Transethnic genetic correlation estimates from summary statistics
By Brielin C. Brown, Chun Jimmie Ye, Alkes L Price, Noah A. Zaitlen. 2016
321 citations recorded by the source corpus.
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.
Subfields: Genetics
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In Genetics
- Gonçalo R. Abecasis
- Christian Fuchsberger
- Cristen J. Willer
- P. van der Harst
- N. Verweij
- Michael Boehnke
- Jian Yang
- Joel Hirschhorn
Citations in the corpus
- The power of genetic diversity in genome-wide association studies of lipids (2021, methodology): “Of these 159 variants, 31 showed the largest effect in African ancestry analyses, 24 in East Asian, 67 in European, 20 in Hispanic and 17 in South Asian.”
- Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, methodology): “Use Popcorn or GREML with admixtureaware estimation of genetic relatedness.”