Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA.
Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA.
Am J Hum Genet. 2020 Jan 2;106(1):71-91. doi: 10.1016/j.ajhg.2019.11.015.
Gene-environment interactions (GxE) can be fundamental in applications ranging from functional genomics to precision medicine and is a conjectured source of substantial heritability. However, unbiased methods to profile GxE genome-wide are nascent and, as we show, cannot accommodate general environment variables, modest sample sizes, heterogeneous noise, and binary traits. To address this gap, we propose a simple, unifying mixed model for gene-environment interaction (GxEMM). In simulations and theory, we show that GxEMM can dramatically improve estimates and eliminate false positives when the assumptions of existing methods fail. We apply GxEMM to a range of human and model organism datasets and find broad evidence of context-specific genetic effects, including GxSex, GxAdversity, and GxDisease interactions across thousands of clinical and molecular phenotypes. Overall, GxEMM is broadly applicable for testing and quantifying polygenic interactions, which can be useful for explaining heritability and invaluable for determining biologically relevant environments.
基因-环境相互作用（GxE）在功能基因组学到精准医学等领域的应用中具有重要意义，是大量遗传的推测来源。然而，用于全基因组分析 GxE 的无偏方法还处于起步阶段，并且正如我们所展示的，无法适应一般环境变量、适度的样本量、异质噪声和二元特征。为了解决这一差距，我们提出了一种简单、统一的基因-环境相互作用混合模型（GxEMM）。在模拟和理论中，我们表明，当现有方法的假设不成立时，GxEMM 可以显著提高估计值并消除假阳性。我们将 GxEMM 应用于一系列人类和模式生物数据集，并发现了广泛的证据，证明了特定于上下文的遗传效应，包括数千种临床和分子表型中的 GxSex、GxAdversity 和 GxDisease 相互作用。总的来说，GxEMM 广泛适用于测试和量化多基因相互作用，这对于解释遗传率非常有用，对于确定生物学上相关的环境也非常宝贵。
