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Transethnic genetic correlation estimates from summary statistics

By Brielin C. Brown, Asian Genetic Epidemiology Network-Type 2 Diabetes (AGEN-T2G) Consortium, Chun Jimmie Ye, Alkes Price, Noah Zaitlen

Posted 14 Jan 2016
bioRxiv DOI: 10.1101/036657 (published DOI: 10.1016/j.ajhg.2016.05.001)

The increasing number of genetic association studies conducted in multiple populations provides unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics. Here we develop a method for estimating the transethnic genetic correlation: the correlation of causal variant effect sizes at SNPs common in populations. We take advantage of the entire spectrum of SNP associations and use only summary-level GWAS data. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We apply our method to gene expression, rheumatoid arthritis, and type-two diabetes data and overwhelmingly find that the genetic correlation is significantly less than 1. Our method is implemented in a python package called popcorn.

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