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

@inproceedings{Frei2019BivariateCM,
  title={Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation},
  author={Oleksandr Frei and Dominic Holland and Olav B Smeland and Alexey A. Shadrin and Chun Chieh Fan and Steffen Maeland and Kevin S O'Connell and Yunpeng Wang and Srdjan Djurovic and Wesley K. Thompson and Ole Andreas Andreassen and Anders M. Dale},
  booktitle={Nature communications},
  year={2019}
}
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… CONTINUE READING

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