se = 0.025) than with BD2 (rg = 0.51, se = 0.072), and were greater for DEPR with BD2 (rg = 0.69, se = 0.093) than with BD1 (rg = 0.30, se = 0.028) (Supplementary Table 12). Systems biology and in silico functional analyses We tested for functional genomic enrichment in our BD GWAS using partitioned LD-score regression and a range of functional annotations across tissues 46 (Supplementary Note, Supplementary Table 14). SNP-based BD heritability was most enriched in open chromatin annotations in the central nervous system (proportion SNPs = 0.14, proportion h2snp = 0.60, enrichment =3.8, p = 3 × 10−14). We also used DEPICT 47 to test for expression of BD- associated genes across tissues, and found significant enrichment of central nervous system (p < 1.4×10−3, FDR < 0.01) and neurosecretory system (p = 2.0×10−6, FDR < 0.01) genes Author Manuscript (Supplementary Table 15). To prioritize genes that may play a functional role in BD, we integrated BD GWAS association statistics with eQTL (SNP-gene expression association) and mQTL (SNP-DNA methylation association) data using summary Mendelian randomization (SMR) 48,49,50 (Supplementary Table 16; Supplementary Note). SMR identified 21 genes using eQTL data that were significant after multiple testing correction, without evidence of heterogeneity Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 7 between GWAS and eQTL association signals. Association with GNL3 was observed in Author Manuscript both brain and blood, highlighting the utility of using blood eQTL data as proxy for brain eQTLs 50. Methylation profiles at 6 CpGs in brain and 10 CpGs in blood were associated with BD, four of which overlapped between brain and blood mQTL: MUSTN1, GLT8D1, HAPLN4 and FADS2. Finally, we used MAGMA 51 to conduct a gene-wise BD GWAS and to test for enrichment of pathways curated from multiple sources (see Supplementary Note). We note that significance levels were assigned to genes by physical proximity of SNPs, and do not imply that significant genes are causal for BD. Genic association results included 154 Bonferroni significant genes (MAGMA pJOINT < 2.8×10−6), including 82 genes in 20 genome-wide significant loci, and 73 genes in 27 additional loci that did not reach genome-wide significance (Supplementary Table 17). Nine related pathways were significantly enriched for genes with BD associations (p < 7.0×10−5, FDR < 0.05), including abnormal motor Author Manuscript coordination/balance pathways (from mice), regulation of insulin secretion, and endocannabinoid signaling (Supplementary Table 18, Supplementary Figure 7). DISCUSSION We carried out a large bipolar disorder (BD) GWAS and identified 30 genome-wide significant loci, including 20 that were novel. Previous BD GWAS have reported a total of 20 loci significantly associated with BD9–23 ; twelve of these previously reported loci were not genome-wide significant in our GWAS meta analysis, but all had PGWAS ≤ 1.3×10−5 (Supplementary Table 4C). Our recent GWAS of BD and SCZ 52, which included our discovery GWAS data jointly analyzed with published SCZ data 31 (without overlapping control subjects), highlighted similarities and differences in BD and SCZ in terms of known Author Manuscript associated SNPs and PRS-subphenotype associations; here we maximized power to identify BD associations. Phenotypic variance explained by polygenic risk scores (PRS) based on our BD GWAS data is ~8% (observed scale; 4% on the liability scale 53), an increase from 2.8% (1.2% on the liability scale) in our previous study 9. The results of our BD subtype PRS analyses support the nosological distinction between BD1 and BD2, but also highlight the importance of psychosis beyond DSM subtypes, corroborating and expanding evidence from previous clinical 54 and genetic studies 52,55,56. The DEPR vs. BD PRS analyses provide further support for the distinction between BD1 and BD2, independent of the presence of psychosis. Of the 19 loci identified in our discovery GWAS, only 11 were genome-wide significant in meta-analysis of our GWAS and follow-up samples. These results are not unexpected given small effect sizes, the winner’s curse 28,57 (Supplementary Note and Supplementary Figure Author Manuscript 4); SNPs can teeter-totter around the genome-wide significance threshold even as sample sizes increase. Genetic heterogeneity observed among BD GWAS cohorts8 could also contribute to inconsistent replication results; we observed variable polygenic effects between BD subtypes (Figure 2, Supplementary Table 13) as well as between cohorts in our study (Supplementary Figure 2, Supplementary Table 4) which used a diversity of criteria to define cases (Supplementary Note). Remarkably, the strongest association signal from the discovery GWAS, at the TRANK1 locus (rs9834970; pcombined = 5.7E-12, OR = 0.93), Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 8 exhibited significant heterogeneity among discovery GWAS cohorts (Cochran’s Q p = 1.9×10−4), and did not replicate in the follow-up sample (1-tailed pfollowup = 0.3) Author Manuscript (Supplementary Data 2 & 3). This locus has been significant in recent 11,12,17,18 but not earlier BD GWAS 9,13,20. Thus, complex genetic architecture as well as phenotypic heterogeneity may contribute to the inconsistency of genome-wide significant findings within and across BD GWAS studies. The observed heterogeneity is a major challenge for GWAS of psychiatric disorders and calls for careful and systematic clinical assessment of cases and controls in parallel with continued efforts to collect larger sample sizes. Of the 30 BD associated loci, 8 also harbor associations 31,32,58 with schizophrenia (SCZ); however, conditional analyses suggest that the BD and SCZ associations at 3 of the 8 shared loci (in the NCAN, TRANK1 and chr7q22.3:105 Mb loci) may be independent (Supplementary Table 9). Differential BD and SCZ associations may represent opportunities to understand the genetic distinctions between these closely related and sometimes clinically Author Manuscript difficult to distinguish disorders. We did not find BD loci that overlap with those associated with major depression33. The confirmed association within loci containing CACNA1C and other voltage-gated calcium channel genes supports the rekindled interest in calcium channel antagonists as potential treatments for BD, with similar examination ongoing for other genes implicated in SCZ GWAS 59. Other genes within novel BD-associated loci include those coding for other ion channels and transporters (SCN2A, SLC4A1), neurotransmitter receptors (GRIN2A) and synaptic components (RIMS1, ANK3). Further study will confirm whether or not these are the causal genes in the loci. These processes are important in neuronal hyperexcitability60, an excess of which has been reported in iPSC derived neurons from BD patients, and which has been shown to be affected by the classic mood stabilizing drug lithium 61. In addition, Author Manuscript SMR eQTL and mQTL analyses implicate GLT8D1, which is involved in proliferation and differentiation of neural stem cells 62. Pathway analyses reveal new genetic evidence for insulin secretion and endocannabinoid signaling in BD. There is evidence of insulin action in the brain 63 and in BD 64. The endocannabinoid system has possible roles in schizophrenia 65,66 and depression 67. Top genes appearing in these pathways include calcium and potassium channel subunit, MAP kinase and GABA-A receptor subunit genes (Supplementary Table 18). We observe significant positive genetic correlations with educational attainment, but not with either adult or childhood IQ, suggesting that the role of BD genetics in educational attainment may be independent of general intelligence. This result is inconsistent with suggestions from epidemiological studies 68, but in agreement with a recent clinical study 69. Author Manuscript In summary, findings from the genome-wide analysis of BD reveal an extensive polygenic genetic architecture of the disease, implicate brain calcium channels and neurotransmitter function in BD etiology, and confirm that BD is part of a spectrum of highly correlated psychiatric and mood disorders. Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 9 Accession codes: Author Manuscript Accessioned data were part of the following datasets analyzed in this study. Fat2: phs000167.v1.p1 or PGC bundle phs001254.v1.p1 (MGS nonGAIN controls). Gain: dbGAP phs000017.v3.p1 (GAIN Bip cases/controls), phs000021.v3.p2 (GAIN scz controls); also part of the PGC dbGAP bundle phs001254.v1.p1. Jjst: phs000092.v1.p1 or PGC bundle phs001254.v1.p1 (SAGE controls). St2c, NIMH RGR Bipolar Study 19 (STEP-BD), dbGAP phs000294.v1.p1 (MIGEN controls). Mich: NIMH RGR Bipolar Study 2 (Pritzker). Wtcc: EGAD00000000002. Data Availability. The PGC’s policy is to make genome-wide summary results public. Summary statistics for our meta-analysis are available through the PGC (https://www.med.unc.edu/pgc/results-and- downloads). Data are accessible with collaborative analysis proposals through the Bipolar Author Manuscript Disorder working group of the PGC (https://med.unc.edu/pgc). ONLINE METHODS GWAS and follow-up cohorts. Our discovery GWAS sample was comprised of 32 cohorts from 14 countries in Europe, North America and Australia (Supplementary Table 1A), totaling 20,352 cases and 31,358 controls of European descent. A selected set of variants (see below) were tested in 7 follow- up cohorts of European descent (Supplementary Table 1B), totalling 9,025 cases and 142,824 controls (Neff = 23,991). The Supplementary Note summarizes the source and inclusion/exclusion criteria for cases and controls for each cohort. All cohorts in the initial PGC BD paper were included 9. Cases were required to meet international consensus criteria Author Manuscript (DSM-IV or ICD-10) for a lifetime diagnosis of BD established using structured diagnostic instruments from assessments by trained interviewers, clinician-administered checklists, or medical record review. In most cohorts, controls were screened for the absence of lifetime psychiatric disorders and randomly selected from the population. GWAS cohort analysis We tested 20 principal components for association with BD using logistic regression; seven were significantly associated with phenotype and used in GWAS association analysis (PCs 1-6, 19). In each cohort, we performed logistic regression association tests for BD with imputed marker dosages including 7 principal components to control for population stratification. For all GWAS cohorts, X-chromosome association analyses were conducted separately by sex, and then meta-analyzed across sexes. We also conducted BD1, BD2, and Author Manuscript SAB GWAS, retaining only cohorts with at least 30 subtype cases and filtering SNPs for MAF > 0.02. Results were combined across cohorts using an inverse variance-weighted fixed effects meta-analysis 70. We used Plink ‘clumping’ 71,72 to identify an LD-pruned set of discovery GWAS meta-analysis BD-associated variants (P < 0.0001, and distance >500 kb or LD r2 < 0.1, n variants =822) for analysis in the follow-up cohorts. Conditional analyses were conducted within each GWAS cohort and meta-analyzed as above. Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 10 Follow-up cohort analysis. Author Manuscript In each follow-up cohort we performed BD association analysis of the 822 selected GWAS variants (when available) including genetic ancestry covariates, following QC and analysis methods of the individual study contributors. We performed inverse variance-weighted fixed-effects meta-analyses of the association results from the follow-up cohorts, and of the discovery GWAS and follow-up analyses. Polygenic risk score (PRS) analyses. We tested PRS for our primary GWAS on each GWAS cohort as a target set, using a GWAS where the target cohort was left out of the meta-analysis (Supplementary Table 2). To test genetic overlaps with other psychiatric diseases, we calculated PRS for DEPR and SCZ in our GWAS cohort BD cases 73. In pairwise case subtype or psychosis analyses (Figure 2, Supplementary Table 13), we regressed outcome on the PRS adjusting for ancestry principal Author Manuscript components and a cohort indicator using logistic regression, and visualized covariate- adjusted PRS in BD1 and BD2 subtypes (Figure 2). Outcome sample sizes were BD1 n=8,044, BD2 n=3,365, SAB n=977; BD1 cases with and without psychosis n= 2175 and 798 respectively, BD2 cases with and without psychosis n= 146 and 660. Linkage disequilibrium (LD) score regression. LD score regression 25,26 was used to conduct SNP-heritability analyses from GWAS summary statistics. LD score regression bivariate genetic correlations attributable to genome-wide common variants were estimated between the full BD GWAS, BD subtype GWASs, and other traits and disorders in LD-Hub 26. We also used LD score regression to partition heritability by genomic features 46. Author Manuscript Relation of BD GWA findings to tissue and cellular gene expression. We used partitioned LD score 47,74 and DEPICT 47 regression to evaluate which somatic tissues and brain tissues were enriched in the BD GWAS. We used summary-data-based Mendelian randomization (SMR) 48,50 to identify SNPs with strong evidence of causality of brain or blood gene expression or methylation in BD risk (Supplementary Table 16), with a test for heterogeneity to exclude regions with LD between distinct causal SNPs (pHET < 0.01). Gene-wise and pathway analysis. Guided by rigorous method comparisons conducted by PGC members 51,75, p-values quantifying the degree of association of genes and gene sets with BD were generated using MAGMA (v1.06) 51. We used ENSEMBL gene coordinates for 18,172 genes giving a Author Manuscript Bonferroni corrected P-value threshold of 2.8×10−6. Joint multi-SNP LD-adjusted gene- level p-values were calculated using SNPs 35 kb upstream to 10 kb downstream, adjusting for LD using 1,000 Genomes Project (Phase 3 v5a, MAF ≥ 0.01, European-ancestry subjects) 76. Gene sets were compiled from multiple sources. Competitive gene set tests were conducted correcting for gene size, variant density, and LD within and between genes. The pathway map (Supplementary Figure 5) was constructed using the kernel generative topographic mapping algorithm (k-GTM) as described by 77. Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 11 Genome build. Author Manuscript All genomic coordinates are given in NCBI Build 37/UCSC hg19. Supplementary Material Refer to Web version on PubMed Central for supplementary material. Authors Eli A Stahl1,2,3,†,&, Gerome Breen4,5,†, Andreas J Forstner6,7,8,9,10,†, Andrew McQuillin11,†, Stephan Ripke12,13,14,†, Vassily Trubetskoy13, Manuel Mattheisen15,16,17,18,19, Yunpeng Wang20,21, Jonathan R I Coleman4,5, Héléna A Gaspar4,5, Christiaan A de Leeuw22, Stacy Steinberg23, Jennifer M Whitehead Pavlides24, Maciej Trzaskowski25, Enda M Byrne25, Tune H Pers3,26, Peter A Author Manuscript Holmans27, Alexander L Richards27, Liam Abbott12, Esben Agerbo19,28,29, Huda Akil30, Diego Albani31, Ney Alliey-Rodriguez32, Thomas D Als15,16,19, Adebayo Anjorin33, Verneri Antilla14, Swapnil Awasthi13, Judith A Badner34, Marie Bækvad- Hansen19,35, Jack D Barchas36, Nicholas Bass11, Michael Bauer37, Richard Belliveau12, Sarah E Bergen38, Carsten Bøcker Pedersen19,28,29, Erlend Bøen39, Marco P. Boks40, James Boocock41, Monika Budde42, William Bunney43, Margit Burmeister44, Jonas Bybjerg-Grauholm19,35, William Byerley45, Miquel Casas46,47,48,49, Felecia Cerrato12, Pablo Cervantes50, Kimberly Chambert12, Alexander W Charney2, Danfeng Chen12, Claire Churchhouse12,14, Toni-Kim Clarke51, William Coryell52, David W Craig53, Cristiana Cruceanu50,54, David Curtis55,56, Piotr M Czerski57, Anders M Dale58,59,60,61, Simone de Jong4,5, Franziska Degenhardt8, Jurgen Del-Favero62, J Raymond DePaulo63, Srdjan Author Manuscript Djurovic64,65, Amanda L Dobbyn1,2, Ashley Dumont12, Torbjørn Elvsåshagen66,67, Valentina Escott-Price27, Chun Chieh Fan61, Sascha B Fischer6,10, Matthew Flickinger68, Tatiana M Foroud69, Liz Forty27, Josef Frank70, Christine Fraser27, Nelson B Freimer71, Louise Frisén72,73,74, Katrin Gade42,75, Diane Gage12, Julie Garnham76, Claudia Giambartolomei77, Marianne Giørtz Pedersen19,28,29, Jaqueline Goldstein12, Scott D Gordon78, Katherine Gordon-Smith79, Elaine K Green80, Melissa J Green81,82, Tiffany A Greenwood60, Jakob Grove15,16,19,83, Weihua Guan84, José Guzman-Parra85, Marian L Hamshere27, Martin Hautzinger86, Urs Heilbronner42, Stefan Herms6,8,10, Maria Hipolito87, Per Hoffmann6,8,10, Dominic Holland58,88, Laura Huckins1,2, Stéphane Jamain89,90, Jessica S Johnson1,2, Anders Juréus38, Radhika Kandaswamy4, Robert Karlsson38, James L Kennedy91,92,93,94, Sarah Kittel-Schneider95, James A Knowles96,97, Author Manuscript Manolis Kogevinas98, Anna C Koller8, Ralph Kupka99,100,101, Catharina Lavebratt72, Jacob Lawrence102, William B Lawson87, Markus Leber103, Phil H Lee12,14,104, Shawn E Levy105, Jun Z Li106, Chunyu Liu107, Susanne Lucae108, Anna Maaser8, Donald J MacIntyre109,110, Pamela B Mahon63,111, Wolfgang Maier112, Lina Martinsson73, Steve McCarroll12,113, Peter McGuffin4, Melvin G McInnis114, James D McKay115, Helena Medeiros97, Sarah E Medland78, Fan Meng30,114, Lili Milani116, Grant W Montgomery25, Derek W Morris117,118, Thomas W Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 12 Mühleisen6,119, Niamh Mullins4, Hoang Nguyen1,2, Caroline M Nievergelt60,120, Annelie Nordin Adolfsson121, Evaristus A Nwulia87, Claire ODonovan76, Loes M Author Manuscript Olde Loohuis71, Anil P S Ori71, Lilijana Oruc122, Urban Ösby123, Roy H Perlis124,125, Amy Perry79, Andrea Pfennig37, James B Potash63, Shaun M Purcell2,111, Eline J Regeer126, Andreas Reif95, Céline S Reinbold6,10, John P Rice127, Fabio Rivas85, Margarita Rivera4,128, Panos Roussos1,2,129, Douglas M Ruderfer130, Euijung Ryu131, Cristina Sánchez-Mora46,47,49, Alan F Schatzberg132, William A Scheftner133, Nicholas J Schork134, Cynthia Shannon Weickert81,82, Tatyana Shehktman60, Paul D Shilling60, Engilbert Sigurdsson135, Claire Slaney76, Olav B Smeland136,137, Janet L Sobell138, Christine Søholm Hansen19,35, Anne T Spijker139, David St Clair140, Michael Steffens141, John S Strauss93,142, Fabian Streit70, Jana Strohmaier70, Szabolcs Szelinger143, Robert C Thompson114, Thorgeir E Thorgeirsson23, Jens Treutlein70, Helmut Vedder144, Weiqing Wang1,2, Stanley J Watson114, Thomas W Weickert81,82, Stephanie H Witt70, Simon Xi145, Author Manuscript Wei Xu146,147, Allan H Young148, Peter Zandi149, Peng Zhang150, Sebastian Zöllner114, eQTLGen Consortium151, BIOS Consortium151, Rolf Adolfsson121, Ingrid Agartz17,39,152, Martin Alda76,153, Lena Backlund73, Bernhard T Baune154,155, Frank Bellivier156,157,158,159, Wade H Berrettini160, Joanna M Biernacka131, Douglas H R Blackwood51, Michael Boehnke68, Anders D Børglum15,16,19, Aiden Corvin118, Nicholas Craddock27, Mark J Daly12,14, Udo Dannlowski155, Tõnu Esko3,113,116,161, Bruno Etain156,158,159,162, Mark Frye163, Janice M Fullerton82,164, Elliot S Gershon32,165, Michael Gill118, Fernando Goes63, Maria Grigoroiu-Serbanescu166, Joanna Hauser57, David M Hougaard19,35, Christina M Hultman38, Ian Jones27, Lisa A Jones79, René S Kahn2,40, George Kirov27, Mikael Landén38,167, Marion Leboyer90,156,168, Cathryn M Lewis4,5,169, Qingqin S Li170, Jolanta Lissowska171, Nicholas G Martin78,172, Fermin Mayoral85, Author Manuscript Susan L McElroy173, Andrew M McIntosh51,174, Francis J McMahon175, Ingrid Melle176,177, Andres Metspalu116,178, Philip B Mitchell81, Gunnar Morken179,180, Ole Mors19,181, Preben Bo Mortensen15,19,28,29, Bertram Müller-Myhsok54,182,183, Richard M Myers105, Benjamin M Neale3,12,14, Vishwajit Nimgaonkar184, Merete Nordentoft19,185, Markus M Nöthen8, Michael C ODonovan27, Ketil J Oedegaard186,187, Michael J Owen27, Sara A Paciga188, Carlos Pato97,189, Michele T Pato97, Danielle Posthuma22,190, Josep Antoni Ramos-Quiroga46,47,48,49, Marta Ribasés46,47,49, Marcella Rietschel70, Guy A Rouleau191,192, Martin Schalling72, Peter R Schofield82,164, Thomas G Schulze42,63,70,75,175, Alessandro Serretti193, Jordan W Smoller12,194,195, Hreinn Stefansson23, Kari Stefansson23,196, Eystein Stordal197,198, Patrick F Sullivan38,199,200, Gustavo Turecki201, Arne E Vaaler202, Eduard Vieta203, John B Vincent142, Thomas Werge19,204,205, John I Author Manuscript Nurnberger206, Naomi R Wray24,25, Arianna Di Florio27,200, Howard J Edenberg207, Sven Cichon6,8,10,119, Roel A Ophoff40,41,71, Laura J Scott68, Ole A Andreassen136,137, John Kelsoe60,*,&, Pamela Sklar1,2,*, Bipolar Disorder Working Group of the Psychiatric Genomics Consortium151 Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 13 Affiliations Author Manuscript 1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, US 2Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, US 3Medical and Population Genetics, Broad Institute, Cambridge, MA, US 4MRC Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, GB 5NIHR BRC for Mental Health, King’s College London, London, GB 6Department of Biomedicine, University of Basel, Basel, CH Author Manuscript 7Department of Psychiatry (UPK), University of Basel, Basel, CH 8Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, DE 9Centre for Human Genetics, University of Marburg, Marburg, DE 10Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, CH 11Division of Psychiatry, University College London, London, GB 12Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, US 13Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, DE Author Manuscript 14Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, US 15iSEQ, Center for Integrative Sequencing, Aarhus University, Aarhus, DK 16Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, DK 17Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, SE 18Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, Würzburg, DE 19iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, DK Author Manuscript 20Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Copenhagen, DK 21Institute of Clinical Medicine, University of Oslo, Oslo, NO 22Departmentof Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, NL 23deCODE Genetics / Amgen, Reykjavik, IS Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 14 24Queensland Brain Institute, The University of Queensland, Brisbane, QLD, AU Author Manuscript 25Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, AU 26Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA, US 27Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, GB 28National Centre for Register-Based Research, Aarhus University, Aarhus, DK 29Centre for Integrated Register-based Research, Aarhus University, Aarhus, DK 30Molecular & Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, Author Manuscript MI, US 31NEUROSCIENCE, IRCCS - Istituto Di Ricerche Farmacologiche Mario Negri, Milano, IT 32Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, US 33Psychiatry, Berkshire Healthcare NHS Foundation Trust, Bracknell, GB 34Psychiatry, Rush University Medical Center, Chicago, IL, US 35Centerfor Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, DK Author Manuscript 36Department of Psychiatry, Weill Cornell Medical College, New York, NY, US 37Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, DE 38Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE 39Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, NO 40Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, NL 41Human Genetics, University of California Los Angeles, Los Angeles, CA, US 42Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, DE Author Manuscript 43Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, US 44Molecular& Behavioral Neuroscience Institute and Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, US 45Psychiatry, University of California San Francisco, San Francisco, CA, US Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 15 46Instituto de Salud Carlos III, Biomedical Network Research Centre on Mental Author Manuscript Health (CIBERSAM), Madrid, ES 47Department of Psychiatry, Hospital Universitari Vall d´Hebron, Barcelona, ES 48Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, ES 49Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addictions, Vall d´Hebron Research Institut (VHIR), Universitat Autònoma de Barcelona, Barcelona, ES 50Department of Psychiatry, Mood Disorders Program, McGill University Health Center, Montreal, QC, CA 51Division of Psychiatry, University of Edinburgh, Edinburgh, GB Author Manuscript 52University of Iowa Hospitals and Clinics, Iowa City, IA, US 53Translational Genomics, USC, Phoenix, AZ, US 54Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, DE 55Centre for Psychiatry, Queen Mary University of London, London, GB 56UCL Genetics Institute, University College London, London, GB 57Department of Psychiatry, Laboratory of Psychiatric Genetics, Poznan University of Medical Sciences, Poznan, PL 58Department of Neurosciences, University of California San Diego, La Jolla, CA, Author Manuscript US 59Department of Radiology, University of California San Diego, La Jolla, CA, US 60Department of Psychiatry, University of California San Diego, La Jolla, CA, US 61Department of Cognitive Science, University of California San Diego, La Jolla, CA, US 62Applied Molecular Genomics Unit, VIB Department of Molecular Genetics, University of Antwerp, Antwerp, Belgium 63Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, US 64Department of Medical Genetics, Oslo University Hospital Ullevål, Oslo, NO Author Manuscript 65NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, NO 66Department of Neurology, Oslo University Hospital, Oslo, NO 67NORMENT, KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo, NO Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 16 68Centerfor Statistical Genetics and Department of Biostatistics, University of Author Manuscript Michigan, Ann Arbor, MI, US 69Department of Medical & Molecular Genetics, Indiana University, Indianapolis, IN, US 70Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, DE 71Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, US 72Department of Molecular Medicine and Surgery, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, SE 73Department of Clinical Neuroscience, Karolinska Institutet and Center for Author Manuscript Molecular Medicine, Karolinska University Hospital, Stockholm, SE 74Child and Adolescent Psychiatry Research Center, Stockholm, SE 75Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, DE 76Department of Psychiatry, Dalhousie University, Halifax, NS, CA 77Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA, US 78Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU 79Department of Psychological Medicine, University of Worcester, Worcester, GB Author Manuscript 80Schoolof Biomedical Sciences, Plymouth University Peninsula Schools of Medicine and Dentistry, University of Plymouth, Plymouth, GB 81School of Psychiatry, University of New South Wales, Sydney, NSW, AU 82Neuroscience Research Australia, Sydney, NSW, AU 83Bioinformatics Research Centre, Aarhus University, Aarhus, DK 84Biostatistics, University of Minnesota System, Minneapolis, MN, US 85Mental Health Department, University Regional Hospital, Biomedicine Institute (IBIMA), Málaga, ES 86Department of Psychology, Eberhard Karls Universität Tübingen, Tubingen, DE Author Manuscript 87Department of Psychiatry and Behavioral Sciences, Howard University Hospital, Washington, DC, US 88Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, US 89Psychiatrie Translationnelle, Inserm U955, Créteil, FR 90Faculté de Médecine, Université Paris Est, Créteil, FR Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 17 91Campbell Family Mental Health Research Institute, Centre for Addiction and Author Manuscript Mental Health, Toronto, ON, CA 92Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, ON, CA 93Department of Psychiatry, University of Toronto, Toronto, ON, CA 94Institute of Medical Sciences, University of Toronto, Toronto, ON, CA 95Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, DE 96Cell Biology, SUNY Downstate Medical Center College of Medicine, Brooklyn, NY, US 97Institute for Genomic Health, SUNY Downstate Medical Center College of Medicine, Brooklyn, NY, US Author Manuscript 98ISGlobal, Barcelona, ES 99Psychiatry, Altrecht, Utrecht, NL 100Psychiatry, GGZ inGeest, Amsterdam, NL 101Psychiatry, VU medisch centrum, Amsterdam, NL 102Psychiatry, North