Schizophrenia is a debilitating psychiatric disorder with approximately 1% lifetime risk globally. Large-scale schizophrenia genetic studies have reported primarily on European ancestry samples, potentially missing important biological insights. Here, we report the largest study to date of East Asian participants (22,778 schizophrenia cases and 35,362 controls), identifying 21 genome-wide-significant associations in 19 genetic loci. Common genetic variants that confer risk for schizophrenia have highly similar effects between East Asian and European ancestries (genetic correlation = 0.98 ± 0.03), indicating that the genetic basis of schizophrenia and its biology are broadly shared across populations. A fixed-effect meta-analysis including individuals from East Asian and European ancestries identified 208 significant associations in 176 genetic loci (53 novel). Trans-ancestry fine-mapping reduced the sets of candidate causal variants in 44 loci. Polygenic risk scores had reduced performance when transferred across ancestries, highlighting the importance of including sufficient samples of major ancestral groups to ensure their generalizability across populations.
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Ultra-rare and common genetic variant analysis converge to implicate negative selection and neuronal processes in the aetiology of schizophrenia
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Genome-wide summary statistics relating to the EAS samples, EUR samples (n = 49) and all samples combined (that is, EAS and EUR) can be downloaded from https://www.med.unc.edu/pgc/. Individual-level genotype data for EAS samples are available on request from the contact authors (Supplementary Note). Alternatively, requests can be made to the Psychiatric Genomics Consortium. In this case, access to individual-level genotypes from samples recruited outside of mainland China will go through the Psychiatric Genomics Consortium’s fast-track approval system. Access to individual-level genotypes from samples recruited within mainland China has to be approved by the individual Chinese contact authors (Supplementary Note), and are subject to the policies and approvals from the Human Genetic Resource Administration, Ministry of Science and Technology of the People’s Republic of China. Individual-level genotypes from samples recruited within mainland China are stored and kept in a server physically located in mainland China. Analyses were performed on these samples using the same computer codes as those used for other EAS and EUR samples, which are available in the ‘Code availability’ section.
Computer code relating to this study includes: RICOPILI (quality control, PCA, pre-phasing, imputation, association test and meta-analysis; https://github.com/Nealelab/ricopili/wiki); The following code is embedded within RICOPILI (EIGENSTRAT; https://github.com/DReichLab/EIG/tree/master/EIGENSTRAT; SHAPEIT, https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html; EAGLE, https://github.com/poruloh/Eagle; IMPUTE, https://mathgen.stats.ox.ac.uk/impute/impute_v2.html; Minimac, https://genome.sph.umich.edu/wiki/Minimac); POPCORN (trans-ancestry genetic correlation; https://github.com/brielin/Popcorn); LDSC (heritability, partitioned heritability and within-ancestry genetic correlation; https://github.com/bulik/ldsc); MAGMA (pathway analysis; https://ctg.cncr.nl/software/magma); fine-mapping (fine-mapping and PAINTOR; https://github.com/hailianghuang/FM-summary and https://github.com/gkichaev/PAINTOR_V3.0, respectively); rehh (selection; https://cran.r-project.org/web/packages/rehh/index.html); B score (background selection; http://www.phrap.org/othersoftware.html); and PRS analyses (https://github.com/armartin/pgc_scz_asia).
Stilo, S. A. & Murray, R. M. The epidemiology of schizophrenia: replacing dogma with knowledge. Dialogues Clin. Neurosci. 12, 305–315 (2010).
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
Li, Y. R. & Keating, B. J. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med. 6, 91 (2014).
Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).
Yu, H. et al. Common variants on 2p16.1, 6p22.1 and 10q24.32 are associated with schizophrenia in Han Chinese population. Mol. Psychiatry 22, 954–960 (2017).
Li, Z. et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat. Genet. 49, 1576–1583 (2017).
Moltke, I. et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512, 190–193 (2014).
Tang, C. S. et al. Exome-wide association analysis reveals novel coding sequence variants associated with lipid traits in Chinese. Nat. Commun. 6, 10206 (2015).
Clarke, T.-K. et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117). Mol. Psychiatry 22, 1376–1384 (2017).
CONVERGE consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015).
Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).
Report of the International Pilot Study of Schizophrenia (World Health Organization, 1973).
