Estimating trans-ancestry genetic correlation with unbalanced data resources

TL;DR

This paper introduces a new method for estimating trans-ancestry genetic correlations in GWAS data, effectively handling unbalanced sample sizes and heterogeneity across populations, with validated results on UK Biobank data.

Contribution

A novel estimator that corrects for prediction errors and accommodates unbalanced GWAS data across different populations, improving trans-ancestry genetic correlation estimates.

Findings

Reliable estimates across 30 complex traits

Effective handling of unbalanced GWAS sample sizes

Insights into genetic transferability across populations

Abstract

The aim of this paper is to propose a novel estimation method of using genetic-predicted observations to estimate trans-ancestry genetic correlations, which describes how genetic architecture of complex traits varies among populations, in genome-wide association studies (GWAS). Our new estimator corrects for prediction errors caused by high-dimensional weak GWAS signals, while addressing the heterogeneity of GWAS data across ethnicities, such as linkage disequilibrium (LD) differences, which can lead to biased findings in homogeneity-agnostic analyses. Moreover, our estimator only requires one population to have a large GWAS sample size, and the second population can only have a much smaller number of participants (for example, hundreds). It is designed to specifically address the unbalanced data resources such that the GWAS sample size for European populations is usually larger than that of non-European ancestry groups. Extensive simulations and real data analyses of 30 complex traits in the UK Biobank study show that our method is capable of providing reliable estimates of a wide range of complex traits. Our results provide deep insights into the transferability of population-specific genetic findings.