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Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies.
Human Heredity ( IF 1.8 ) Pub Date : 2020-05-16 , DOI: 10.1159/000506008
Jianjun Zhang 1 , Qiuying Sha 2 , Han Hao 1 , Shuanglin Zhang 2 , Xiaoyi Raymond Gao 3, 4, 5 , Xuexia Wang 6
Affiliation  

Motivation: The risk of many complex diseases is determined by an interplay of genetic and environmental factors. The examination of gene-environment interactions (G×Es) for multiple traits can yield valuable insights about the etiology of the disease and increase power in detecting disease-associated genes. However, the methods for testing G×Es for multiple traits are very limited. Method: We developed novel approaches to test G×Es for multiple traits in sequencing association studies. We first perform a transformation of multiple traits by using either principal component analysis or standardization analysis. Then, we detect the effects of G×Es using novel proposed tests: testing the effect of an optimally weighted combination of G×Es (TOW-GE) and/or variable weight TOW-GE (VW-TOW-GE). Finally, we employ Fisher’s combination test to combine the p values. Results: Extensive simulation studies show that the type I error rates of the proposed methods are well controlled. Compared to the interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are only rare risk and protective variants; VW-TOW-GE is more powerful when there are both rare and common variants. Both TOW-GE and VW-TOW-GE are robust to directions of effects of causal G×Es. Application to the COPDGene Study demonstrates that our proposed methods are very effective. Conclusions: Our proposed methods are useful tools in the identification of G×Es for multiple traits. The proposed methods can be used not only to identify G×Es for common variants, but also for rare variants. Therefore, they can be employed in identifying G×Es in both genome-wide association studies and next-generation sequencing data analyses.
Hum Hered


中文翻译:

在测序关联研究中测试多种特征的基因-环境相互作用。

动机:许多复杂疾病的风险取决于遗传和环境因素的相互作用。对多种性状的基因-环境相互作用(G×Es)的检查可以提供有关疾病病因的宝贵见解,并提高检测疾病相关基因的能力。然而,测试G×E的多个性状的方法非常有限。方法:我们开发了新颖的方法来测试序列关联研究中多个性状的G×E。我们首先使用主成分分析或标准化分析对多个特征进行转换。然后,我们使用新提出的测试来检测G×E的效果:测试G×Es(TOW-GE)和/或可变权重TOW-GE(VW-TOW-GE)的最佳加权组合的效果。最后,我们采用Fisher组合检验来组合p值。结果:大量的仿真研究表明,所提出方法的I类错误率得到了很好的控制。与交互序列内核关联测试(ISKAT)相比,TOW-GE在仅具有罕见风险和保护性变异的情况下更强大。当有稀有和常见变体时,VW-TOW-GE会更强大。TOW-GE和VW-TOW-GE都对因果G×E的影响方向具有鲁棒性。在COPDGene研究中的应用表明,我们提出的方法非常有效。结论:我们提出的方法是用于鉴定多个性状的G×E的有用工具。所提出的方法不仅可以用于识别常见变体的G×E,而且还可以用于罕见变体。因此,它们可以在全基因组关联研究和下一代测序数据分析中用于鉴定G×E。
嗡嗡声
更新日期:2020-05-16
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