Carpenter, W. T. Jr, Strauss, J. S. & Bartko, J. J. Flexible system for the diagnosis of schizophrenia: report from the WHO International Pilot Study of Schizophrenia. Science 182, 1275–1278 (1973).
Jablensky, A. et al. Schizophrenia: manifestations, incidence and course in different cultures. A World Health Organization ten-country study. Psychol. Med. Monogr. Suppl. 20, 1–97 (1992).
McGrath, J. J. Variations in the incidence of schizophrenia: data versus dogma. Schizophr. Bull. 32, 195–197 (2006).
Haro, J. M. et al. Cross-national clinical and functional remission rates: worldwide schizophrenia outpatient health outcomes (W-SOHO) study. Br. J. Psychiatry 199, 194–201 (2011).
Gureje, O. & Cohen, A. Differential outcome of schizophrenia: where we are and where we would like to be. Br. J. Psychiatry 199, 173–175 (2011).
Scutari, M., Mackay, I. & Balding, D. Using genetic distance to infer the accuracy of genomic prediction. PLoS Genet. 12, e1006288 (2016).
Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).
Bulik-Sullivan, B. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Geisler, S., Schöpf, C. L. & Obermair, G. J. Emerging evidence for specific neuronal functions of auxiliary calcium channel α2δ subunits. Gen. Physiol. Biophys. 34, 105–118 (2015).
Heyes, S. et al. Genetic disruption of voltage-gated calcium channels in psychiatric and neurological disorders. Prog. Neurobiol. 134, 36–54 (2015).
Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).
Gulsuner, S. et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154, 518–529 (2013).
Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).
Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151). Mol. Psychiatry 21, 758–767 (2016).
Cross-Disorder Group of the Psychiatric Genomics Consortium et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).
Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Bryois, J. et al. Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia. Nat. Commun. 9, 3121 (2018).
Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).
De Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Genovese, G. et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19, 1433–1441 (2016).
Bowden, J. & Dudbridge, F. Unbiased estimation of odds ratios: combining genomewide association scans with replication studies. Genet. Epidemiol. 33, 406–418 (2009).
Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. Introduction to Meta-Analysis (John Wiley & Sons, 2009).
Spain, S. L. & Barrett, J. C. Strategies for fine-mapping complex traits. Hum. Mol. Genet. 24, R111–R119 (2015).
Schaid, D. J., Chen, W. & Larson, N. B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19, 491–504 (2018).
Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).
Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
Swaminathan, B. et al. Fine mapping and functional analysis of the multiple sclerosis risk gene CD6. PLoS ONE 8, e62376 (2013).
Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).
Kichaev, G. & Pasaniuc, B. Leveraging functional-annotation data in trans-ethnic fine-mapping studies. Am. J. Hum. Genet. 97, 260–271 (2015).
Pawitan, Y., Seng, K. C. & Magnusson, P. K. E. How many genetic variants remain to be discovered? PLoS ONE 4, e7969 (2009).
Márquez-Luna, C., Loh, P.-R., South Asian Type 2 Diabetes (SAT2D) Consortium, SIGMA Type 2 Diabetes Consortium & Price, A. L. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41, 811–823 (2017).
Chen, C.-Y., Han, J., Hunter, D. J., Kraft, P. & Price, A. L. Explicit modeling of ancestry improves polygenic risk scores and BLUP prediction. Genet. Epidemiol. 39, 427–438 (2015).
Yue, W.-H. et al. Genome-wide association study identifies a susceptibility locus for schizophrenia in Han Chinese at 11p11.2. Nat. Genet. 43, 1228–1231 (2011).
Shi, Y. et al. Common variants on 8p12 and 1q24.2 confer risk of schizophrenia. Nat. Genet. 43, 1224–1227 (2011).
Salinas, Y. D., Wang, L. & DeWan, A. T. Multiethnic genome-wide association study identifies ethnic-specific associations with body mass index in Hispanics and African Americans. BMC Genet. 17, 78 (2016).
International Schizophrenia Consortium et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).
Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).
Chen, J. Y. et al. Effects of complement C4 gene copy number variations, size dichotomy, and C4A deficiency on genetic risk and clinical presentation of systemic lupus erythematosus in East Asian populations. Arthritis Rheumatol. 68, 1442–1453 (2016).
