Multi-Cohort, Network-Guided Regression for GE/GG Interactions in Disease Traits

描述(申请人提供)：复杂的疾病被认为是由许多遗传和环境性质的因素以及生活方式引起的。例子包括心血管疾病(如心脏病、高血压)、代谢疾病(如糖尿病)、神经疾病(如阿尔茨海默氏症、帕金森氏症和自闭症)和癌症。这些疾病给家庭和社会造成的经济代价令人震惊，而它们对个人及其亲人造成的身体和精神损失是无法估量的。虽然早些时候人们预计破译人类基因组将是深入了解复杂疾病原因的直接先兆，但现在很明显，这一领域的进展必须来自于通过生物路径和相关网络系统来解开基因与环境以及基因与彼此之间的相互作用。我们在这个提案中的目标是开发一种新的网络引导的统计方法，以促进发现与人类疾病相关的复杂数量性状的基因-环境(GxE)和基因-基因(GXG)交互作用。具体地说，我们将(1)开发一类稀疏的、网络引导的回归模型来检测GxE和GXG的相互作用，(2)通过开发两阶段荟萃分析策略将该回归框架的适用性扩展到多个队列，以及(3)在模拟和使用来自两个特定疾病领域的数据的情况下评估整体方法：糖尿病相关数量性状和肺数量性状和疾病。数据分析将与Framingham心脏研究的同事和两个财团一起完成：Magic和Charge。拟议研究的成功完成将产生一套非常新颖和连贯的工具(包括软件实施)，用于在目前的大规模多队列关联分析中，采用原则性和生物知情的两阶段方法来检测与人类疾病相关的GxE和GXG相互作用。归根结底，我们的工作应该有助于通过对整个进程的早期阶段产生根本性影响，显著加快靶向治疗和个性化药物战略的发展。

DESCRIPTION (provided by applicant): Complex diseases are believed to be caused by a number of factors of both genetic and environmental nature, as well as lifestyle. Examples include cardiovascular (e.g., heart disease, hypertension), metabolic (e.g., diabetes), and neurological (e.g., Alzheimer's, Parkinson's, and autism) diseases, and cancer. The financial costs of these diseases on families and society are staggering, while their physical and emotional toll on individuals and their loved ones is incalculable. While early on it was expected that decoding the human genome would be an immediate precursor to gaining significant insight into the causes of complex diseases, it is clear now that progress in this area must come from unraveling the interplay of genes with environment, as well as with each other, through the system of biological pathways and related networks. Our goal in this proposal is the development of a novel network-guided statistical methodology to facilitate the discovery of gene-environment (GxE) and gene-gene (GxG) interactions associated with complex quantitative traits associated with human disease. Specifically, we will (1) develop a class of sparse, network-guided regression models for detection of GxE and GxG interactions, (2) extend the applicability of this regression framework to multiple cohorts through the development of a two-stage meta-analysis strategy, and (3) assess the overall methodology both in simulation and using data from two specific disease areas: diabetes-related quantitative traits and pulmonary quantitative traits and diseases. The data analyses will be done in conjunction with colleagues at the Framingham Heart Study and two consortia: MAGIC and CHARGE. Successful completion of the proposed research will yield a highly novel and coherent set of tools (including software implementation) for a principled and biologically- informed two-stage approach to detecting GxE and GxG interactions associated with human disease in current large-scale, multi-cohort association analyses. Ultimately, our work should help to significantly accelerate the development of targeted therapies and personalized medicine strategies, through its fundamental impact on the early stages of the overall process.