title: Module-based association analysis for omics data with network structure.
abstract: Module-based analysis (MBA) aims to evaluate the effect of a group of biological elements sharing common features, such as SNPs in the same gene or metabolites in the same pathways, and has become an attractive alternative to traditional single bio-element approaches. Because bio-elements regulate and interact with each other as part of network, incorporating network structure information can more precisely model the biological effects, enhance the ability to detect true associations, and facilitate our understanding of the underlying biological mechanisms. However, most MBA methods ignore the network structure information, which depicts the interaction and regulation relationship among basic functional units in biology system. We construct the connectivity kernel and the topology kernel to capture the relationship among bio-elements in a module, and use a kernel machine framework to evaluate the joint effect of bio-elements. Our proposed kernel machine approach directly incorporates network structure so to enhance the study efficiency; it can assess interactions among modules, account covariates, and is computational efficient. Through simulation studies and real data application, we demonstrate that the proposed network-based methods can have markedly better power than the approaches ignoring network information under a range of scenarios.
authors.affiliation: Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, 27695, United States of America.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695, United States of America.
authors.forename: Arnab
authors.initials: A
authors.lastname: Maity
authors.affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Chuhsing Kate
authors.initials: CK
authors.lastname: Hsiao
authors.affiliation: Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America.
authors.forename: Deepak
authors.initials: D
authors.lastname: Voora
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, United States of America.
authors.forename: Rima
authors.initials: R
authors.lastname: Kaddurah-Daouk
authors.affiliation: Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, 27695, United States of America; Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695, United States of America; Department of Statistics, National Cheng-Kung University, Taiwan, R.O.C.
authors.forename: Jung-Ying
authors.initials: JY
authors.lastname: Tzeng
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D006801:Humans; D020641:Polymorphism, Single Nucleotide
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