A partially Linear Tree-based Regression Model for Assessing Complex Joint Gene-g

描述（由申请人提供）： 复杂疾病的基因解剖的成功可能会大大受益于联合基因效应或基因-基因相互作用的明智探索，这反过来又严重依赖于统计工具的力量。标准回归模型便于评估主效应和低阶相互作用，但不适合探索复杂的高阶基因-基因相互作用。基于树的方法是一个有吸引力的替代解开可能的相互作用，但它有困难的建模加性主效应。本文提出了一类新的半参数回归模型，称为部分线性树基回归（PLTR）模型，它具有广义线性回归和树模型的优点。PLTR模型通过线性主效应和非参数树结构的组合来量化基因的联合效应。特别是，它允许使用的混淆变量的线性项和树结构的联合效果的利益。第一个具体的目标是评估迭代算法，以适应PLTR模型，并制定方法，用于识别和测试嵌套在树的最佳修剪树的显着性结果从拟合算法。第二个具体目标是应用开发的方法在几个癌症遗传流行病学研究的分析，探讨联合基因-基因或基因-环境相互作用。

DESCRIPTION (provided by applicant): The success of genetic dissection of complex diseases may greatly benefit from judicious exploration of joint gene effects or gene-gene interactions, which, in turn, critically depends on the power of statistical tools. Standard regression models are convenient for assessing main effects and low-order interactions but not for exploring complex higher- order gene-gene interactions. Tree-based methodology is an attractive alternative for disentangling possible interactions, but it has difficulty in modeling additive main effects. We propose a new class of semiparametric regression models, termed partially linear tree-based regression (PLTR) models, that has the advantages of both generalized linear regression and tree models. A PLTR model quantifies joint effects of genes by a combination of linear main effects and a nonparametric tree-structure. In particular, it permits the use of linear terms for confounder variables and a tree structure for the joint effect of interest. The first specific aim is to assess an iterative algorithm to fit the PLTR model and to develop methods for identifying and testing the significance of an optimal-pruned tree nested within the tree resultant from the fitting algorithm. The second specific aim is to apply the developed method in analyses of several cancer genetic epidemiology studies to explore joint gene-gene or gene-environment interactions.