Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models. In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the “joint” model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. These analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s.

中文翻译：

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从研究基因与环境相互作用的联合模型得出分层效应。

经常使用包含相互作用项并执行SNP和/或相互作用效应联合测试的模型来发现基因-环境（GxE）相互作用。当环境暴露是一个二元变量时，从暴露分层模型进行的分析（包括分别估计未暴露个体和暴露个体的遗传效应）可能会很有意义。在专注于GxE交互的大型联合体中，仅执行了联合测试，从暴露分层模型和边际模型（即不考虑交互作用）的模型中获取汇总统计数据可能是具有挑战性的。在这项工作中，我们使用“联合”模型中的汇总统计数据，开发了一个简单的框架来估算二元暴露的每个层次和边际模型中的汇总统计数据。我们进行了模拟研究，以评估估算器的准确性，并检查了潜在的偏差来源，例如基因型与暴露之间的相关性以及暴露层次内不同的表型差异。这些模拟的结果突显了我们的估算器的高理论准确性，并提供了对潜在偏差来源影响的见解。然后，我们将我们的方法应用于真实数据，并证明了在通过样本量过滤SNP以减轻潜在偏差之后，估算器的保留精度。这些分析证明了我们的方法从基因-环境相互作用的联合模型中估计分层统计量和边际汇总统计量的准确性。除了有助于解释GxE筛查外，这项工作还可用于指导进一步的功能分析。我们提供了一个用户友好的Python脚本，可将该策略应用于实际数据集。Python脚本和文档可从https://gitlab.pasteur.fr/statistical-genetics/j2s获得。