Estimating cross-population genetic correlations of causal effect sizes

Abstract

Recent studies have examined the genetic correlations of single-nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated ${\rho }_g$ , the cross-population correlation of joint-fit effect sizes at genotyped SNPs. However, the value of ${\rho }_g$ depends both on the cross-population correlation of true causal effect sizes ( ${\rho }_b$ ) and on the similarity in linkage disequilibrium (LD) patterns in the two populations, which drive tagging effects. Here, we derive the value of the ratio ${\rho }_g/{\rho }_b$ as a function of LD in each population. By applying existing methods to obtain estimates of ${\rho }_g$ , we can use this ratio to estimate ${\rho }_b$ . Our estimates of ${\rho }_b$ were equal to 0.55 ( SE = 0.14) between Europeans and East Asians averaged across nine traits in the Genetic Epidemiology Research on Adult Health and Aging data set, 0.54 ( SE = 0.18) between Europeans and South Asians averaged across 13 traits in the UK Biobank data set, and 0.48 ( SE = 0.06) and 0.65 ( SE = 0.09) between Europeans and East Asians in summary statistic data sets for type 2 diabetes and rheumatoid arthritis, respectively. These results implicate substantially different causal genetic architectures across continental populations.

Keywords: genetic architecture; genetic correlation; multiethnic.

Figure 1.. Estimates of ρ_b are accurate in simulations at various values of ρ_b.

We report estimates of ρ_b for each value of true ρ_b. Dashed line is y=x.

Figure 2.. Estimates of ρ_b are downward biased at very low values of h².

We report estimates of ρ_b for various values of h². The dashed line is the true value ρ_b =0.8.