East London NHS Foundation Trust, Ilford, GB 103Department of Neurodegenerative Diseases and Geropsychiatry, University Hospital Bonn, Bonn, DE 104Psychiatricand Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, US Author Manuscript 105HudsonAlpha Institute for Biotechnology, Huntsville, AL, US 106Department of Human Genetics, University of Michigan, Ann Arbor, MI, US 107Psychiatry, University of Illinois at Chicago College of Medicine, Chicago, IL, US 108Max Planck Institute of Psychiatry, Munich, DE 109Mental Health, NHS 24, Glasgow, GB 110Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, GB 111Psychiatry, Brigham and Women’s Hospital, Boston, MA, US 112Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, DE Author Manuscript 113Department of Genetics, Harvard Medical School, Boston, MA, US 114Department of Psychiatry, University of Michigan, Ann Arbor, MI, US 115GeneticCancer Susceptibility Group, International Agency for Research on Cancer, Lyon, FR 116Estonian Genome Center, University of Tartu, Tartu, EE Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 18 117Disciplineof Biochemistry, Neuroimaging and Cognitive Genomics (NICOG) Author Manuscript Centre, National University of Ireland, Galway, Galway, IE 118Neuropsychiatric Genetics Research Group, Dept of Psychiatry and Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, IE 119Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, DE 120Research/Psychiatry, Veterans Affairs San Diego Healthcare System, San Diego, CA, US 121Department of Clinical Sciences, Psychiatry, Umeå University Medical Faculty, Umeå, SE 122Department of Clinical Psychiatry, Psychiatry Clinic, Clinical Center University of Author Manuscript Sarajevo, Sarajevo, BA 123Department of Neurobiology, Care sciences, and Society, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, SE 124Psychiatry, Harvard Medical School, Boston, MA, US 125Division of Clinical Research, Massachusetts General Hospital, Boston, MA, US 126Outpatient Clinic for Bipolar Disorder, Altrecht, Utrecht, NL 127Department of Psychiatry, Washington University in Saint Louis, Saint Louis, MO, US 128Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, ES Author Manuscript 129Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, US 130Medicine,Psychiatry, Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, US 131Department of Health Sciences Research, Mayo Clinic, Rochester, MN, US 132Psychiatryand Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, US 133Rush University Medical Center, Chicago, IL, US 134Scripps Translational Science Institute, La Jolla, CA, US Author Manuscript 135Faculty of Medicine, Department of Psychiatry, School of Health Sciences, University of Iceland, Reykjavik, IS 136Div Mental Health and Addiction, Oslo University Hospital, Oslo, NO 137NORMENT, University of Oslo, Oslo, NO 138Psychiatryand the Behavioral Sciences, University of Southern California, Los Angeles, CA, US Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 19 139Mood Disorders, PsyQ, Rotterdam, NL Author Manuscript 140Institute for Medical Sciences, University of Aberdeen, Aberdeen, UK 141Research Division, Federal Institute for Drugs and Medical Devices (BfArM), Bonn, DE 142Centre for Addiction and Mental Health, Toronto, ON, CA 143Neurogenomics, TGen, Los Angeles, AZ, US 144Psychiatry, Psychiatrisches Zentrum Nordbaden, Wiesloch, DE 145Computational Sciences Center of Emphasis, Pfizer Global Research and Development, Cambridge, MA, US 146Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, CA Author Manuscript 147Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CA 148Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, GB 149Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, US 150Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US 151A list of members appears in the Supplementary Note 152NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine and Diakonhjemmet Hospital, Author Manuscript University of Oslo, Oslo, NO 153National Institute of Mental Health, Klecany, CZ 154Department of Psychiatry, University of Melbourne, Melbourne, Vic, AU 155Department of Psychiatry, University of Münster, Münster, DE 156Department of Psychiatry and Addiction Medicine, Assistance Publique - Hôpitaux de Paris, Paris, FR 157Paris Bipolar and TRD Expert Centres, FondaMental Foundation, Paris, FR 158UMR-S1144 Team 1: Biomarkers of relapse and therapeutic response in addiction and mood disorders, INSERM, Paris, FR Author Manuscript 159Psychiatry, Université Paris Diderot, Paris, FR 160Psychiatry, University of Pennsylvania, Philadelphia, PA, US 161Division of Endocrinology, Children’s Hospital Boston, Boston, MA, US 162Centrefor Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, London, GB 163Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, US Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 20 164School of Medical Sciences, University of New South Wales, Sydney, NSW, AU Author Manuscript 165Department of Human Genetics, University of Chicago, Chicago, IL, US 166Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, RO 167Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, SE 168INSERM, Paris, FR 169Department of Medical & Molecular Genetics, King’s College London, London, GB 170Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ, US Author Manuscript 171Cancer Epidemiology and Prevention, M. Sklodowska-Curie Cancer Center and Institute of Oncology, Warsaw, PL 172School of Psychology, The University of Queensland, Brisbane, QLD, AU 173Research Institute, Lindner Center of HOPE, Mason, OH, US 174Centrefor Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, GB 175Human Genetics Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, MD, US 176Division of Mental Health and Addiction, Oslo University Hospital, Oslo, NO Author Manuscript 177Divisionof Mental Health and Addiction, University of Oslo, Institute of Clinical Medicine, Oslo, NO 178Institute of Molecular and Cell Biology, University of Tartu, Tartu, EE 179MentalHealth, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology - NTNU, Trondheim, NO 180Psychiatry, St Olavs University Hospital, Trondheim, NO 181Psychosis Research Unit, Aarhus University Hospital, Risskov, DK 182Munich Cluster for Systems Neurology (SyNergy), Munich, DE 183University of Liverpool, Liverpool, GB Author Manuscript 184Psychiatry and Human Genetics, University of Pittsburgh, Pittsburgh, PA, US 185Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, DK 186Division of Psychiatry, Haukeland Universitetssjukehus, Bergen, NO 187Faculty of Medicine and Dentistry, University of Bergen, Bergen, NO Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 21 188HumanGenetics and Computational Biomedicine, Pfizer Global Research and Author Manuscript Development, Groton, CT, US 189Collegeof Medicine Institute for Genomic Health, SUNY Downstate Medical Center College of Medicine, Brooklyn, NY, US 190Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, NL 191Department of Neurology and Neurosurgery, McGill University, Faculty of Medicine, Montreal, QC, CA 192Montreal Neurological