An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Abstract

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes produces a substantially better accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected nine independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis, and Peripheral Vascular Disease. The GERA cohort analysis suggests that CPBayes is more powerful than ASSET with respect to detecting independent pleiotropic variants. We provide an R-package CPBayes implementing the proposed method.\n\nAuthor SummaryGenome-wide association studies (GWASs) have highlighted shared genetic susceptibility to various human diseases (pleiotropy). We propose a Bayesian meta-analysis method CPBayes that simultaneously evaluates the evidence of aggregate-level pleiotropic association and selects an optimal subset of associated traits underlying a pleiotropic signal. CPBayes analyzes pleiotropy using summary-level data across a wide range of studies for two or more phenotypes - separate GWASs with or without shared subjects, cohort study for multiple traits. It performs a fully Bayesian analysis and offers various flexibilities in the inference. In addition to parameters of primary interest (e.g., the measures of overall pleiotropic association, the optimal subset of associated traits), it provides additional interesting insights into a pleiotropic signal (e.g., the trait-specific posterior probability of association, the credible interval of unknown true genetic effects). Using computer simulations and a real data application to the large Kaiser GERA cohort, we demonstrate that CPBayes offers substantially better accuracy while selecting the non-null traits compared to a well known subset-based meta analysis ASSET. In the GERA cohort analysis, CPBayes detected a larger number of independent pleiotropic variants than ASSET. We provide a user-friendly R-package CPBayes for general use.