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Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation

Author

Listed:
  • Oleksandr Frei

    (University of Oslo)

  • Dominic Holland

    (University of California at San Diego
    University of California, San Diego)

  • Olav B. Smeland

    (University of Oslo
    Oslo University Hospital)

  • Alexey A. Shadrin

    (University of Oslo)

  • Chun Chieh Fan

    (University of California at San Diego
    University of California at San Diego
    University of California, San Diego)

  • Steffen Maeland

    (University of Oslo)

  • Kevin S. O’Connell

    (University of Oslo)

  • Yunpeng Wang

    (University of Oslo
    University of California at San Diego
    University of California, San Diego)

  • Srdjan Djurovic

    (Oslo University Hospital
    University of Bergen)

  • Wesley K. Thompson

    (Department of Family Medicine and Public Health, University of California, San Diego
    Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Capital Region of Denmark)

  • Ole A. Andreassen

    (University of Oslo
    Oslo University Hospital)

  • Anders M. Dale

    (University of California at San Diego
    University of California, San Diego
    University of California, San Diego
    University of California, San Diego)

Abstract

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.

Suggested Citation

  • Oleksandr Frei & Dominic Holland & Olav B. Smeland & Alexey A. Shadrin & Chun Chieh Fan & Steffen Maeland & Kevin S. O’Connell & Yunpeng Wang & Srdjan Djurovic & Wesley K. Thompson & Ole A. Andreassen, 2019. "Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10310-0
    DOI: 10.1038/s41467-019-10310-0
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    Cited by:

    1. Morten Dybdahl Krebs & Gonçalo Espregueira Themudo & Michael Eriksen Benros & Ole Mors & Anders D. Børglum & David Hougaard & Preben Bo Mortensen & Merete Nordentoft & Michael J. Gandal & Chun Chieh F, 2021. "Associations between patterns in comorbid diagnostic trajectories of individuals with schizophrenia and etiological factors," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Mengge Liu & Lu Wang & Yujie Zhang & Haoyang Dong & Caihong Wang & Yayuan Chen & Qian Qian & Nannan Zhang & Shaoying Wang & Guoshu Zhao & Zhihui Zhang & Minghuan Lei & Sijia Wang & Qiyu Zhao & Feng Li, 2024. "Investigating the shared genetic architecture between depression and subcortical volumes," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Royce E. Clifford & Adam X. Maihofer & Chris Chatzinakos & Jonathan R. I. Coleman & Nikolaos P. Daskalakis & Marianna Gasperi & Kelleigh Hogan & Elizabeth A. Mikita & Murray B. Stein & Catherine Tchea, 2024. "Genetic architecture distinguishes tinnitus from hearing loss," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. Florin Ratajczak & Mitchell Joblin & Marcel Hildebrandt & Martin Ringsquandl & Pascal Falter-Braun & Matthias Heinig, 2023. "Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    5. Cheng, Yuanyuan, 2023. "A method of 3R to evaluate the correlation and predictive value of variables," OSF Preprints c79tu, Center for Open Science.

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