Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis - Chimusa, Emile R.; Defo , Joel (Frontiers in Genetics, 2022)
Over the past decades, advanced high-throughput technologies have continuously contributed to Genome-wide Association Studies (GWAS). GWAS meta-analysis has become increasingly an adopted and cross-ancestry replicability has power to illuminate the genetic architecture of complex traits, informing about the reliability of effect estimations and their variability across human ancestries. However, detecting genetic variants that have low disease risk still poses a challenge. Designing a meta-analysis approach that combines the effect of various SNPs within genes or genes within pathways from multiple independent population GWAS may be helpful in identifying associations with small effect sizes and increasing the association power. Here, we proposed a Bayesian graph-based framework to perform gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes, and genes within sub-network/pathways across multiple independent population GWAS to de-convolute the interactions between genes underlying the pathogenesis of complex diseases across human populations. We assessed the proposed framework on simulated datasets and results show that the proposed model holds promise for increasing statistical power for meta-analysis of genetic variants underlying the pathogenesis of complex diseases. To illustrate proposed meta-analysis framework, we leverage 7 different European bipolar disorder (BD) cohorts, and we identify variants in the Angiotensinogen AGT gene to be significantly associated with BD across all 7 studies. We detect a commonly significant BD specific sub-network with the ESR1 gene as the main hub of a sub-network, associated with Neurotrophin signalling (p = 4e-14) and Myometrial Relaxation and Contraction (p = 3e-08) pathways. ancMETA provides a new contribution to post-GWAS methodologies and holds promise for comprehensively examining interactions between genes underlying the pathogenesis of genetic diseases and also underlying ethnic differences..