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

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 and Anders M. Dale ()
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Oleksandr Frei: University of Oslo
Dominic Holland: University of California at San Diego
Olav B. Smeland: University of Oslo
Alexey A. Shadrin: University of Oslo
Chun Chieh Fan: University of California at San Diego
Steffen Maeland: University of Oslo
Kevin S. O’Connell: University of Oslo
Yunpeng Wang: University of Oslo
Srdjan Djurovic: Oslo University Hospital
Wesley K. Thompson: Department of Family Medicine and Public Health, University of California, San Diego
Ole A. Andreassen: University of Oslo
Anders M. Dale: University of California at San Diego

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-10310-0

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