Multivariate functional analysis of the genetic basis of cancer

描述（由申请人提供）：拟议研究的目的是开发多变量统计方法，用于使用现有GWA数据联合分析功能相关的生物信息、关联和相互作用。具体而言，我们提出开发用于联合分析SNP集合（例如基因）中的多个SNP的方法，以及用于联合分析具有潜在可验证和不可验证缺失数据的多变量次级表型的方法。我们进一步提出开发全基因组基因-基因相互作用分析的方法，其中基因间SNP-SNP相关性的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因子进行分组，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。拟议的研究是出于GWA研究的机会和需求，我们提出的大部分工作可以通过公开获取的不同癌症的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的理解。

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.