Meta‐Analysis of Genome‐Wide Association Studies with Correlated Individuals: Application to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Investigators often meta‐analyze multiple genome‐wide association studies (GWASs) to increase the power to detect associations of single nucleotide polymorphisms (SNPs) with a trait. Meta‐analysis is also performed within a single cohort that is stratified by, e.g., sex or ancestry group. Having correlated individuals among the strata may complicate meta‐analyses, limit power, and inflate Type 1 error. For example, in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), sources of correlation include genetic relatedness, shared household, and shared community. We propose a novel mixed‐effect model for meta‐analysis, “MetaCor,” which accounts for correlation between stratum‐specific effect estimates. Simulations show that MetaCor controls inflation better than alternatives such as ignoring the correlation between the strata or analyzing all strata together in a “pooled” GWAS, especially with different minor allele frequencies (MAFs) between strata. We illustrate the benefits of MetaCor on two GWASs in the HCHS/SOL. Analysis of dental caries (tooth decay) stratified by ancestry group detected a genome‐wide significant SNP (rs7791001, P‐value = , compared to in pooled), with different MAFs between strata. Stratified analysis of body mass index (BMI) by ancestry group and sex reduced overall inflation from (pooled) to (MetaCor). Furthermore, even after removing close relatives to obtain nearly uncorrelated strata, a naïve stratified analysis resulted in compared to for MetaCor.