A Partially Linear Tree-based Regression Model for Multivariate Outcomes
Yu, Kai1; Wheeler, William2; Li, Qizhai1,3; Bergen, Andrew W.4; Caporaso, Neil1; Chatterjee, Nilanjan1; Chen, Jinbo5
Source PublicationBIOMETRICS
AbstractP>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.
KeywordGeneralized estimating equation Genetic association study Model selection Multiple-comparison adjustment Tree-based model
Funding ProjectNIH ; National Cancer Institute ; National Science Foundation of China[10371126] ; [U01 DA020830]
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiology ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000275727200011
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Document Type期刊论文
Corresponding AuthorYu, Kai
Affiliation1.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
Recommended Citation
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.
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