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.
medRxiv. 2024 Apr 18:2023.09.22.23295969. doi: 10.1101/2023.09.22.23295969.
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 E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) 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. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with significantly < 1 (FDR<5%) (average ); for example, white blood cell count had (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 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 the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.
基因-环境（GxE）相互作用在疾病和复杂性状结构中的作用被广泛假设，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区间的单核苷酸多态性遗传力差异。第三，我们通过测试无跨E区间单核苷酸多态性遗传力差异的显著PRSxE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。模拟表明，这些方法在区分这三种GxE情况时具有高灵敏度和特异性。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；平均值）和10个跨越生活方式、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，其显著小于1（错误发现率<5%）（平均值）；例如，吸烟者和非吸烟者之间的白细胞计数为（标准误0.01）。其次，我们确定了28个性状-E对，其具有显著的PRSxE和跨E区间的显著单核苷酸多态性遗传力差异；例如，身体质量指数（BMI）对身体活动具有显著的PRSxE（P=4.6×10⁻⁵），在身体活动量最大与最小的五分位数中，单核苷酸多态性遗传力大5%（P=7×10⁻⁴）。第三，我们确定了15个性状-E对，其具有显著的PRSxE且跨E区间无单核苷酸多态性遗传力差异；例如，经BMI调整的腰臀比对于看电视的时间具有显著的PRSxE效应（P=5×10⁻³），且无单核苷酸多态性遗传力差异。在这三种情况下，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变量的分析在这三种情况中的每一种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和疾病方差有显著贡献。