Hong, G. H. et al. Association of complement C4 and HLA-DR alleles with systemic lupus erythematosus in Koreans. J. Rheumatol. 21, 442–447 (1994).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders 4th edn (American Psychiatric Association, 2000).
Nurnberger, J. I. Jr. et al. Diagnostic interview for genetic studies: rationale, unique features, and training. NIMH Genetics Initiative. Arch. Gen. Psychiatry 51, 849–859 (1994).
Sand, P. G. A lesson not learned: allele misassignment. Behav. Brain Funct. 3, 65 (2007).
Lam, M. et al. RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics https://doi.org/10.1093/bioinformatics/btz633 (2019).
O’Connell, J. et al. A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 10, e1004234 (2014).
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).
1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
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).
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43, 977–983 (2011).
Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).
Boraska, V. et al. A genome-wide association study of anorexia nervosa. Mol. Psychiatry 19, 1085–1094 (2014).
Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
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).
Juric, I., Aeschbacher, S. & Coop, G. The strength of selection against Neanderthal introgression. PLoS Genet. 12, e1006340 (2016).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Turner, T. N. et al. denovo-db: a compendium of human de novo variants. Nucleic Acids Res. 45, D804–D811 (2017).
Pirooznia, M. et al. High-throughput sequencing of the synaptome in major depressive disorder. Mol. Psychiatry 21, 650–655 (2016).
Gautier, M. & Vitalis, R. rehh: an R package to detect footprints of selection in genome-wide SNP data from haplotype structure. Bioinformatics 28, 1176–1177 (2012).
Sabeti, P. C. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918 (2007).
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).
Grossman, S. R. et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327, 883–886 (2010).
Grossman, S. R. et al. Identifying recent adaptations in large-scale genomic data. Cell 152, 703–713 (2013).
Ma, Y. et al. Properties of different selection signature statistics and a new strategy for combining them. Heredity 115, 426–436 (2015).
McVicker, G., Gordon, D., Davis, C. & Green, P. Widespread genomic signatures of natural selection in hominid evolution. PLoS Genet. 5, e1000471 (2009).
Gormley, P. et al. Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat. Genet. 48, 856–866 (2016).
Wellcome Trust Case Control Consortium et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).
Lee, S. H., Goddard, M. E., Wray, N. R. & Visscher, P. M. A better coefficient of determination for genetic profile analysis. Genet. Epidemiol. 36, 214–224 (2012).
We thank K. Kendler, J. McGrath, J. Walters, D. Levinson and M. Owen for helpful discussions. We thank S. Awathi, V. Trubetskoy and G. Panagiotaropoulou for support with the RICOPILI analysis pipeline. We thank SURFsara and Digital China Health for computing infrastructure for this study. M.L. acknowledges the National Medical Research Council Research Training Fellowship award (grant number: MH095:003/008-1014). A.R.M. acknowledges funding from K99MH117229. B.B. acknowledges funding support from the National Health and Medical Research Council (NHMRC; funding numbers 1084417 and 1079583). L.G. acknowledges the National Key Research and Development Program of the Ministry of Science and Technology of China (2016YFC1306802). M.I., Y.K. and N.I. acknowledge the Strategic Research Program for Brain Sciences (grant number JP19dm0107097) and Advanced Genome Research and Bioinformatics Study toFacilitate Medical Innovation (GRIFIN) of Platform Program for Promotion of Genome Medicine (P3GM) (grant numbers JP19km0405201 and JP19km0405208) from the Japan Agency for Medical Research and Development. Y.K., M.K. and A.T. acknowledge the BioBank Japan Project from the Ministry of Education, Culture, Sports, Science and Technology of Japan. S.