Institute and Hospital, Montreal, QC, CA 193Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, IT Author Manuscript 194Department of Psychiatry, Massachusetts General Hospital, Boston, MA, US 195Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA, US 196Faculty of Medicine, University of Iceland, Reykjavik, IS 197Department of Psychiatry, Hospital Namsos, Namsos, NO 198Department of Mental Health, Norwegian University of Science and Technology, Trondheim NO 199Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, US Author Manuscript 200Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, US 201Department of Psychiatry, McGill University, Montreal, QC, CA 202Deptof Psychiatry, Sankt Olavs Hospital Universitetssykehuset i Trondheim, Trondheim, NO 203Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, ES 204Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services Copenhagen, Roskilde, DK 205Department of Clinical Medicine, University of Copenhagen, Copenhagen, DK Author Manuscript 206Psychiatry, Indiana University School of Medicine, Indianapolis, IN, US 207Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, US Acknowledgments: This paper is dedicated to the memory of Psychiatric Genomics Consortium (PGC) founding member and Bipolar disorder working group co-chair Pamela Sklar. We thank the participants who donated their time, experiences and Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 22 DNA to this research, and to the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who comprise the PGC. The views expressed are those of the authors and not necessarily those of any Author Manuscript funding or regulatory body. Analyses were carried out on the NL Genetic Cluster Computer (http:// www.geneticcluster.org ) hosted by SURFsara, and the Mount Sinai high performance computing cluster (http:// hpc.mssm.edu). PGC members have received major funding from the US National Institute of Mental Health. This work was funded in part by the Brain and Behavior Research Foundation, Stanley Medical Research Institute, University of Michigan, Pritzker Neuropsychiatric Disorders Research Fund L.L.C., Marriot Foundation and the Mayo Clinic Center for Individualized Medicine, the NIMH Intramural Research Program; Canadian Institutes of Health Research; the UK Maudsley NHS Foundation Trust, NIHR, NRS, MRC, Wellcome Trust; European Research Council; German Ministry for Education and Research, German Research Foundation IZKF of Münster, Deutsche Forschungsgemeinschaft, ImmunoSensation, the Dr. Lisa-Oehler Foundation, University of Bonn; the Swiss National Science Foundation; French Foundation FondaMental and ANR; Spanish Ministerio de Economía, CIBERSAM, Industria y Competitividad, European Regional Development Fund (ERDF), Generalitat de Catalunya, EU Horizon 2020 Research and Innovation Programme; BBMRI-NL; South-East Norway Regional Health Authority and Mrs. Throne-Holst; Swedish Research Council, Stockholm County Council, Söderström Foundation; Lundbeck Foundation, Aarhus University; Australia NHMRC, NSW Ministry of Health, Janette M O’Neil and Betty C Lynch. The views expressed are those of the authors and not necessarily those of their institutions or funding bodies. Additional acknowledgements and including funding sources are presented in the Supplementary Note. Author Manuscript References: 1. Ferrari AJ et al. The prevalence and burden of bipolar disorder: findings from the Global Burden of Disease Study 2013. Bipolar Disord. 18, 440–450 (2016). [PubMed: 27566286] 2. Lichtenstein P et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet 373, 234–239 (2009). [PubMed: 19150704] 3. Edvardsen J et al. Heritability of bipolar spectrum disorders. Unity or heterogeneity? J. Affect. Disord 106, 229–240 (2008). [PubMed: 17692389] 4. McGuffin P et al. The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Arch. Gen. Psychiatry 60, 497–502 (2003). [PubMed: 12742871] 5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). (American Psychiatric Pub, 2013). 6. World Health Organization. The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. (World Health Organization, 1992). Author Manuscript 7. Craddock N & Owen MJ The Kraepelinian dichotomy - going, going… but still not gone. Br. J. Psychiatry 196, 92–95 (2010). [PubMed: 20118450] 8. Lee SH et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet 45, 984–994 (2013). [PubMed: 23933821] 9. Sklar P et al. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet 43, 977–U162 (2011). [PubMed: 21926972] 10. Baum AE et al. A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder. Mol. Psychiatry 13, 197–207 (2008). [PubMed: 17486107] 11. Charney AW et al. Evidence for genetic heterogeneity between clinical subtypes of bipolar disorder. Transl. Psychiatry 7, e993 (2017). [PubMed: 28072414] 12. Chen DT et al. Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder. Mol. Psychiatry 18, 195–205 (2013). [PubMed: 22182935] Author Manuscript 13. Cichon S et al. Genome-wide association study identifies genetic variation in neurocan as a susceptibility factor for bipolar disorder. Am. J. Hum. Genet 88, 372–381 (2011). [PubMed: 21353194] 14. Ferreira MAR et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat. Genet 40, 1056–1058 (2008). [PubMed: 18711365] 15. Green EK et al. Association at SYNE1 in both bipolar disorder and recurrent major depression. Mol. Psychiatry 18, 614–617 (2013). [PubMed: 22565781] Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 23 16. Green EK et al. Replication of bipolar disorder susceptibility alleles and identification of two novel genome-wide significant associations in a new bipolar disorder case-control sample. Mol. Author Manuscript Psychiatry 18, 1302–1307 (2013). [PubMed: 23070075] 17. Hou L et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum. Mol. Genet 25, 3383–3394 (2016). [PubMed: 27329760] 18. Mühleisen TW et al. Genome-wide association study reveals two new risk loci for bipolar disorder. Nat. Commun 5, 3339 (2014). [PubMed: 24618891] 19. Schulze TG et al. Two variants in Ankyrin 3 (ANK3) are independent genetic risk factors for bipolar disorder. Mol. Psychiatry 14, 487–491 (2009). [PubMed: 19088739] 20. Scott LJ et al. Genome-wide association and meta-analysis of bipolar disorder in individuals of European ancestry. Proc. Natl. Acad. Sci. U. S. A 106, 7501–7506 (2009). [PubMed: 19416921] 21. Sklar P et al. Whole-genome association study of bipolar disorder. Mol. Psychiatry 13, 558–569 (2008). [PubMed: 18317468] 22. Smith EN et al. Genome-wide association study of bipolar disorder in European American and African American individuals. Mol. Psychiatry 14, 755–763 (2009). [PubMed: 19488044] 23. Burton PR et al. Genome-wide association study of 14,000 cases of seven common diseases and Author Manuscript 3,000 shared controls. Nature 447, 661–678 (2007). [PubMed: 17554300] 24. Gratten J, Wray NR, Keller MC & Visscher PM Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat. Neurosci 17, 782–790 (2014). [PubMed: 24866044] 25. Bulik-Sullivan BK et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet 47, 291–295 (2015). [PubMed: 25642630] 26. Zheng J et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017). [PubMed: 27663502] 27. Palmer C & Pe’er I Statistical correction of the Winner’s Curse explains replication variability in quantitative trait genome-wide association studies. PLoS Genet. 