Genetic association meta-analysis is susceptible to confounding by between-study cryptic relatedness.

Meta-analysis of Genome-Wide Association Studies (GWAS) has important advantages, but it assumes that studies are independent, which does not hold when there is relatedness between studies. As a motivating example, recent work suggested applying sex-stratified meta-analysis to correct for participation bias, without considering that men and women from the same population will be highly related. Our theory demonstrates how cryptic relatedness results in correlated test statistics between studies, inflating meta-analysis. We characterize the effects of different between-study relatedness scenarios, particularly population structure and recent family relatedness, on meta-analysis type I error control and power. We simulated data with (1) no family relatedness between subpopulations, (2) family relatedness within subpopulations, (3) family relatedness across subpopulations, and (4) single population with family relatedness. We run joint and meta-analyses on simulations using both binary and quantitative traits. In scenarios with family relatedness, sex-stratified meta-analysis exhibits severe inflation and lower AUC compared to joint and subpopulation meta-analyses. Remarkably, genomic control succeeds in correcting inflation in these cases, but does not alter calibrated power. Analysis of real datasets confirms severe inflation for sex-stratified meta-analysis in family studies, but a negligible effect for population studies with up to 10,000 individuals. Our theoretical framework demonstrates that the inflation factor increases as the sample size increases. We recommend against meta-analyzing studies that share the same populations, which increases the risk of inflation due to cryptic relatedness between studies.