Semiparametric Methods for Gene-environment Interaction

基因-环境相互作用的半参数方法

• 批准号: 8512724
• 负责人: Eric Joel Tchetgen Tchetgen
• 金额: $ 33.58万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-20 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8512724
• 关键词: Algorithms; Area; Case-Control Studies; Cations; Cohort Studies; Collection; Communities; Computer software; Coronary heart disease; Cox Models; Cox Proportional Hazards Models; Data; Development; Disease Outcome; Environment; Environmental Exposure; Environmental Health; Frequencies; Genes; Genetic; Genetic screening method; Genomics; Goals; Investigation; Joints; Knowledge; Lead; Logistic Regressions; Methodology; Methods; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Performance; Population; Property; Proportional Hazards Models; Public Health; Regression Analysis; Research; Role; Sampling; Science; Statistical Methods; Testing; Work; abstracting; base; cancer type; case control; cohort; density; disorder risk; gene environment interaction; genetic association; genetic variant; genome wide association study; genome-wide; hazard; health science research; human disease; improved; interest; malignant breast neoplasm; novel; prospective; simulation; user friendly software; user-friendly

DESCRIPTION (provided by applicant): Recent advance of genomic sciences has significantly changed the landscape of environmental health science research. Collection of high throughput genomic data has become increasingly important for investigating the interplay of genes and environment in causing human diseases in environmental case-control and cohort studies. Analysis of such high-dimensional gene-environmental data presents substantial statistical and computational challenges, especially in investigating gene and environment interactions. Limited statistical developments have been made in this area so far. This methodological shortage has become a bottleneck for effectively studying the roles of genes and their interactions with environment in causing human diseases. The purpose of this proposal responds to this need by developing advanced semi-parametric statistical methods to analyze high throughput data from gene and environment studies. We plan (1) to develop semi-parametric locally efficient methods for double-robust estimation in a case-control study, of a model for the joint effect of a genetic factor, an environmental exposure and multiple extraneous confounding factors, (2) to develop semi-parametric methods for multiple robust estimation in cohort and case-control studies, of a model of interaction between a genetic factor and an environmental exposure in the effect that they produce on a binary disease outcome, (3) to develop semi-parametric methods for double robust inferences of genetic effects incorporating gene-environment interaction and confounding adjustment in a Cox proportional hazards model for censored survival data and (4) develop efficient and open access user-friendly algorithms and statistical software that implement these methods with the goal of disseminating them freely to the gene-environment research community. In addition, we will evaluate the performance of our methods in three ongoing GWAS we have been involved with as well as in simulation studies.

描述(由申请人提供)：基因组科学的最新进展显著改变了环境健康科学研究的格局。在环境病例对照和队列研究中，高通量基因组数据的收集对于研究基因和环境在导致人类疾病中的相互作用变得越来越重要。对这种高维基因-环境数据的分析提出了巨大的统计和计算挑战，特别是在研究基因和环境的相互作用方面。到目前为止，这一领域的统计发展有限。这种方法的缺乏已经成为有效研究基因的作用以及它们与环境相互作用导致人类疾病的瓶颈。这项建议的目的是通过开发先进的半参数统计方法来分析来自基因和环境研究的高通量数据来回应这一需求。我们计划(1)在病例对照研究中开发用于双重稳健估计的半参数局部有效方法，用于遗传因素、环境暴露和多个外来混杂因素的联合作用的模型，(2)开发用于在队列和病例对照研究中的多重稳健估计的半参数方法，用于遗传因素和环境暴露之间的相互作用的模型，它们对二元疾病结果的影响，(3)开发半参数方法，用于考虑基因-环境交互作用和混杂调整的遗传效应在COX比例风险模型中的双重稳健推断。(4)开发高效和开放的用户友好的算法和统计软件，以实现这些方法，目的是将它们自由地传播到基因-环境研究社区。此外，我们将在我们参与的三个正在进行的GWA以及模拟研究中评估我们方法的性能。