13, e1006916 (2017). [PubMed: 28715421] 28. Zhong H & Prentice RL Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies. Biostatistics 9, 621–634 (2008). [PubMed: 18310059] 29. Holland D et al. Beyond SNP Heritability: Polygenicity and Discoverability Estimated for Multiple Author Manuscript Phenotypes with a Univariate Gaussian Mixture Model. bioRxiv 133132 (2018). doi: 10.1101/133132 30. Gao X, Starmer J & Martin ER A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet. Epidemiol 32, 361–369 (2008). [PubMed: 18271029] 31. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). [PubMed: 25056061] 32. Ripke S et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet 45, 1150–1159 (2013). [PubMed: 23974872] 33. Wray NR & Sullivan PF Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. bioRxiv (2017). 34. Okbay A et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet (2016). doi:10.1038/ng.3552 35. Cross-Disorder Group of the Psychiatric Genomics Consortium et al. Genetic relationship between Author Manuscript five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet 45, 984–994 (2013). [PubMed: 23933821] 36. Duncan L et al. Significant Locus and Metabolic Genetic Correlations Revealed in Genome-Wide Association Study of Anorexia Nervosa. Am. J. Psychiatry appiajp201716121402 (2017). 37. Otowa T et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol. Psychiatry 21, 1391–1399 (2016). [PubMed: 26754954] 38. Gale CR et al. Pleiotropy between neuroticism and physical and mental health: findings from 108 038 men and women in UK Biobank. Transl. Psychiatry 6, e791 (2016). [PubMed: 27115122] Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 24 39. Rietveld CA et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013). [PubMed: 23722424] Author Manuscript 40. Okbay A et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016). [PubMed: 27225129] 41. Benyamin B et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol. Psychiatry 19, 253–258 (2014). [PubMed: 23358156] 42. Sniekers S et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet 49, 1107–1112 (2017). [PubMed: 28530673] 43. MacArthur J et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017). [PubMed: 27899670] 44. Hou L et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum. Mol. Genet 25, 3383–3394 (2016). [PubMed: 27329760] 45. Ikeda M et al. A genome-wide association study identifies two novel susceptibility loci and trans population polygenicity associated with bipolar disorder. Mol. Psychiatry (2017). doi:10.1038/mp. 2016.259 Author Manuscript 46. Finucane HK et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet 47, 1228–1235 (2015). [PubMed: 26414678] 47. Pers TH et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun 6, 5890 (2015). [PubMed: 25597830] 48. Zhu Z et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet 48, 481–487 (2016). [PubMed: 27019110] 49. Pavlides JMW et al. Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Med. 8, 84 (2016). [PubMed: 27506385] 50. Qi T et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun 9, 2282 (2018). [PubMed: 29891976] 51. de Leeuw CA, Mooij JM, Heskes T & Posthuma D MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol 11, e1004219 (2015). [PubMed: 25885710] 52. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Author Manuscript Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes. Cell 173, 1705–1715.e16 (2018). [PubMed: 29906448] 53. Lee SH, Goddard ME, Wray NR & Visscher PM A better coefficient of determination for genetic profile analysis. Genet. Epidemiol 36, 214–224 (2012). [PubMed: 22714935] 54. Simonsen C et al. Neurocognitive dysfunction in bipolar and schizophrenia spectrum disorders depends on history of psychosis rather than diagnostic group. Schizophr. Bull 37, 73–83 (2011). [PubMed: 19443616] 55. Goes FS et al. Genome-wide association of mood-incongruent psychotic bipolar disorder. Transl. Psychiatry 2, e180 (2012). [PubMed: 23092984] 56. Allardyce J et al. Association Between Schizophrenia-Related Polygenic Liability and the Occurrence and Level of Mood-Incongruent Psychotic Symptoms in Bipolar Disorder. JAMA Psychiatry 75, 28–35 (2018). [PubMed: 29167880] 57. Palmer C & Pe’er I Statistical Correction of the Winner9s Curse Explains Replication Variability in Quantitative Trait Genome-Wide Association Studies. bioRxiv 104786 (2017). Author Manuscript 58. Goes FS et al. Genome-wide association study of schizophrenia in Ashkenazi Jews. Am. J. Med. Genet. B Neuropsychiatr. Genet 168, 649–659 (2015). [PubMed: 26198764] 59. Gaspar HA & Breen G Pathways analyses of schizophrenia GWAS focusing on known and novel drug targets. Biorxiv (2017). doi:10.1101/091264 60. Camandola S & Mattson MP Aberrant subcellular neuronal calcium regulation in aging and Alzheimer’s disease. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 1813, 965– 973 (2011). [PubMed: 20950656] 61. Mertens J et al. Differential responses to lithium in hyperexcitable neurons from patients with bipolar disorder. Nature 527, 95–99 (2015). [PubMed: 26524527] Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 25 62. Yang C-P et al. Comprehensive integrative analyses identify GLT8D1 and CSNK2B as schizophrenia risk genes. Nat. Commun 9, 838 (2018). [PubMed: 29483533] Author Manuscript 63. Lee S-H, Zabolotny JM, Huang H, Lee H & Kim Y-B Insulin in the nervous system and the mind: Functions in metabolism, memory, and mood. Mol Metab 5, 589–601 (2016). [PubMed: 27656397] 64. McIntyre RS et al. A randomized, double-blind, controlled trial evaluating the effect of intranasal insulin on neurocognitive function in euthymic patients with bipolar disorder. Bipolar Disord. 14, 697–706 (2012). [PubMed: 23107220] 65. Desfossés J, Stip E, Bentaleb LA & Potvin S Endocannabinoids and Schizophrenia. Pharmaceuticals 3, 3101–3126 (2010). 66. Zamberletti E, Rubino T & Parolaro D The endocannabinoid system and schizophrenia: integration of evidence. Curr. Pharm. Des 18, 4980–4990 (2012). [PubMed: 22716159] 67. Serra G & Fratta W A possible role for the endocannabinoid system in the neurobiology of depression. Clin. Pract. Epidemiol. Ment. Health 3, 25 (2007). [PubMed: 18021439] 68. MacCabe JH et al. Excellent school performance at age 16 and risk of adult bipolar disorder: national cohort study. Br. J. Psychiatry 196, 109–115 (2010). [PubMed: 20118454] Author Manuscript 69. Vreeker A et al. High educational performance is a distinctive feature of bipolar disorder: a study on cognition in bipolar disorder, schizophrenia patients, relatives and controls. Psychol. Med 46, 807–818 (2016). [PubMed: 26621616] Methods-only References: 70. Ripke S Ricopili: a tool for visualizing regions of interest in select GWAS data sets. (2014). 