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Am J Hum Genet. 2016 Jul 7;99(1):76-88. doi: 10.1016/j.ajhg.2016.05.001. Epub 2016 Jun 16.

Transethnic Genetic-Correlation Estimates from Summary Statistics.

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Department of Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA. Electronic address:
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94117, USA.
Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA.
Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA.


The increasing number of genetic association studies conducted in multiple populations provides an 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 have developed a method for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations. This methods takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. 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 applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the genetic correlation was significantly less than 1. Our method is implemented in a Python package called Popcorn.

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