American Journal of Human Genetics ( IF 8.1 ) Pub Date : 2020-09-03 , DOI: 10.1016/j.ajhg.2020.08.009 Matthew Kerin 1 , Jonathan Marchini 2
The contribution of gene-by-environment (GxE) interactions for many human traits and diseases is poorly characterized. We propose a Bayesian whole-genome regression model for joint modeling of main genetic effects and GxE interactions in large-scale datasets, such as the UK Biobank, where many environmental variables have been measured. The method is called LEMMA (Linear Environment Mixed Model Analysis) and estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome. The ES provides a readily interpretable way to examine the combined effect of many environmental variables. The ES can be used both to estimate the proportion of phenotypic variance attributable to GxE effects and to test for GxE effects at genetic variants across the genome. GxE effects can induce heteroskedasticity in quantitative traits, and LEMMA accounts for this by using robust standard error estimates when testing for GxE effects. When applied to body mass index, systolic blood pressure, diastolic blood pressure, and pulse pressure in the UK Biobank, we estimate that , , , and , respectively, of phenotypic variance is explained by GxE interactions and that low-frequency variants explain most of this variance. We also identify three loci that interact with the estimated environmental scores ().
中文翻译：
用贝叶斯全基因组回归模型推断基因与环境的相互作用。
基因与环境（GxE）相互作用对许多人类特征和疾病的影响尚不清楚。我们提出了一种贝叶斯全基因组回归模型，用于对大型数据集中的主要遗传效应和 GxE 相互作用进行联合建模，例如英国生物银行，其中测量了许多环境变量。该方法称为 LEMMA（线性环境混合模型分析），估计环境变量的线性组合，称为环境评分 (ES)，它与整个基因组的遗传标记相互作用。 ES 提供了一种易于解释的方法来检查许多环境变量的综合影响。 ES 既可用于估计 GxE 效应引起的表型变异的比例，也可用于测试整个基因组中遗传变异的 GxE 效应。 GxE 效应可以引起数量性状的异方差性，LEMMA 在测试 GxE 效应时通过使用稳健的标准误差估计来解释这一点。当应用于英国生物银行的体重指数、收缩压、舒张压和脉压时，我们估计： , , ， 和表型方差的分别由 GxE 相互作用解释，而低频变异解释了大部分方差。 我们还确定了与估计环境得分相互作用的三个基因座（ ）。
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