71. Purcell S et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet 81, 559–575 (2007). [PubMed: 17701901] 72. Chang CC et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015). [PubMed: 25722852] 73. Euesden J, Lewis CM & O’Reilly PF PRSice: Polygenic Risk Score software. Bioinformatics 31, 1466–1468 (2015). [PubMed: 25550326] 74. Finucane H et al. Heritability enrichment of specifically expressed genes identifies disease-relevant Author Manuscript tissues and cell types. doi:10.1101/103069 75. O’Dushlaine C et al. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci 18, 199–209 (2015). [PubMed: 25599223] 76. 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015). [PubMed: 26432245] 77. Olier I, Vellido A & Giraldo J Kernel generative topographic mapping. in ESANN 2010, 481–486 (2010). Author Manuscript Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 26 Editorial Summary: Author Manuscript Genome-wide analysis identifies 30 loci associated with bipolar disorder, allowing for comparisons of shared genes and pathways with other psychiatric disorders, including schizophrenia and depression. Author Manuscript Author Manuscript Author Manuscript Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 27 Author Manuscript Author Manuscript Author Manuscript Figure 1. Manhattan plot for our primary genomewide association analysis of 20,352 cases and 31,358 controls. GWAS -log10P-values are plotted for all SNPs across chromosomes 1-22 (diamonds, green for loci with lead SNP GWAS P < 10−6). Combined GWAS+followup −log10P-values for lead SNPs reaching genome-wide significance in either GWAS or combined analysis (triangles, inverted if GWAS+followup −log10P > GWAS -log10P). Labels correspond to gene symbols previously reported for published loci (black) and the nearest genes for novel loci (blue), at top if GWAS+followup P < 5×10−8. Loci with one- tailed follow-up p > 0.05 (Table 1) have dotted underlined locus names. Author Manuscript Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 28 Author Manuscript Author Manuscript Author Manuscript Figure 2. Author Manuscript Association of BD1 and BD2 subtypes with schizophrenia (SCZ) and major depression (DEPR) polygenic risk scores (PRS). Shown are mean PRS values (1 s.e. error bars), adjusted for study and ancestry covariates and scaled to the PRS mean and sd in control subjects, in BD1 (red) and BD2 (blue) cases, for increasing source GWAS P-value thresholds (increasing grey) as indicated. P-values (italics) test BD1 vs BD2 mean PRS, in Nat Genet. Author manuscript; available in PMC 2020 January 13. Stahl et al. Page 29 logistic regression of case subtype on PRS with covariates. Results are detailed in Author Manuscript Supplementary Table 13. Author Manuscript Author Manuscript Author Manuscript Nat Genet. Author manuscript; available in PMC 2020 January 13. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Table 1. Genome-wide significant bipolar disorder risk loci GWAS Meta-analysis Follow-up samples Combined Stahl et al. *1 Lead SNP CHR BP A1/A2 Locus Name *2 *3 *2 Freq. A1 OR P-value OR P-value OR P-value A. Thirty loci with lead SNP P < 5×10−8 in combined GWAS+followup analysis 1,PLEKHO1 rs7544145 1 150,138,699 T/C 0.81 1.095 4.8E-07 1.064 0.010 1.085 4.8E-08 2,LMAN2L** rs57195239 2 97,376,407 I/D 0.34 0.92 5.8E-09 0.96 0.030 0.93 3.8E-09 3,SCN2A rs17183814 2 166,152,389 A/G 0.075 0.87 1.5E-07 0.89 0.0017 0.88 2.0E-09 4,[Intergenic]*** rs61332983 2 194,465,711 I/D 0.41 0.93 2.3E-08 0.95 0.0031 0.93 7.9E-10 5,TRANK1** rs9834970 3 36,856,030 T/C 0.51 0.90 5.5E-14 0.98 0.15 0.93 5.7E-12 6,ITIH1** rs2302417 3 52,814,256 A/T 0.49 0.92 4.9E-09 0.94 0.0012 0.93 6.6E-11 7,CD47 rs3804640 3 107,793,709 A/G 0.53 1.075 9.3E-08 1.044 0.016 1.065 2.0E-08 8,FSTL5 rs11724116 4 162,294,038 T/C 0.16 0.90 3.3E-08 0.95 0.031 0.92 2.4E-08 9,ADCY2** rs200550695 5 7,587,236 I/D 0.82 0.91 1.2E-07 0.94 0.011 0.92 1.5E-08 10,SSBP2 rs10035291 5 80,796,368 T/C 0.68 1.081 1.1E-07 1.047 0.018 1.070 2.7E-08 11,RIMS1 rs57970360 6 72,519,394 D/I 0.44 1.066 3.1E-06 1.062 0.0016 1.064 3.5E-08 12,POU3F2** rs2388334 6 98,591,622 A/G 0.52 0.93 8.6E-08 0.95 0.0051 0.94 4.0E-09 13,RPS6KA2 rs10455979 6 166,995,260 C/G 0.53 0.93 4.6E-08 0.97 0.046 0.94 4.3E-08 14,THSD7A rs113779084 7 11,871,787 A/G 0.30 1.068 7.3E-06 1.095 2.9E-05 1.076 2.5E-09 15,SRPK2 rs73188321 7 105,048,158 T/C 0.33 0.92 7.0E-08 0.94 0.0015 0.92 1.1E-09 Nat Genet. Author manuscript; available in PMC 2020 January 13. 16,MRPS33 rs201231874 7 140,700,006 D/I 0.25 0.92 9.4E-08 0.93 0.0008 0.92 6.2E-10 17,ANK3** rs10994318 10 62,125,856 C/G 0.057 1.151 4.5E-07 1.130 0.0021 1.145 6.8E-09 18,ADD3** rs59134449 10 111,745,562 I/D 0.16 1.105 5.0E-08 1.059 0.017 1.090 1.2E-08 19,FADS2** rs12226877 11 61,591,907 A/G 0.29 1.095 1.2E-08 1.062 0.0073 1.085 9.9E-10 20,PACS1 rs10896090 11 65,945,186 A/G 0.81 1.094 2.1E-07 1.062 0.0089 1.084 1.9E-08 21,PC rs7122539 11 66,662,731 A/G 0.35 0.93 2.2E-07 0.96 0.015 0.94 3.8E-08 22,SHANK2 rs12575685 11 70,517,927 A/G 0.31 1.066 1.2E-05 1.088 5.7E-05 1.073 7.7E-09 23,CACNA1C** rs10744560 12 2,387,099 T/C 0.34 1.087 2.9E-09 1.052 0.0086 1.076 3.6E-10 24,STARD9 rs4447398 15 42,904,904 A/C 0.12 1.112 1.1E-07 1.072 0.0079 1.099 9.4E-09 Page 30 Author Manuscript Author Manuscript Author Manuscript Author Manuscript GWAS Meta-analysis Follow-up samples Combined *1 Lead SNP CHR BP A1/A2 Locus Name *2 *3 *2 Freq. A1 OR P-value OR P-value OR P-value 25,ZNF592 rs139221256 15 85,357,857 I/D 0.28 0.92 8.5E-09 0.97 0.082 0.93 2.7E-08 Stahl et al. 26,GRIN2A rs11647445 16 9,926,966 T/G 0.65 0.93 1.2E-07 0.93 9.8E-05 0.93 1.1E-10 27,HDAC5 rs112114764 17 42,201,041 T/G 0.69 0.93 1.7E-06 0.94 0.0021 0.93 2.5E-08 28,ZCCHC2 rs11557713 18 60,243,876 A/G 0.29 1.074 1.2E-06 1.059 0.0038 1.069 3.6E-08 29,NCAN** rs111444407 19 19,358,207 T/C 0.15 1.124 2.4E-10 1.040 0.075 1.097 1.3E-09 30,STK4 rs202012857 20 43,682,549 I/D 0.28 0.923 3.0E-07 0.942 0.0043 0.929 1.1E-08 B. Additional loci with lead SNP P < 5×10−8 in GWAS analysis TFAP2B rs55648125 6 50,816,718 A/G 0.90 0.89 4.9E-08 0.95 0.068 0.91 8.5E-08 DFNA5 rs17150022 7 24,771,777 T/C 0.88 0.89 2.7E-08 0.96 0.087 0.91 8.6E-08 SLC25A17 rs138321 22 41,209,304 A/G 0.50 1.083 4.7E-09 1.012 0.28 1.060 1.9E-07 HLF rs884301 17 53,367,464 T/C 0.37 1.084 5.8E-09 1.013 0.26 1.061 2.1E-07 PHF15 rs329319 5 133,906,609 A/G 0.43 1.082 1.5E-08 1.019 0.18 1.061 2.1E-07 ODZ4** rs73496688 11 79,156,748 A/T 0.14 1.11 1.0E-08 1.016 0.29 1.083 4.2E-07 [Intergenic]*** rs57681866 2 57,975,714 A/G 0.06 0.85 5.0E-08 0.97 0.23 0.89 1.2E-06 [Intergenic]*** rs13231398 7 110,197,412 C/G 0.11 0.89 3.4E-08 0.998 0.47 0.92 4.6E-06 *1 Loci are numbered 1 to 30, ordered by genomic position, with previously reported gene name for published loci *2 P-values for GWAS and combined analyses are two-tailed, bold and underlined if p < 5×10−8. *3 P-values for follow-up are one-tailed based on the direction of effect in the discovery GWAS, bold and underlined if p < 0.05. Nat Genet. Author manuscript; available in PMC 2020 January 13. ** Previously published and named loci. (Locus 12 would be named as Intergenic, nearest gene is POU3F2 691Kb.) *** Intergenic loci nearest genes: Locus 4 PCGEM1 824kb, Table 1B chr2 locus VRK2 298Kb, Table 1B chr7 IMMP2L 106Kb. Page 31