Multivariate functional analysis of the genetic basis of cancer

癌症遗传基础的多变量功能分析

• 批准号: 8486199
• 负责人: Lin Chen
• 金额: $ 7.9万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-07 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8486199
• 关键词: Biological; Cancer Etiology; Cataloging; Catalogs; Complex; Data; Disease; Disease Pathway; Environmental Risk Factor; Equilibrium; Genes; Genetic; Genome; Genomics; Human; Intercistronic Region; Joints; Junk DNA; Knowledge; Location; Malignant Neoplasms; Methods; Modeling; Multivariate Analysis; National Human Genome Research Institute; Pathway interactions; Phenotype; Population; Predisposition; Publishing; Research; Risk; Sampling; Source; Statistical Methods; Structure; Testing; Time; Work; base; case control; cost; design; flexibility; gene environment interaction; gene interaction; genetic analysis; genetic risk factor; genome wide association study; genome-wide; human disease; improved; insight; method development; non-genetic; public health relevance; trait

DESCRIPTION (provided by applicant): The objective of the proposed research is to develop multivariate statistical methods for joint analyses of functionally related biological information n association and interaction using existing GWA data. Specifically, we propose to develop methods for joint analyzing multiple SNPs in a SNP set (e.g. a gene), and methods for jointly analyzing multivariate secondary phenotypes with potential ignorable and non-ignorable missing data. We further propose to develop methods for genome-wide gene-gene interaction analysis, in which groups of between- gene SNP-SNP correlations will be analyzed together to detect interactions. In addition, we propose to integrate a priori knowledge in our genome-wide association or interaction analyses; and we group sets of predictors by a priori knowledge and design flexible regularized regression approaches to constrain the parameter estimation and achieve efficiency. The proposed research is motivated by opportunities and needs in GWA studies, and much of our proposed work can be cost-efficiently implemented with publicly accessible GWA data for different cancers to improve our understanding of the genetic basis and disease etiology of cancer.

描述（由申请人提供）：拟议研究的目的是开发多元统计方法，使用现有的 GWA 数据对功能相关的生物信息、关联和相互作用进行联合分析。具体来说，我们建议开发联合分析 SNP 集中（例如基因）中的多个 SNP 的方法，以及联合分析具有潜在可忽略和不可忽略缺失数据的多元次级表型的方法。我们进一步建议开发全基因组基因间相互作用分析的方法，其中基因间 SNP-SNP 相关性组将被一起分析以检测相互作用。此外，我们建议将先验知识整合到我们的全基因组关联或相互作用分析中；我们通过先验知识对预测变量集进行分组，并设计灵活的正则化回归方法来约束参数估计并实现效率。拟议的研究是由 GWA 研究的机会和需求推动的，我们提出的大部分工作都可以利用公开的不同癌症的 GWA 数据来经济有效地实施，以提高我们对癌症遗传基础和疾病病因学的理解。