Statistical methods for gene–environment interaction analysis

Abstract

Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene–environment interactions (G × E). In this review, we present state‐of‐the‐art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single‐variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.This article is categorized under: Applications of Computational Statistics > Genomics/Proteomics/Genetics Data: Types and Structure > Massive Data Statistical Models > Linear Models

Time‐dependent gene–environment interactions are essential drivers of asthma initiation and persistence

“…in the future, large-scale GWAS meta-analyses on gene-environment interactions (as we now approach genotype-phenotype associations) with global accessibility of the statistical results will increase our understanding in this field and revolutionize the way we approach our asthma patients. 102 Globalization of gathered genetic data increases their validity by allowing replication of the results across diverse ancestry groups. The inclusion of reproducibility has always been regarded as an essential component of the scientific method.…”