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Statistical methods for gene-environment interaction analysis
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DOI：10.1002/wics.1635.png)
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Abstract
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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 x E). In this review, we present state-of-the-art statistical methodologies for G x E analysis. We will survey a spectrum of approaches for single-variant G x E mapping, followed by various techniques for polygenic G x E analysis. We conclude this review with a discussion on the future directions and challenges in G x E research.This article is categorized under: Applications of Computational Statistics > Genomics/Proteomics/Genetics Data: Types and Structure > Massive Data Statistical Models > Linear Models
Keywords:
gene-environment interaction (G x E)
precision medicine
statistical genetics
Journal
W
IF:
5.4
Papers:
201
Citations:
5.1K
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Cited Papers
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