An Empirical Comparison of Joint and Stratified Frameworks for Studying G x E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group
An Empirical Comparison of Joint and Stratified Frameworks for Studying G x E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group
复制标题
DOI：
10.1002/gepi.21978
复制
发表时间：
2016-07-01
影响因子：
2.1
通讯作者：
Cupples, L. Adrienne
中科院分区：
文献类型：
--
作者：
Sung, Yun Ju;Winkler, Thomas W.;Cupples, L. Adrienne
研究基因与环境(G×E)的相互作用很重要，因为它们扩展了我们对复杂性状遗传结构的了解，并可能有助于识别仅通过主效应分析无法检测到的新变异。研究G×E交互作用的主要统计框架使用同时包括遗传主效应和G×E交互作用效应的单一回归模型(联合框架)。替代分层框架结合了在暴露组和非暴露组中分别进行的遗传主效应分析的结果。虽然已经有一些理论和模拟的研究，但缺乏对这两个框架的实证比较。在这里，我们使用全基因组收缩压关联研究的结果来比较这两个框架，这些研究涉及20个欧洲血统的20个队列，包括79,731个个体，其中包括320万个低频和650万个常见变异。我们的队列样本大小从456到22,983不等，既包括基于家庭的样本，也包括基于人口的样本。在针对群体的分析中，这两个框架为基于人群的群体提供了类似的推论。对于以家庭为基础的队列，这一协议被减少了。在Meta分析中，尽管样本量增加，但两个框架之间的一致性低于特定队列分析中观察到的一致性。在Meta分析中，一致性取决于(1)次要等位基因频率，(2)是否将以家族为基础的队列纳入Meta分析，以及(3)筛选方案。分层框架似乎只适用于以人口为基础的队列中的常见变种，与联合框架非常接近。我们的结论是，联合框架是首选的方法，在处理低频变异和/或基于家族的队列时，应该使用该框架来控制假阳性。
Studying gene-environment (G x E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G x E interactions uses a single regression model that includes both the genetic main and G x E interaction effects (the joint framework). The alternative stratified framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts.