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Tests for Gene-Environment Interactions and Joint Effects with Exposure Misclassification

机译:基因-环境相互作用和联合效应的暴露分类错误测试

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摘要

The number of methods for genome-wide testing of gene-environment interactions (GEI) continues to increase with the hope of discovering new genetic risk factors and obtaining insight into the disease-gene-environment relationship. The relative performance of these methods based on family-wise type 1 error rate and power depends on underlying disease-gene-environment associations, estimates of which may be biased in the presence of exposure misclassification. This simulation study expands on a previously published simulation study of methods for detecting GEI by evaluating the impact of exposure misclassification. We consider seven single step and modular screening methods for identifying GEI at a genome-wide level and seven joint tests for genetic association and GEI, for which the goal is to discover new genetic susceptibility loci by leveraging GEI when present. In terms of statistical power, modular methods that screen based on the marginal disease-gene relationship are more robust to exposure misclassification. Joints tests that include main/marginal effects of a gene display a similar robustness, confirming results from earlier studies. Our results offer an increased understanding of the strengths and limitations of methods for genome-wide search for GEI and joint tests in presence of exposure misclassification. KEY WORDS: case-control; genome-wide association; gene discovery, gene-environment independence; modular methods; multiple testing; screening test; weighted hypothesis test. Abbreviations: CC, case-control; CC(EXP), CC in the exposed subgroup; CO, case-only; CT, cocktail; DF, degree of freedom; D-G, disease-gene; EB, empirical Bayes; EB(EXP), EB in the exposed subgroup; EDGxE, joint marginal/association screening; FWER, family-wise error rate; G-E, gene-environment; GEI, gene-environment interaction; GEWIS, Gene Environment Wide Interaction Study; H2, hybrid two-step; LR, likelihood ratio; MA, marginal; OR, odds ratio; SE, sensitivity; SP, specificity; TS, two-step gene-environment screening;
机译:希望发现新的遗传危险因素并获得对疾病-基因-环境关系的洞察力,用于基因组-环境相互作用(GEI)的全基因组测试的方法数量不断增加。这些基于家庭式1型错误率和功效的方法的相对性能取决于潜在的疾病-基因-环境关联,在暴露分类错误的情况下,其估计值可能会有所偏差。该模拟研究扩展了先前发布的通过评估暴露错误分类的影响来检测GEI的方法的模拟研究。我们考虑了七种用于在全基因组水平上识别GEI的单步和模块化筛选方法,以及七种针对遗传关联和GEI的联合测试,其目的是通过利用GEI来发现新的遗传易感基因座。在统计功效方面,基于边际疾病-基因关系进行筛选的模块化方法对于暴露分类错误更为有效。包括基因的主要/边际效应的关节测试显示出相似的鲁棒性,证实了早期研究的结果。我们的结果提供了对在暴露分类错误的情况下对全基因组搜索GEI和联合测试的方法的优势和局限性的进一步了解。关键词:案例控制;全基因组关联;基因发现,基因环境独立性;模块化方法;多次测试;筛选测试;加权假设检验。缩写:CC,区分大小写; CC(EXP),CC在暴露的子组中; CO(仅案例); CT,鸡尾酒; DF,自由度; D-G,疾病基因; EB,经验贝叶斯; EB(EXP),暴露亚组中的EB; EDGxE,联合边缘/关联筛查; FWER,家庭错误率; G-E,基因环境; GEI,基因-环境相互作用; GEWIS,基因环境广泛的相互作用研究; H2,混合两步; LR,似然比; MA,边际;或,优势比; SE,灵敏度; SP,特异性; TS,两步基因环境筛选;

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