A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits
A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits
复制标题
DOI：
10.1016/j.ajhg.2019.11.015
复制
发表时间：
2020-01-02
影响因子：
9.8
通讯作者：
Zaitlen, Noah
中科院分区：
文献类型：
--
作者：
Dahl, Andy;Khiem Nguyen;Zaitlen, Noah
基因-环境相互作用（GxE）在从功能基因组学到精确医学的应用中是基础性的，并且是大量遗传性的可靠来源。然而，公正的方法来配置文件GxE全基因组是新生的，正如我们所展示的，不能适应一般的环境变量，适度的样本量，异质性噪声和二进制性状。为了解决这一差距，我们提出了一个简单、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，GxEMM可以显着提高估计和消除误报时，现有方法的假设失败。我们将GxEMM应用于一系列人类和模式生物数据集，并发现了背景特异性遗传效应的广泛证据，包括GxSex，GxAdversity和GxDisease在数千种临床和分子表型中的相互作用。总体而言，GxEMM广泛适用于测试和量化多基因相互作用，这对于解释遗传性和确定生物相关环境非常有用。
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.