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

Meta-analysis of genome-wide association studies (GWASs) 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 no family relatedness between subpopulations, family relatedness within subpopulations, family relatedness across subpopulations, and a single population with family relatedness. We evaluated joint GWAS, standard meta-analysis, and our proposed meta-analysis method for correlated studies (R package metalcor) on both binary and quantitative traits. In scenarios with family relatedness, standard sex-stratified meta-analysis exhibits severe inflation and lower area under the curve (AUC) than joint and subpopulation meta-analyses, which our method improves by modeling correlation. Genomic control also corrects for inflation but does not alter calibrated power and may fail under high power and high polygenicity. Inflation in standard meta-analysis increases with sample size, which our proposed method avoids. Analysis of real datasets confirms severe inflation for standard sex-stratified meta-analysis in family studies but a negligible effect for population studies with up to 10,000 individuals. Meta-analyses of studies of the same population have increased risk of between-study cryptic relatedness and should be avoided.