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.

中文翻译：

---

一种稳健的方法揭示了各种复杂性状中显着的特定背景遗传力。


基因-环境相互作用 (GxE) 是从功能基因组学到精准医学等应用的基础，并且是遗传性的重要来源。然而，在全基因组范围内分析 GxE 的无偏方法还处于萌芽状态，并且正如我们所表明的，无法适应一般环境变量、适度的样本量、异质噪声和二元性状。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，当现有方法的假设失败时，GxEMM 可以显着改进估计并消除误报。我们将 GxEMM 应用于一系列人类和模型生物数据集，并找到了特定背景遗传效应的广泛证据，包括数千种临床和分子表型中的 GxSex、GxAdversity 和 GxDisease 相互作用。总体而言，GxEMM 广泛适用于测试和量化多基因相互作用，这对于解释遗传性非常有用，并且对于确定生物学相关环境非常有价值。