Methods and Software for Large-Scale Gene-Environment Interaction Studies

项目概要/摘要 复杂的人类疾病和相关的数量性状是许多风险因素的相互作用，包括遗传因素， 和环境成分。基因-环境相互作用研究是一个通用的框架， 识别改变环境、生理、生活方式或治疗效果的遗传变异，以及 那些导致复杂特征上的年龄、性别、种族/民族差异的因素。此外，遗传关联研究 解释基因与环境的相互作用，以加强我们对遗传的理解， 通过考虑不同暴露层的不同遗传效应，对复杂疾病的结构进行了研究。与 随着技术的进步和成本的降低，基因和基因组数据正在大规模地产生， 鳞片然而，用于基因-环境相互作用研究的常用统计软件程序 通常是在多年前开发的，并且它们的计算算法还没有被优化以分析 从可能复杂的研究设计中获得数十万到数百万的样本。为了填补之间的差距 大规模基因-环境相互作用研究中当前和未来的分析需求以及当前的分析需求 解决方案，我们计划（目标1）开发有效的算法，常见的变异基因-环境相互作用 分析，规模与样本量线性;（目标2）开发新的统计检验罕见变异基因- 环境相互作用分析，在相关样本的混合效应模型框架内;以及（目标3） 在开源新软件程序中实现所提出的统计方法和计算算法。 我们的目标1解决了当前在进行基因-环境相互作用研究中的计算挑战， 多达数百万个样本。在目标2中，我们计划解决基因环境中的统计和计算挑战 大规模全基因组测序数据的相互作用分析，解释相关性，复杂研究 设计，以及模型错误。目标3专注于软件开发，我们将出色地交付- 用于大规模基因环境的文档化和用户友好的软件包和分析管道 互动研究该方法和软件程序将应用于正在进行的全基因组测序 项目，以及生物库规模的数据，它们将大大促进大规模基因和 基因组数据，用于未来几年的基因-环境相互作用研究，以更好地了解遗传基础 复杂的心脏代谢、肺、血液、睡眠疾病及其年龄、性别、种族/民族差异，以及 在精准健康研究中推广个性化疾病预防和治疗策略。

PROJECT SUMMARY/ABSTRACT Complex human diseases and related quantitative traits are the interplay of many risk factors, including genetic and environmental components. Gene-environment interaction studies are a general framework that can be used to identify genetic variations that modify environmental, physiological, lifestyle, or treatment effects, as well as those contributing to age, sex, racial/ethnic disparities on complex traits. Moreover, genetic association studies accounting for gene-environment interactions are conducted to enhance our understandings on the genetic architecture of complex diseases by allowing for different genetic effects in different exposure strata. With the recent advances in technology and lowering costs, genetic and genomic data are being generated on very large scales. However, commonly used statistical software programs for gene-environment interaction studies were generally developed many years ago, and their computational algorithms have not been optimized to analyze hundreds of thousands to millions of samples from possibly complex study designs. To fill in the gap between current and future analytical needs in large-scale gene-environment interaction studies and current analytical solutions, we plan to (Aim 1) develop efficient algorithms for common variant gene-environment interaction analyses that scale linearly with the sample size; (Aim 2) develop new statistical tests for rare variant gene- environment interaction analyses, in the mixed effects model framework for correlated samples; and (Aim 3) implement proposed statistical methods and computational algorithms in open-source new software programs. Our Aim 1 addresses current computational challenges in conducting gene-environment interaction studies in up to millions of samples. In Aim 2, we plan to solve statistical and computational challenges in gene-environment interaction analyses of large-scale whole genome sequencing data, accounting for relatedness, complex study designs, as well as model misspecification. Aim 3 focuses on software development and we will deliver well- documented and user-friendly software packages and analysis pipelines for large-scale gene-environment interaction studies. The methods and software programs will be applied to ongoing whole genome sequencing projects, as well as biobank-scale data, and they will significantly facilitate the use of large-scale genetic and genomic data for gene-environment interaction studies in upcoming years to better understand the genetic basis of complex cardio-metabolic, lung, blood, sleep diseases and their age, sex, racial/ethnic disparities, and promote personalized disease prevention and treatment strategies in precision health research.