Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies.

IF 1.1 4区 生物学 Q4 GENETICS & HEREDITY Human Heredity Pub Date : 2019-01-01 Epub Date: 2020-05-16 DOI:10.1159/000506008
Jianjun Zhang, Qiuying Sha, Han Hao, Shuanglin Zhang, Xiaoyi Raymond Gao, Xuexia Wang
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Abstract

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.

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在测序关联研究中测试多个性状的基因-环境交互作用
动机许多复杂疾病的风险是由遗传和环境因素相互作用决定的。对多个性状的基因-环境相互作用(G×Es)进行检测,可以获得有关疾病病因学的宝贵见解,并提高检测疾病相关基因的能力。然而,测试多性状 G×Es 的方法非常有限:我们开发了在测序关联研究中测试多性状 G×E 的新方法。我们首先使用主成分分析或标准化分析对多个性状进行转换。然后,我们使用新提出的检验方法检测 G×Es 的影响:检测 G×Es 最佳加权组合(TOW-GE)和/或可变加权 TOW-GE(VW-TOW-GE)的影响。最后,我们采用费雪组合检验来合并 p 值:广泛的模拟研究表明,所提方法的 I 类错误率得到了很好的控制。与交互序列核关联检验(ISKAT)相比,TOW-GE 在只有罕见风险变异体和保护变异体的情况下更有效;VW-TOW-GE 在同时存在罕见变异体和常见变异体的情况下更有效。TOW-GE和VW-TOW-GE对因果G×E的影响方向都很稳健。在 COPDGene 研究中的应用表明,我们提出的方法非常有效:我们提出的方法是识别多性状 G×E 的有用工具。我们提出的方法不仅可用于识别常见变异的 G×E,也可用于识别罕见变异。因此,这些方法可用于全基因组关联研究和下一代测序数据分析中的 G×Es 鉴定。
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来源期刊
Human Heredity
Human Heredity 生物-遗传学
CiteScore
2.50
自引率
0.00%
发文量
12
审稿时长
>12 weeks
期刊介绍: Gathering original research reports and short communications from all over the world, ''Human Heredity'' is devoted to methodological and applied research on the genetics of human populations, association and linkage analysis, genetic mechanisms of disease, and new methods for statistical genetics, for example, analysis of rare variants and results from next generation sequencing. The value of this information to many branches of medicine is shown by the number of citations the journal receives in fields ranging from immunology and hematology to epidemiology and public health planning, and the fact that at least 50% of all ''Human Heredity'' papers are still cited more than 8 years after publication (according to ISI Journal Citation Reports). Special issues on methodological topics (such as ‘Consanguinity and Genomics’ in 2014; ‘Analyzing Rare Variants in Complex Diseases’ in 2012) or reviews of advances in particular fields (‘Genetic Diversity in European Populations: Evolutionary Evidence and Medical Implications’ in 2014; ‘Genes and the Environment in Obesity’ in 2013) are published every year. Renowned experts in the field are invited to contribute to these special issues.
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