Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution.

Reedik Mägi,Nora Franceschini,Momoko Horikoshi,Tamar Sofer,Hidetoshi Kitajima,Andrew P Morris,Anubha Mahajan,Mark I Mccarthy

doi:10.1093/hmg/ddx280

Abstract

Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals.

Highlights

There is increasing evidence from genome-wide association studies (GWAS) that common SNPs driving complex human trait associations are shared across diverse populations [1,2], and that alleles at these signals demonstrate concordant directions of effect across ethnicities [3]
We have developed a novel approach to aggregate association summary statistics across GWAS from diverse populations to account for heterogeneity in allelic effects that is correlated with ancestry (Materials and Methods)
We have developed a novel approach to aggregating association summary statistics across GWAS from diverse populations through trans-ethnic meta-regression

Summary

Introduction

There is increasing evidence from genome-wide association studies (GWAS) that common SNPs driving complex human trait associations are shared across diverse populations [1,2], and that alleles at these signals demonstrate concordant directions of effect across ethnicities [3]. This observation is consistent with a model in which causal variants are shared across diverse populations, for which trans-ethnic metaanalysis offers an opportunity to increase power to detect novel loci through increased sample size. The quality of imputation might vary between populations, dependent on the reference panel used, leading to downward bias in allelic effect estimates within ethnic groups in which genotypes are less well predicted

Methods

Results

Conclusion