Novel Statistical Methods for Gene-Environment Interactions in Complex Diseases

复杂疾病中基因-环境相互作用的新统计方法

• 批准号: 7348474
• 负责人: KUNG-YEE LIANG
• 金额: $ 36.9万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-21 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7348474
• 关键词: Accounting; Address; Algorithms; Asthma; Attention; Back; Behavioral; Candidate Disease Gene; China; Chromosome Mapping; Chronic Disease; Cohort Studies; Communities; Complex; Coronary heart disease; DNA; Data Set; Development; Disease; Disease regression; Ensure; Environment; Environmental Risk Factor; Epidemiologist; Failure; Focus Groups; Genes; Genetic; Genetic Materials; Genome; Genomics; Human Genome Project; International; Investigation; Knowledge; Localized; Location; Logic; Malignant Neoplasms; Maps; Measurement; Mental disorders; Methods; Mind; Modeling; Numbers; Phase; Play; Policies; Predisposition; Prevention intervention; Principal Investigator; Process; Public Health; Public Health Applications Research; Reporting; Research; Research Personnel; Research Project Grants; Risk; Role; Signal Transduction; Singapore; Statistical Methods; Statistical Models; Structure; Students; Systems Analysis; Taiwan; Techniques; Training; Uncertainty; United States National Institutes of Health; Universities; Variant; Work; abstracting; cost; design; gene environment interaction; genome wide association study; genotyping technology; improved; interest; member; novel; oral cleft; size; skills

DESCRIPTION (provided by applicant): Unlike monogenic diseases, both genetic and environmental factors play essential and interactive roles in controlling risk to complex diseases such as coronary heart disease, asthma, cancer and psychiatric disorders. A principal challenge facing genome-wide association (GWA) scans and more focused investigations is the difficulty in detecting true genetic signals embedded in considerable statistical and technical variation, and the multiplicity associated with examining a large number of markers. Motivated by these issues and opportunities, we will develop a suite of complementary methods to address gene environment (GxE) interactions at many levels, including GWA studies, gene localization via linkage, and candidate gene approaches. For each level we will develop new techniques that fill gaps in currently available methods or develop and evaluate new strategic approaches. The research proposed here specifically focuses on enhancing methods to identify GxE interactions in the search for biologically important genes controlling risk. We propose to address the following specific aims: (1) Develop and evaluate new statistical methods to prioritize genes through proper ranking in genome-wide association studies that address GxE interactions; (2) Develop and evaluate new statistical methods to localize causal genes as part of linkage and fine mapping studies while considering GxE interactions; (3) Develop and evaluate new statistical methods to identify higher order interactions between environmental variables and SNPs in candidate gene studies; (4) Adapt existing and develop new statistical methods to address imprecise and missing environmental and genetic measurements; and (5) Develop and disseminate efficient algorithms for GxE analyses, and apply these methods in several ongoing genetic studies of complex diseases. Our novel statistical approaches will accommodate multiplicity, identify and incorporate measurement uncertainty, take advantage of genomic structure and the wealth of information from related linkage, fine mapping, and candidate gene studies, and structure inferences through biologically relevant statistical models. They will make efficient use of available information and report findings in a scientifically relevant framework. Our proposed research will confer scientific benefits, many with public health implications, of improved characterization of the interaction between genes and environmental factors controlling risk to complex diseases. In particular, quantifying GxE interaction can help identify high risk groups for focused intervention and prevention, and improve our understanding of etiologic mechanisms for specific diseases. (End of Abstract)

描述（由申请人提供）： 与单基因疾病不同，遗传因素和环境因素在控制复杂疾病（例如冠心病，哮喘，癌症和精神病）的风险中起着重要和互动作用。全基因组关联（GWA）扫描和更集中研究的主要挑战是难以检测嵌入相当大的统计和技术变化中的真实遗传信号，以及与大量标记相关的多重性。在这些问题和机遇的推动下，我们将开发一套互补方法，以在许多层面上解决基因环境（GXE）相互作用，包括GWA研究，通过连锁链接进行基因定位以及候选基因方法。对于每个级别，我们将开发新技术，以填补当前可用方法的空白或开发和评估新的战略方法。这里提出的研究专门针对增强方法，以识别搜索控制风险的生物学重要基因时的GXE相互作用。我们建议解决以下具体目的：（1）开发和评估新的统计方法，以通过在基因组全基因组关联研究中进行适当排名来确定基因的优先级，以解决GXE相互作用； （2）在考虑GXE相互作用的同时，开发和评估新的统计方法，以将因果基因定位为链接和精细映射研究的一部分； （3）开发和评估新的统计方法，以确定候选基因研究中环境变量和SNP之间的高阶相互作用； （4）调整现有的并开发新的统计方法来解决不精确和缺失的环境和遗传测量； （5）开发和传播有效的GXE分析算法，并将这些方法应用于复杂疾病的几种正在进行的遗传研究。我们的新型统计方法将适应多样性，识别和纳入测量不确定性，利用基因组结构以及相关链接，精细映射和候选基因研究的大量信息以及通过生物学相关的统计模型来推断。他们将有效利用可用信息，并在科学相关的框架中报告调查结果。我们提出的研究将赋予许多具有公共卫生影响的科学益处，以改善基因与控制复杂疾病风险的环境因素之间相互作用的表征。特别是，量化GXE相互作用可以帮助识别高风险群体以进行集中干预和预防，并提高我们对特定疾病的病因机制的理解。 （抽象的结尾）