A Partially Linear Tree-based Regression Model for Multivariate Outcomes

doi:10.1111/j.1541-0420.2009.01235.x

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	A Partially Linear Tree-based Regression Model for Multivariate Outcomes
	Yu, Kai 1; Wheeler, William 2; Li, Qizhai 1,3; Bergen, Andrew W.4; Caporaso, Neil 1; Chatterjee, Nilanjan 1; Chen, Jinbo 5
	2010-03-01
发表期刊	BIOMETRICS
ISSN	0006-341X
卷号	66 期号:1 页码:89-96
摘要	P>In the genetic study of complex traits, especially behavior related ones, such as smoking and alcoholism, usually several phenotypic measurements are obtained for the description of the complex trait, but no single measurement can quantify fully the complicated characteristics of the symptom because of our lack of understanding of the underlying etiology. If those phenotypes share a common genetic mechanism, rather than studying each individual phenotype separately, it is more advantageous to analyze them jointly as a multivariate trait to enhance the power to identify associated genes. We propose a multilocus association test for the study of multivariate traits. The test is derived from a partially linear tree-based regression model for multiple outcomes. This novel tree-based model provides a formal statistical testing framework for the evaluation of the association between a multivariate outcome and a set of candidate predictors, such as markers within a gene or pathway, while accommodating adjustment for other covariates. Through simulation studies we show that the proposed method has an acceptable type I error rate and improved power over the univariate outcome analysis, which studies each component of the complex trait separately with multiple-comparison adjustment. A candidate gene association study of multiple smoking-related phenotypes is used to demonstrate the application and advantages of this new method. The proposed method is general enough to be used for the assessment of the joint effect of a set of multiple risk factors on a multivariate outcome in other biomedical research settings.
关键词	Generalized estimating equation Genetic association study Model selection Multiple-comparison adjustment Tree-based model
DOI	10.1111/j.1541-0420.2009.01235.x
语种	英语
资助项目	NIH ; National Cancer Institute ; National Science Foundation of China[10371126] ; [U01 DA020830]
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biology ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000275727200011
出版者	WILEY-BLACKWELL PUBLISHING, INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/10668
专题	中国科学院数学与系统科学研究院
通讯作者	Yu, Kai
作者单位	1.NCI, Div Canc Epidemiol & Genet, Rockville, MD 20892 USA 2.Informat Management Serv Inc, Rockville, MD 20892 USA 3.CAS, Acad Math & Syst Sci, Beijing 100190, Peoples R China 4.SRI Int, Ctr Hlth Sci, Mol Genet Program, Menlo Pk, CA 94025 USA 5.Univ Penn, Sch Med, Dept Biostat & Epidemiol, Philadelphia, PA 19104 USA
推荐引用方式 GB/T 7714	Yu, Kai,Wheeler, William,Li, Qizhai,et al. A Partially Linear Tree-based Regression Model for Multivariate Outcomes[J]. BIOMETRICS,2010,66(1):89-96.
APA	Yu, Kai.,Wheeler, William.,Li, Qizhai.,Bergen, Andrew W..,Caporaso, Neil.,...&Chen, Jinbo.(2010).A Partially Linear Tree-based Regression Model for Multivariate Outcomes.BIOMETRICS,66(1),89-96.
MLA	Yu, Kai,et al."A Partially Linear Tree-based Regression Model for Multivariate Outcomes".BIOMETRICS 66.1(2010):89-96.