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

  • Author(s): Frei, Oleksandr
  • Holland, Dominic
  • Smeland, Olav B
  • Shadrin, Alexey A
  • Fan, Chun Chieh
  • Maeland, Steffen
  • O'Connell, Kevin S
  • Wang, Yunpeng
  • Djurovic, Srdjan
  • Thompson, Wesley K
  • Andreassen, Ole A
  • Dale, Anders M
  • et al.
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.

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