Abstract
Some candidate genes have been robustly reported to be associated with complex traits, such as the fat mass and obesity-associated (FTO) gene on body mass index (BMI), and the fibroblast growth factor 5 (FGF5) 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 p-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 FTO gene on four obesity measures: BMI (p = 0.0009), body fat percentage (p = 0.0031), waist circumference (p = 0.0052), and hip circumference (p = 0.0001). As another example, we used RIDGE and found the FGF5 gene has a stronger effect on blood pressure in Han Chinese with a higher waist-to-hip ratio [p = 0.0013 for diastolic blood pressure (DBP) and p = 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.
Highlights
The detection of “gene-environment interactions” (G × E) is important and is even more challenging than the detection of main effects of genes (Greenland, 1983; Hunter, 2005; Aschard, 2016)
We compared the power of the 4 genetic risk score” (GRS)-based tests and 2 G × E methods: interaction sequence kernel association test” (iSKAT) (Lin et al, 2016) and adaptive combination of Bayes factors method” (ADABF) (Lin et al, 2019)
Set-based G × E tests have been evaluated in a genome-wide context, very few genes have been found to interact with some exposures at the genome-wide significance level
Summary
The detection of “gene-environment interactions” (G × E) is important and is even more challenging than the detection of main effects of genes (Greenland, 1983; Hunter, 2005; Aschard, 2016). Some gene-based G × E methods have been developed (Jiao et al, 2013; Lin et al, 2013; Chen et al., 2014; Lin et al, 2016; Lin et al, 2019), very few G × E findings have reached the genome-wide significance level Physical activity has been found to attenuate the influence of the fat mass and obesityassociated (FTO) gene on obesity risk (Vimaleswaran et al, 2009; Kilpelainen et al, 2011). This means that the association of the FTO risk alleles with obesity measures is weaker in physically active subjects than in physically inactive subjects
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