Identifying systematic heterogeneity patterns in genetic association meta-analysis studies

Progress in mapping loci associated with common complex diseases or quantitative inherited traits has been expedited by large-scale meta-analyses combining information across multiple studies, assembled through collaborative networks of researchers. Participating studies will usually have been independently designed and implemented in unique settings that are potential sources of phenotype, ancestry or other variability that could introduce between-study heterogeneity into a meta-analysis. Heterogeneity tests based on individual genetic variants (e.g. Q, I2) are not suited to identifying locus-specific from more systematic multi-locus or genome-wide patterns of heterogeneity. We have developed and evaluated an aggregate heterogeneity M statistic that combines between-study heterogeneity information across multiple genetic variants, to reveal systematic patterns of heterogeneity that elude conventional single variant analysis. Application to a GWAS meta-analysis of coronary disease with 48 contributing studies uncovered substantial systematic between-study heterogeneity, which could be partly explained by age-of-disease onset, family-history of disease and ancestry. Future meta-analyses of diseases and traits with multiple known genetic associations can use this approach to identify outlier studies and thereby optimize power to detect novel genetic associations.Author summary: Meta-analysis of genome-wide association studies (GWAS) is a valuable tool for the discovery of genes that protect or predispose individuals to common complex diseases. It can though be hampered by excessive heterogeneity among its participating studies. To date, the impact of heterogeneity is assessed locally on an individual SNP basis using Q, I2 and τ2 statistics. Here, we present a new heterogeneity statistic, M that assesses genomic (multi-SNP) patterns of heterogeneity in GWAS meta-analysis with enhanced power compared to conventional methods. When applied to a recent GWAS meta-analysis of coronary artery disease, the new statistic revealed substantial patterns of systematic heterogeneity, much of which was attributed to differences in ancestry, age-of—disease onset and family history of disease. The new method can dissect genomic heterogeneity patterns to flag underperforming studies that could comprise the power of the meta-analysis as well as identify influential studies with advantageous design features to inform future meta-analyses of multifactorial disease.