Abstract

We conducted a genome-wide association study (GWAS) with replication in 36,180 Chinese individuals and performed further transancestry meta-analyses with data from the Psychiatry Genomics Consortium (PGC2). Approximately 95% of the genome-wide significant (GWS) index alleles (or their proxies) from the PGC2 study were overrepresented in Chinese schizophrenia cases, including 50% that achieved nominal significance and 75% that continued to be GWS in the transancestry analysis. The Chinese-only analysis identified seven GWS loci; three of these also were GWS in the transancestry analyses, which identified 109 GWS loci, thus yielding a total of 113 GWS loci (30 novel) in at least one of these analyses. We observed improvements in the fine-mapping resolution at many susceptibility loci. Our results provide several lines of evidence supporting candidate genes at many loci and highlight some pathways for further research. Together, our findings provide novel insight into the genetic architecture and biological etiology of schizophrenia.

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Acknowledgements

We thank all of the participants in the study and the international Psychiatric GWAS Consortium (PGC) for the large-scale data resources that made this research possible. We also appreciate H. Huang, B. Neale and M. Daly for their valuable suggestions for data analysis and manuscript organization. This work was supported by the 973 Program (2015CB559100 to Y.S.), the National Key R&D Program of China (2016YFC0903402 to Y.S. and Z.L., and 2016YFC1201701 to X.L.), the Natural Science Foundation of China (31325014 to Y.S., 81130022 to Y.S., 81421061 to L.H. and 81701321 to Z.L.), the Program of Shanghai Subject Chief Scientist (15XD1502200 to Y.S.), the National Program for Support of Top-Notch Young Professionals to Y.S., the Shanghai Key Laboratory of Psychotic Disorders (13dz2260500 to Y.X.), the 'Shu Guang' project supported by the Shanghai Municipal Education Commission and Shanghai Education Development Foundation (12SG17 to Y.S.), the China Postdoctoral Science Foundation (2016M590615 to Z.L.), the Shandong Postdoctoral Innovation Foundation (201601015 to Z.L.), the Qingdao Postdoctoral Application Research Project (2016048 to Z.L.), the Shanghai Hospital Development Center (SHDC12016115 to Y.X.), the US NIMH and NIDA (U01 MH109528 to P.F.S. and U01 MH1095320 to P.F.S.), and the Swedish Research Council (Vetenskapsrådet, award D0886501 to P.F.S.).

Author information

Author notes

    • Zhiqiang Li
    •  & Jianhua Chen

    These authors contributed equally to this work.

Affiliations

  1. Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China.

    • Zhiqiang Li
    •  & Yongyong Shi
  2. Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China.

    • Zhiqiang Li
    • , Jianhua Chen
    • , Lin He
    • , Xingwang Li
    • , Jiawei Shen
    • , Zhijian Song
    • , Meng Wang
    • , Juan Zhou
    • , Boyu Chen
    • , Yahui Liu
    • , Jiqiang Wang
    • , Qingzhong Wang
    • , Baojie Li
    • , Guang He
    • , Weidong Li
    • , Chunling Wan
    • , Wenjin Li
    • , Zujia Wen
    • , Ke Liu
    • , Fang Huang
    • , Jue Ji
    •  & Yongyong Shi
  3. Institute of Social Cognitive and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, China.

    • Zhiqiang Li
    •  & Yongyong Shi
  4. Institute of Neuropsychiatric Science and Systems Biological Medicine, Shanghai Jiao Tong University, Shanghai, China.

    • Zhiqiang Li
    • , Lin He
    • , Weidong Ji
    •  & Yongyong Shi
  5. Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

    • Jianhua Chen
    • , Yifeng Xu
    •  & Guoyin Feng
  6. Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China.

    • Hao Yu
    • , Dai Zhang
    •  & Weihua Yue
  7. Institute of Mental Health, Sixth Hospital, Peking University, Beijing, China.

    • Hao Yu
    • , Dai Zhang
    •  & Weihua Yue
  8. Department of Psychiatry, Jining Medical University, Jining, China.

    • Hao Yu
  9. Peking-Tsinghua Joint Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.

    • Dai Zhang
  10. Department of Psychiatry, First Teaching Hospital of Xinjiang Medical University, Urumqi, China.

    • Qizhong Yi
    •  & Yongyong Shi
  11. Shandong Provincial Key Laboratory of Metabolic Disease and Metabolic Disease Institute of Qingdao University, Qingdao, China.

    • Changgui Li
  12. Changning Mental Health Center, Shanghai, China.

    • Weidong Ji
    •  & Yongyong Shi
  13. Wuhu Fourth People's Hospital, Wuhu, China.

    • Peng Wang
    •  & Ping Yang
  14. Longquan Mountain Hospital of Guangxi Province, Liuzhou, China.

    • Benxiu Liu
    •  & Wensheng Sun
  15. National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

    • Qi Xu
  16. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Stephan Ripke
  17. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Stephan Ripke
  18. Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin, Berlin, Germany.

    • Stephan Ripke
  19. Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA.

    • Patrick F Sullivan
  20. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

    • Patrick F Sullivan
  21. MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.

