Data from: Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression

Accounting for gene-environment (GxE) interactions in complex trait association studies can facilitate our understanding of genetic heterogeneity under different environmental exposures, improve the ability to discover susceptible genes that exhibit little marginal effect, provide insight into the biological mechanisms of complex diseases, help to identify high-risk subgroups in the population, and uncover hidden heritability. However, significant GxE interactions can be difficult to find. The sample sizes required for sufficient power to detect association are much larger than those needed for genetic main effects, and interactions are sensitive to misspecification of the main effects model. These issues are exacerbated when working with binary phenotypes and rare variants, which bear less information on association. In this work, we present a similarity-based regression method for evaluating GxE interactions for rare variants with binary traits. The proposed model aggregates the genetic and GxE information across markers using genetic similarity thus increasing the ability to detect GxE signals. The model has a random effects interpretation, which leads to robustness against main effect misspecifications when evaluating GxE interactions. We construct score tests to examine GxE interactions and a computationally efficient EM algorithm to estimate the nuisance variance components. Using simulations and data applications, we show that the proposed method is a flexible and powerful tool to study the GxE effect in common or rare variant studies with binary traits.在复杂性状关联研究中纳入基因-环境交互作用（gene-environment interactions, GxE）的分析，有助于阐明不同环境暴露下的遗传异质性，提升发掘边际效应微弱的易感基因的效能，为复杂疾病的生物学机制提供新认知，辅助识别人群中的高危亚群，并揭示隐藏的遗传力。然而，显著的GxE交互作用往往难以被检出：达到足够检测效力所需的样本量远高于遗传主效应研究所需的样本量，且交互作用对主效应模型的设定偏误极为敏感。当针对二元表型与罕见变异开展研究时，此类问题会进一步加剧，因为这类变异携带的关联信息相对有限。本研究提出一种基于相似性的回归方法，用于分析二元性状罕见变异的GxE交互作用。所提模型通过遗传相似性整合各遗传标记的遗传信息与GxE信息，从而提升GxE信号的检测效能。该模型具备随机效应解释框架，这使得在评估GxE交互作用时，其对主效应模型的设定偏误具备鲁棒性。我们构建了用于检验GxE交互作用的得分检验方法，以及一种计算高效的期望最大化（EM）算法以估计冗余方差组分。通过模拟实验与实际数据应用，我们证实所提方法是一种灵活且高效的分析工具，可用于开展携带二元性状的常见或罕见变异研究中的GxE效应分析。