-W.K. acknowledges a grant of the Korean Mental Health Technology Research and Development Project (HM15C1140). W.J.C. acknowledges the Ministry of Education, Taiwan (‘Aim for the Top University Project’ to National Taiwan University; 2011–2017), Ministry of Science and Technology, Taiwan (MOST 103-2325-B-002-025), National Health Research Institutes, Taiwan (NHRI-EX104-10432PI), NIH/NHGRI grant U54HG003067, NIMH grant R01 MH085521 and NIMH grant R01 MH085560. S.J.G. acknowledges R01 MH08552. B.J.M. acknowledges Australian NHMRC grant 496698. H.-G.H. acknowledges the Ministry of Education, Taiwan (‘Aim for the Top University Project’ to National Taiwan University; 2011–2017), MOST, Taiwan (MOST 103-2325-B-002-025), NIH/NHGRI grant U54HG003067, NIMH grants R01 MH085521 and R01 MH085560, and NHRI, Taiwan (NHRI-EX104-10432PI). P. Sklar acknowledges U01MH109536. B.J.M. acknowledges Australian NHMRC grant 496698. J. Lee acknowledges the National Medical Research Council Translational and Clinical Research Flagship Programme (grant number: NMRC/TCR/003/2008) and the National Medical Research Council under the Centre Grant Programme (grant number: NMRC/CG/004/2013). P.H. acknowledges funding support from the Medical Research Council (MR/L010305/1). S.X. acknowledges funding support from the Strategic Priority Research Program (XDB13040100) and Key Research Program of Frontier Sciences (QYZDJ-SSW-SYS009) of the Chinese Academy of Sciences, as well as the National Natural Science Foundation of China (NSFC; grants 91731303, 31525014 and 31771388), UK Royal Society–Newton Advanced Fellowship (NAF/R1/191094), Program of Shanghai Academic Research Leader (16XD1404700), National Key Research and Development Program (2016YFC0906403) and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01). P.F.S. acknowledges PGC funding from U01 MH109528 and U01 MH1095320. M.J.D. acknowledges NIH/NIMH 5U01MH109539-02. M.C.O.’D. acknowledges funding from MRC (G0800509) and NIMH (reference: 1U01MH109514-01). S.Q. acknowledges the National Key Research and Development Program of China (2016YFC0905000 and 2016YFC0905002) and the Shanghai Key Laboratory of Psychotic Disorders (13dz2260500). K.S.H. acknowledges a grant from the National Research Foundation of Korea (2015R1A2A2A01002699), funded by the Ministry of Science, ICT and Future Planning. D.B.W. and S.G.S. acknowledge funding support from the NHMRC (grant 513861). W.Y. acknowledges the National Key Research and Development Program of China (2016YFC1307000), NSFC (81571313) and the Peking University Clinical Scientist Program, supported by ‘the Fundamental Research Funds for the Central Universities’ (BMU2019LCKXJ012). M.T. acknowledges R01 MH085560 (Expanding Rapid Ascertainment Networks of Schizophrenia Families in Taiwan). J. Liu acknowledges funding support from the Agency for Science, Technology and Research, Singapore. Xiancang Ma acknowledges (and was principal investigator on) the National NSFC Surface project (81471374). Y.S. acknowledges the National Key Research and Development Program of China (2016YFC0903402), NSFC (31325014, 81130022 and 81421061) and 973 Program (2015CB559100). H.H. acknowledges support from NIH K01DK114379, NIH R21AI139012, the Zhengxu and Ying He Foundation and the Stanley Center for Psychiatric Research.
B.M.N. is a member of the Deep Genomics Scientific Advisory Board. He also serves as a consultant for the Camp4 Therapeutics Corporation, Takeda Pharmaceutical and Biogen. M.J.D. is a founder of Maze Therapeutics. The remaining authors declare no competing interests.
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a-p, QQ plots for two-tailed logistic regression in each EAS stage 1 sample (a-m) and fixed effect inverse variance meta-analyses including all EAS stage 1 samples (n), stages 1 and 2 samples (o), and all EUR and EAS (stages 1 and 2) samples (p). Blue line indicates the expected null distribution, and the shaded area indicates the 95% confidence interval of the null distribution. Legend: “lambda” = genomic inflation factor; “lambda1000” = genomic inflation factor for an equivalent study of 1,000 cases and 1,000 controls; “N(pvals)” = number of variants used in the plot. Autosomal variants that have minor allele frequency ≥ 1% and INFO ≥ 0.6 from imputation were included. Observed P-values were capped at 10-12 for visualization purposes.
