Transethnic Genetic-Correlation Estimates from Summary Statistics

Abstract

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.

Figure 1

True and Estimated Genetic-Impact and Genetic-Effect Correlations All simulations were conducted with a simulated EUR and EAS heritability of 0.5 with 4,499 simulated EUR and 4,836 simulated EAS individuals at 248,953 SNPs.

Figure 2

The Distributions of the Estimates of Genetic Correlation Computed with Popcorn and GCTA Are Compared The distribution was computed via Gaussian kernel density estimation on the set of genetic-correlation estimates.

Figure 3

Genetic Correlation as a Function of Heritability for Gene Expression The mean and SE of the genetic correlation of the set of genes with $h_1^2$ and $h_2^2$ exceeding threshold h in each analysis (y axis) are plotted against h (x axis).