Comparative genetic architectures of schizophrenia in East Asian and European populations

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Abstract

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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Fig. 1: Genetic associations in EAS populations.
Fig. 2: Schizophrenia associations in EUR and EAS samples.
Fig. 3: Trans-ethnic fine-mapping improves resolution.
Fig. 4: Genetic risk prediction accuracy in EAS from EAS or EUR training data.

Data availability

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.

Code availability

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).

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Acknowledgements

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.

Author information

M.L. and H.H. performed the genotype quality control and PCA. M.L. and C.-Y.C. performed the association analysis. M.L. and B.C.B. performed the heritability and genetic correlation. Xixian Ma, C.-Y.C. and S.X. investigated the effects of natural selection. J.B. investigated the effects of partitioned heritability. Z.L., M.L. and H.G. performed the gene set analysis. A.R.M., C.-Y.C. and R.L. calculated the PRSs. H.H. performed the fine-mapping. P.H. performed the replication analysis. Data acquisition, generation, quality control and analysis were performed by: M.L., J. Lee and J. Liu (IMH-1 and IMH-2); P. Sham (HNK-1); A.T., Y.K., M.K., M.I. and N.I. (JPN-1); Z.L., L.H. and Y.S. (BIX-1, BIX-3 and BIX-5); W.Z., L.H., S.Q., F.Z. and Xiancang Ma (XJU-1 and BIX-4); L.G., H.M., Z.X., P. Sklar, X.Y., R.S.K. and the Genetic REsearch on schizophreniA neTwork-China and Netherlands (UMC-1 and SIX-1); B.B., A.K., D.B.W., S.G.S. and the Indonesia Schizophrenia Consortium (UWA-1); H.Y., D.Z. and W.Y. (BJM-1, BJM-2, BJM-3 and BJM-4); C.-M.L., W.J.C., S.F., S.J.G., H.-G.H., S.A.M., B.M.N. and M.T. (TAI-1 and TAI-2); S.-W.K. and K.S.H. (KOR-1); and the Schizophrenia Working Group of the Psychiatric Genomics Consortium (EUR samples). M.L., C.-Y.C., S.G.S., M.C.O.’D., M.J.D. and H.H. drafted the primary manuscript, with major contributions from A.R.M., S.J.G., B.B., S.P., B.J.M., K.S.H., M.T., J. Lee, W.Y., H.-G.H., J.B., S.R. and P.F.S. H.H., Y.S., R.S.K., X.C.M., J. Liu., M.T., W.Y., S.G.S., K.S.H., N.I., P. Sham, S.Q., B.M.N., M.J.D., M.C.O.’D., S.R., P. Sklar, L.H. and S.E.H. conceived, designed, supervised and implemented the project. All authors reviewed and approved the final draft.

Correspondence to Weihua Yue or Ming Tsuang or Jianjun Liu or Xiancang Ma or René S. Kahn or Yongyong Shi or Hailiang Huang.

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Competing interests

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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Extended data

Extended Data Fig. 1 Quantile-quantile (QQ) plots.

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.

Extended Data Fig. 2 Heritability and genetic correlation.

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.

Extended Data Fig. 3 Gene-sets implicated by schizophrenia genetic associations.

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.

Extended Data Fig. 4 Natural selection signals in EAS and EUR.

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.

Extended Data Fig. 5 Quantile-quantile (QQ) plots for heterogeneity within EAS.

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).

Extended Data Fig. 6 Trans-ethnicity fine-mapping.

Illustration of the fine-mapping method.

Extended Data Fig. 7 Variance explained for schizophrenia associations across EUR and EAS samples.

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”.

Extended Data Fig. 8 Genetic risk prediction accuracy in EAS from EAS or EUR training data.

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).

Extended Data Fig. 10 Principal component analysis of EAS samples.

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 information

Supplementary Information

Supplementary Table 1 and Note

Reporting Summary

Supplementary Tables

Supplementary Tables 2–8

Supplementary Dataset 1

Region association plots for the 19 genomic loci hosting genome-wide-significant associations.

Supplementary Dataset 2

Forest plots for 21 associations with schizophrenia in EAS.

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Lam, M., Chen, C., Li, Z. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat Genet 51, 1670–1678 (2019) doi:10.1038/s41588-019-0512-x

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