a, Heritability (h2) for the EAS stage 1 (n = 13,305 cases; 16,244 controls) and EUR samples (n = 33,640 cases; 43,456 controls). Sample description applies to b-d. Error bars indicate the 95% confidence interval. b, Genetic correlation between schizophrenia and other traits within EUR (blue) and across EAS and EUR (red). Error bars indicate the 95% confidence interval. c, Enrichment and its corresponding significance for heritability partitioned based on various annotations. Error bars indicate the 95% confidence interval. d, Scatterplot showing the enrichment versus the significance for heritability partitioned based on various annotations. More details are available in Methods.
a, Overlap of implicated gene-sets across EAS stage 1 (n = 13,305 cases; 16,244 controls) and EUR samples (n = 33,640 cases; 43,456 controls). b, List of the top 10 gene-sets implicated in the EAS and EUR samples and their MAGMA Gene-Set Analysis P-values in -log10 scale. Descriptions of the gene-sets are available in Supplementary Table 8.
a, Distributions of natural selection signals in the top 100 schizophrenia associations in EAS (red) and EUR (blue). b, Scatterplot of Fst versus the heterogeneity of effect size for schizophrenia associations. More details are available in Methods.
a, Heterogeneity QQ-plot across Northeast Asian and Indonesian samples. b, Heterogeneity across Southeast Asian and Indonesian samples. c, Heterogeneity QQ-plot across Northeast Asian and Southeast Asian samples. d, Heterogeneity QQ-plots across all three subpopulations. Cochran Q-test used to compute heterogeneity effects (a-d).
Illustration of the fine-mapping method.
Genome-wide significant associations that have variance explained greater than 0.05% in either EAS or EUR samples were plotted. One locus can host multiple independent associations. Different MAF is defined as Fst > 0.01, and different OR is defined as heterogeneity test P-value < 0.05 after Bonferroni correction. Nearest genes to the associations were used as labels for associations when the text space is available, with the exception that the MHC locus was labeled as “MHC”.
As in Fig. 4, PRS shows case/control variance explained with EUR and EAS samples using a leave-one-out meta-analysis approach for the EAS samples. Error bars indicate the 95% confidence intervals. a,b, Liability-scale variance explained when LD panel for clumping is from EUR 1000 Genomes Phase 3 samples and best-guess genotypes are from each EAS cohort. c,d, Nagelkerke’s R2 for EAS prediction accuracy when LD panel for clumping is from EUR and EAS 1000 Genomes Phase 3 samples. e,f, Nagelkerke’s R2 for EAS prediction accuracy when LD panel for clumping is from EUR 1000 Genomes Phase 3 samples and best-guess genotypes are from each EAS cohort. a-f, EAS stage 1 (n = 13,305 cases; 16,244 controls) and EUR samples (n = 33,640 cases; 43,456 controls).
Extended Data Fig. 9 Ratio of the heterozygote rate in EAS to that in EUR for existing and new loci.
Het(EAS) and Het(EUR), calculated as 2f(1−f), are the heterozygote rates for a variant in EAS and EUR respectively, in which f is the variant allele frequency in EAS or EUR. Power to identify genetic associations increases with the expected non-centrality parameter for the association, which is proportional to the heterozygote rate. Therefore, we use the ratio of the heterozygote rate in EAS to that in EUR as a measure of the relative power to identify genetic association of the same effect size in the two populations. A ratio greater than 1 means that EAS samples have more power to identify the association and vice versa. Existing loci are those that are genome-wide significant in the previous study of European ancestry2, and new loci are those that are genome-wide significant just in this study combining EAS and EUR samples. Sample sizes utilized were EAS stage 1 (n = 13,305 cases; 16,244 controls) and EUR samples (n = 33,640 cases; 43,456 controls).
a, EAS samples mapped to the global principal components created using 1000 Genomes Project Phase 1 samples. b, EAS cases and controls mapped respectively to principal components created using all EAS samples in this study. Populations were inferred from sample recruitment locations. a-b, n EAS controls = 17,261; cases = 14,607. Samples from stages 1 and 2 that have individual-level genotypes were included (before the removal of population outliers).
Supplementary Table 1 and Note
Supplementary Tables 2–8
Region association plots for the 19 genomic loci hosting genome-wide-significant associations.
Forest plots for 21 associations with schizophrenia in EAS.
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Lam, M., Chen, CY., Li, Z. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat Genet 51, 1670–1678 (2019). https://doi.org/10.1038/s41588-019-0512-x
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