Durvasula Arun, Price Alkes L
Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Genetics, Harvard Medical School, Cambridge, MA, USA; Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Am J Hum Genet. 2025 Mar 6;112(3):644-658. doi: 10.1016/j.ajhg.2025.01.014. Epub 2025 Feb 17.
The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and environmental (E) variable. First, we detect locus-specific GxE interaction by testing for genetic correlation (r) < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRSs) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP heritability across E bins. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average n = 325,000) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r significantly <1 (false discovery rate < 5%); 28 trait-E pairs with significant PRSxE and significant SNP heritability differences across E bins; and 15 trait-E pairs with significant PRSxE but no SNP heritability differences across E bins. Across the three scenarios, eight of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of these scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait variance.
基因 - 环境（GxE）相互作用在疾病和复杂性状结构中的作用已被广泛假设，但目前尚不清楚。在这里，我们应用三种统计方法来量化和区分给定性状和环境（E）变量的三种不同类型的GxE相互作用。首先，我们通过测试跨E区间的遗传相关性（r）<1来检测位点特异性GxE相互作用。其次，我们通过利用多基因风险评分（PRS）来检测E变量对遗传方差的全基因组效应，以在PRS、E和PRSxE对表型的回归中测试显著的PRSxE，以及跨E区间的SNP遗传力差异。第三，我们通过测试显著的PRSxE且跨E区间的SNP遗传力无差异，来检测遗传和环境效应作为E变量函数的全基因组比例放大。我们将我们的框架应用于33个英国生物银行性状（25个定量性状和8种疾病；平均n = 325,000）和10个涵盖生活方式、饮食和其他环境暴露的E变量。首先，我们确定了19个性状 - E对，其r显著<1（错误发现率<5%）；28个性状 - E对具有显著的PRSxE且跨E区间的SNP遗传力有显著差异；以及15个性状 - E对具有显著的PRSxE但跨E区间的SNP遗传力无差异。在这三种情况下，有8个性状 - E对涉及疾病性状，其解释因尺度效应而复杂化。使用生物性别作为E变量的分析在每种情况下都产生了额外的显著发现。总体而言，我们推断GxE和Gx性别效应对复杂性状方差有显著贡献。
