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

用于评估复杂联合 Gene-g 的部分线性基于树的回归模型

• 批准号: 7409706
• 负责人: Jinbo Chen
• 金额: $ 7.88万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7409706
• 关键词: Algorithms; Cancer Etiology; Class; Complex; Computer software; Data; Disease; Disease regression; Dissection; Environment; Epidemiologist; Gene Order; Genes; Genetic; Joints; Linear Models; Linear Regressions; Methodology; Methods; Modeling; National Cancer Institute; Pennsylvania; Research Personnel; Risk; Risk Factors; Standards of Weights and Measures; Structure; Techniques; Testing; Trees; Universities; Variant; base; cancer genetics; disorder risk; gene environment interaction; gene interaction; genetic epidemiology; genetic variant; interest; success; tool

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.

描述（由申请人提供）：

项目成果

Two-stage extreme phenotype sequencing design for discovering and testing common and rare genetic variants: efficiency and power.

• DOI: 10.1159/000337300
• 发表时间: 2012
• 期刊: Human heredity
• 影响因子: 1.8
• 作者: Kang G;Lin D;Hakonarson H;Chen J
• 通讯作者: Chen J

Efficient designs of gene-environment interaction studies: implications of Hardy-Weinberg equilibrium and gene-environment independence.

• DOI: 10.1002/sim.4460
• 发表时间: 2012-09-28
• 期刊: STATISTICS IN MEDICINE
• 影响因子: 2
• 作者: Chen, Jinbo;Kang, Guolian;VanderWeele, Tyler;Zhang, Cuilin;Mukherjee, Bhramar
• 通讯作者: Mukherjee, Bhramar