title: Multivariate dimensionality reduction approaches to identify gene-gene and gene-environment interactions underlying multiple complex traits.
abstract: The elusive but ubiquitous multifactor interactions represent a stumbling block that urgently needs to be removed in searching for determinants involved in human complex diseases. The dimensionality reduction approaches are a promising tool for this task. Many complex diseases exhibit composite syndromes required to be measured in a cluster of clinical traits with varying correlations and/or are inherently longitudinal in nature (changing over time and measured dynamically at multiple time points). A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype and longitudinal data, as well as improving statistical power for multiple significance testing via a two-stage testing procedure that involves a multivariate analysis for grouped phenotypes followed by univariate analysis for the phenotypes in the significant group(s). In this article, we propose a multivariate extension of generalized multifactor dimensionality reduction (GMDR) based on multivariate generalized linear, multivariate quasi-likelihood and generalized estimating equations models. Simulations and real data analysis for the cohort from the Study of Addiction: Genetics and Environment are performed to investigate the properties and performance of the proposed method, as compared with the univariate method. The results suggest that the proposed multivariate GMDR substantially boosts statistical power.
authors.affiliation: Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, P.R. China; Research Center for Air Pollution and Health, Zhejiang University, Hangzhou, P.R. China.
authors.forename: Hai-Ming
authors.initials: HM
authors.lastname: Xu
authors.affiliation: Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, P.R. China.
authors.forename: Xi-Wei
authors.initials: XW
authors.lastname: Sun
authors.affiliation: Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, P.R. China.
authors.forename: Ting
authors.initials: T
authors.lastname: Qi
authors.affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Wan-Yu
authors.initials: WY
authors.lastname: Lin
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
authors.forename: Nianjun
authors.initials: N
authors.lastname: Liu
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
authors.forename: Xiang-Yang
authors.initials: XY
authors.lastname: Lou
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D066264:Datasets as Topic; D004843:Epistasis, Genetic; D059647:Gene-Environment Interaction; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D058011:Multifactor Dimensionality Reduction; D020412:Multifactorial Inheritance; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results; D014029:Tobacco Use Disorder / Q000235:genetics