PT  - JOURNAL ARTICLE
AU  - Daniela Zanetti
AU  - Michael E. Weale
TI  - True causal effect size heterogeneity is not required to explain trans-ethnic differences in GWAS signals
AID  - 10.1101/085092
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 085092
4099  - http://biorxiv.org/content/early/2016/11/03/085092.short
4100  - http://biorxiv.org/content/early/2016/11/03/085092.full
AB  - Through genome-wide association studies (GWASs), researchers have identified hundreds of genetic variants associated with particular complex traits. Previous studies have compared the pattern of association signals across different populations in real data, and these have detected differences in the strength and sometimes even the direction of GWAS signals. These differences could be due to a combination of (1) lack of power (insufficient sample sizes); (2) minor allele frequency (MAF) differences (again affecting power); (3) linkage disequilibrium (LD) differences (affecting power to ‘tag’ the causal variant); and (4) true differences in causal variant effect sizes (defined by relative risks).In the present work, we sought to assess whether the first three of these reasons are sufficient on their own to explain the observed incidence of trans-ethnic differences in replications of GWAS signals, or whether the fourth reason is also required. We simulated case-control data of European, Asian and African ancestry, drawing on observed MAF and LD patterns seen in the 1000-Genomes reference dataset and assuming the true causal relative risks were the same in all three populations.We found that a combination of Euro-centric SNP selection and between-population differences in LD, accentuated by the lower SNP density typical of older GWAS panels, was sufficient to explain the rate of trans-ethnic differences previously reported, without the need to assume between-population differences in true causal SNP effect size. This suggests a cross-population consistency that has implications for our understanding of the interplay between genetics and environment in the aetiology of complex human diseases.