DEVELOPING STRATEGIES FOR JOINT GENE-ENVIRONMENT ANALYSIS

制定联合基因-环境分析策略

• 批准号: 8217748
• 负责人: ROBERT C CULVERHOUSE
• 金额: $ 22.8万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8217748
• 关键词: Accounting; Address; Age; Bioinformatics; Biological Process; Birth; Cigarette; Complex; Data; Data Analyses; Data Set; Detection; Diabetes Mellitus; Disease; Educational Background; Educational workshop; Environment; Environmental Risk Factor; Etiology; Evaluation; Family; Gene Frequency; Genes; Genetic; Genomics; Genotype; Goals; Grant; Guidelines; Hand; Heart; Heart Diseases; Joints; Knowledge; Light; Machine Learning; Meta-Analysis; Methods; Nicotine Dependence; Output; Pathway interactions; Performance; Phenotype; Public Health; Race; Recommendation; Reporting; Research; Research Personnel; Risk; Sample Size; Sensitivity and Specificity; Signal Transduction; Simulate; Smoke; Smoker; Smoking; Staging; Statistical Methods; Testing; Variant; addiction; base; case control; database of Genotypes and Phenotypes; density; gene environment interaction; genetic analysis; genetic association; genome wide association study; improved; sex; tool; tool development; trait

DESCRIPTION (provided by applicant): A key challenge for genetic analysis today is to account for the bulk of the phenotypic variance in complex traits that is attributable to genetic factors, but remains unexplained after multiple well-powered GWAS (Goldstein 2009; Hirschhorn 2009; Kraft and Hunter 2009). Many believe that systematically testing datasets for joint gene-environment effects (GxE) and gene-gene (GxG) effects will be essential in order to understand the genetics of complex phenotypes. However, the new era of high throughput, high-density genomic data (genotypes and sequencing) has given rise to serious computational and statistical challenges for the analysis of joint effects. The goal of this project is to develop effective strategies for the statistical analysis of joint-gene environment effects. The specific aims are to (1) identify strengths and weaknesses of multiple analytic approaches to joint GxE effects, and (2) develop new methods for coordinated meta-analysis. To accomplish Aim 1, we will systematically examine and compare traditional (e.g. regression based) and modern (e.g. partitioning, machine learning, information theoretic) analysis methods for case-control and quantitative phenotypes. The primary product will be specific guidance as to the conditions under which each method performs well. To accomplish Aim 2, we will extend recent joint-effects methods to a meta-analytic framework and develop improvements to currently used methods. For both aims, we will also analyze real data related to smoking, thereby improving our understanding of genetic and environmental contributors to smoking and addiction risk. These analyses of real data will be guided by bioinformatics-based variant prioritization. This study therefore will provide both guidance and tools needed to move the field of joint effects analysis forward. As a result, it will ultimately have a significant impact on our ability to account for the currently unexplained genetic contribution to phenotypic variance for complex traits. PUBLIC HEALTH RELEVANCE: Many diseases that greatly impact public health (such as heart disease, diabetes, and nicotine addiction) are the result of a complex interplay between genes and environmental factors. The purpose of this grant is to improve data analysis strategies (applicable to a wide variety of diseases) for the detection of joint effects of genes and environment. We will apply these methods to analyze existing data on smokers to improve our understanding of gene-environment effects on nicotine addiction.

描述（由申请人提供）：当今遗传分析的一个关键挑战是解释复杂性状中的大部分表型变异，这些变异可归因于遗传因素，但在多次有效的GWAS后仍无法解释（Goldstein 2009;赫什霍恩2009; Kraft and Hunter 2009）。许多人认为，为了了解复杂表型的遗传学，系统地测试联合基因-环境效应（GxE）和基因-基因（GxG）效应的数据集将是必不可少的。然而，高通量，高密度基因组数据（基因型和测序）的新时代已经引起了严重的计算和统计的联合效应的分析挑战。本项目的目标是为联合基因环境效应的统计分析开发有效的策略。具体目标是（1）确定联合GxE效应的多种分析方法的优点和缺点，以及（2）开发协调荟萃分析的新方法。为了实现目标1，我们将系统地检查和比较传统（例如基于回归）和现代（例如分区，机器学习，信息理论）的病例对照和定量表型分析方法。主要产品将是关于每种方法表现良好的条件的具体指导。为了实现目标2，我们将把最近的联合效应方法扩展到一个元分析框架，并对目前使用的方法进行改进。为了这两个目标，我们还将分析与吸烟有关的真实的数据，从而提高我们对吸烟和成瘾风险的遗传和环境因素的理解。这些对真实的数据的分析将由基于生物信息学的变异优先化指导。因此，这项研究将提供指导和工具，推动联合效应分析领域向前发展。因此，它最终将对我们解释目前无法解释的遗传对复杂性状表型方差的贡献的能力产生重大影响。 公共卫生关系：许多严重影响公共健康的疾病（如心脏病、糖尿病和尼古丁成瘾）是基因和环境因素之间复杂相互作用的结果。该补助金的目的是改进数据分析策略（适用于各种疾病），以检测基因和环境的联合影响。我们将应用这些方法来分析吸烟者的现有数据，以提高我们对基因-环境对尼古丁成瘾影响的理解。