Lin Wan-Yu, Lin Yu-Shun, Chan Chang-Chuan, Liu Yu-Li, Tsai Shih-Jen, Kuo Po-Hsiu
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Front Genet. 2020 May 8;11:331. doi: 10.3389/fgene.2020.00331. eCollection 2020.
Some candidate genes have been robustly reported to be associated with complex traits, such as the () gene on body mass index (BMI), and the () gene on blood pressure levels. It is of interest to know whether an environmental factor (E) can attenuate or exacerbate the adverse influence of a candidate gene. To this end, we here evaluate the performance of "genetic risk score" (GRS) approaches to detect "gene-environment interactions" (G × E). In the first stage, a GRS is calculated according to the genotypes of variants in a candidate gene. In the second stage, we test whether E can significantly modify this GRS effect. This two-stage procedure can not only provide a -value for a G × E test but also guide inferences on how E modifies the adverse effect of a gene. With systematic simulations, we compared several ways to construct a GRS. If E exacerbates the adverse influence of a gene, GRS formed by the elastic net (ENET) or the least absolute shrinkage and selection operator (LASSO) is recommended. However, the performance of ENET or LASSO will be compromised if E attenuates the adverse influence of a gene, and using the ridge regression (RIDGE) can be more powerful in this situation. Applying RIDGE to 18,424 subjects in the Taiwan Biobank, we showed that performing regular exercise can attenuate the adverse influence of the gene on four obesity measures: BMI ( = 0.0009), body fat percentage ( = 0.0031), waist circumference ( = 0.0052), and hip circumference ( = 0.0001). As another example, we used RIDGE and found the gene has a stronger effect on blood pressure in Han Chinese with a higher waist-to-hip ratio [ = 0.0013 for diastolic blood pressure (DBP) and = 0.0027 for systolic blood pressure (SBP)]. This study provides an evaluation on the GRS approaches, which is important to infer whether E attenuates or exacerbates the adverse influence of a candidate gene.
一些候选基因已被确凿报道与复杂性状相关，比如与体重指数（BMI）相关的（）基因，以及与血压水平相关的（）基因。了解环境因素（E）是否会减弱或加剧候选基因的不利影响很有意义。为此，我们在此评估“遗传风险评分”（GRS）方法检测“基因 - 环境相互作用”（G×E）的性能。在第一阶段，根据候选基因中变异的基因型计算GRS。在第二阶段，我们测试E是否能显著改变这种GRS效应。这个两阶段程序不仅能为G×E测试提供一个p值，还能指导关于E如何改变基因不利影响的推断。通过系统模拟，我们比较了几种构建GRS的方法。如果E加剧基因的不利影响，推荐使用弹性网络（ENET）或最小绝对收缩和选择算子（LASSO）构建GRS。然而，如果E减弱基因的不利影响，ENET或LASSO的性能会受到影响，在这种情况下使用岭回归（RIDGE）可能更有效。将RIDGE应用于台湾生物银行的18424名受试者，我们发现进行规律运动可以减弱（）基因对四种肥胖指标的不利影响：BMI（p = 0.0009）、体脂百分比（p = 0.0031）、腰围（p = 0.0052）和臀围（p = 0.0001）。再举一个例子，我们使用RIDGE发现，对于腰臀比更高的汉族人，（）基因对血压的影响更强[舒张压（DBP）的p = 0.0013，收缩压（SBP）的p = 0.0027]。本研究对GRS方法进行了评估，这对于推断E是否减弱或加剧候选基因的不利影响很重要。
