Miao Jiacheng, Wu Yixuan, Lu Qiongshi
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
University of Wisconsin-Madison, Madison, Wisconsin, USA.
Wiley Interdiscip Rev Comput Stat. 2024 Jan-Feb;16(1). doi: 10.1002/wics.1635. Epub 2023 Oct 5.
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.
大多数人类复杂表型是由多种遗传和环境因素及其相互作用导致的。了解遗传和环境因素相互作用的机制，可为复杂性状的遗传结构提供有价值的见解，并在推进精准医学方面具有巨大潜力。大型人群生物样本库的出现推动了众多旨在识别基因-环境相互作用（G×E）的统计方法的发展。在本综述中，我们介绍了用于G×E分析的最新统计方法。我们将概述单变量G×E定位的一系列方法，随后介绍多基因G×E分析的各种技术。我们通过讨论G×E研究的未来方向和挑战来结束本综述。
