Vol. 12, Issue 5 (2023)

Author(s):
Ymberzal Koul and Gaurav Patel

Abstract:
This review article presents a comprehensive overview of meta-analysis methodologies and their applications in the context of Genome-Wide Association Studies (GWAS). Over the past decade, meta-analysis has emerged as a powerful tool for harnessing the collective power of multiple independent GWAS studies to unravel the genetic architecture underlying complex traits and diseases. Through the integration and analysis of summary statistics from diverse datasets, meta-analysis offers valuable insights into the identification of hidden loci, replication and validation of genetic associations, and the characterization of polygenic effects. In this article, we explore the fundamental principles of conducting meta-analyses in GWAS, including the importance of data harmonization, quality control, and addressing potential sources of heterogeneity. We discuss the various statistical methodologies commonly employed in meta-analysis, such as fixed-effects and random-effects models, as well as novel approaches for accounting for heterogeneity and identifying gene-gene interactions. Additionally, we highlight advancements in pathway and functional analysis, which help elucidate the biological mechanisms underlying the observed associations.