    • Michael C O'Donovan

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Contributions

Y.S. conceived and designed the experiments, and supervised all aspects of the work; J.C., Y.X., L.H., D.Z., W.Y., P.W., P.Y., B. Liu, W.S., Q.X., W.J., G.F., Q.Y., C.L. and X.L. performed sample collection and phenotyping; J.C., H.Y., J.Z., B.C., Y.L., J.W., J.J., M.W., Q.W., Z.W., Wenjin Li, K.L., F.H., J.Z., G.H., Weidong Li, C.W. and B. Li performed the experiments and data management; Z.L., H.Y., Z.S., J.S., S.R., P.F.S. and M.C.O'D. performed bioinformatics and statistical analyses; Y.S. and Z.L. interpreted the main findings; Y.S. and Z.L. drafted the manuscript; Y.S., L.H., Z.L., Y.X., X.L. and P.F.S. obtained the funding support; all authors revised and approved the final manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Yongyong Shi.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–7 and Supplementary Tables 1 and 7

  2. 2.

    Life Sciences Reporting Summary

  3. 3.

    Supplementary Data 1

    Regional plots of the GWS loci from the Chinese and PGC2 metaanalysis–log10 P values are shown for SNPs for the region 500 kb on either side of the marker SNPs. The index SNP is shown in purple, and the r2 values of the other SNPs are indicated by color. The r2 values are established based on the 1000 Genome data (Nov2014). The genes within the relevant regions are annotated and shown as arrows.

Excel files

  1. 1.

    Supplementary Table 2

    Results for the independent variants in 104 GWS regions in the PGC2 and Chinese GWAS meta-analysisGenomic position was based on the UCSC hg19/NCBI Build 37. OR=odds ratio for A1 and SE=standard error. I2 index represents the degree of heterogeneity. The previously reported schizophrenia associated variants were extracted from the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas, as to Jan. 2017). The r2 values between the index SNPs and previously reported associated variants were calculated based on 1000 Genome Project dataset.

  2. 2.

    Supplementary Table 3

    Results for the Chinese GWAS and replicationThe genome-wide significant SNPs in the meta-analysis were indicated as bold. Genomic position was based on the UCSC hg19/NCBI Build 37. OR=odds ratio for A1 and SE=standard error.

  3. 3.

    Supplementary Table 4

    Results for meta-analysis of the Chinese and PGC2 samplesThe genome-wide significant SNPs in the meta-analysis were indicated as bold. Genomic position was based on the UCSC hg19/NCBI Build 37. OR=odds ratio for A1 and SE=standard error. I2 index represents the degree of heterogeneity. a The SNPs were unavailable in the Chinese Replication data set.

  4. 4.

    Supplementary Table 5

    GWS schizophrenia loci in this studyGenomic position was based on the UCSC hg19/NCBI Build 37. OR=odds ratio for A1 and SE=standard error. The results are based on the meta-analysis of Chinese GWAS and replication samples (CHN) or all samples (ALL), which were shown in the 'Analysis' column. a The SNPs also reached GWS in the meta-analysis of Chinese GWAS and replication samples. b The LD results were based on the 1000Genome Project (European or Chinese samples). c eMHC, the extended major histocompatibility complex region.

  5. 5.

    Supplementary Table 6

    Results of the meta-analysis of the Chinese and PGC2 samples for the index SNPs or their proxies identified in the PGC2 reportThe ID for Loci and SNP is from the PGC2 report (Nature 511, 421–427, 2014). The SNP followed by (P) indicated a proxy of the index SNP. Genomic position was based on the UCSC hg19/NCBI Build 37. OR=odds ratio for A1 and SE=standard error. I2 index represents the degree of heterogeneity. PGC2+Chinese (Fixed) are the results under a fixed-effects model meta-analysis.

  6. 6.

    Supplementary Table 8

    GWS schizophrenia loci and notable genesGenomic position was based on the UCSC hg19/NCBI Build 37. a Notable genes from gene nearest to the index SNP (N); Schizophrenia-associated variant is in strong LD (r2 ≥ 0.8) with a missense variant in the indicated gene (M); genes prioritized by DEPICT (D); genes for which the mRNA levels showed cis-genetic linkage with the index SNPs (Q); genes prioritized by SMR analysis (S).

  7. 7.

    Supplementary Table 9

    The fine-mapping regions for schizophrenia GWS loci in different data sets.

  8. 8.

    Supplementary Table 10

    Results for fine-mapping analysis using PAINTOR

  9. 9.

    Supplementary Table 11

    Annotations for the 16 SNPs with a posterior probability of greater than 0.80 only in the trans-ethnic analysis

  10. 10.

    Supplementary Table 12

    eQTL analysis of rs3814883

  11. 11.

    Supplementary Table 13

    The top 100 enriched cell-type specific epigenomic annotations for schizophrenia associations in the current and PGC2 analysesEID, the epigenome identifier in the Roadmap Epigenomics Project. Descriptions for cell and tissue types, related groups, and marks at the Roadmap Epigenomics Project website (http://www.roadmapepigenomics.org). P value, enrichment P for schizophrenia associations derived from GREGOR.

  12. 12.

    Supplementary Table 14

    The top ranked SNPs with higher posterior probability in the further PAINTOR analyses with the cell-type specific epigenomic annotationsa EID, the epigenome identifier in the Roadmap Epigenomics Project. Descriptions for cell and tissue types, related groups, and marks at the Roadmap Epigenomics Project website (http://www.roadmapepigenomics.org).

  13. 13.

    Supplementary Table 15

    Top 30 significantly enriched pathways and gene sets in the cross-ethnic meta-analysisNGENES denotes the number of genes in pathway (number of genes successfully mapped by MAGMA).