title: Systems biology approaches to epidemiological studies of complex diseases.
abstract: Systems biology approaches to epidemiological studies of complex diseases include collection of genetic, genomic, epigenomic, and metagenomic data in large-scale epidemiological studies of complex phenotypes. Designs and analyses of such studies raise many statistical challenges. This article reviews some issues related to integrative analysis of such high dimensional and inter-related datasets and outline some possible solutions. I focus my review on integrative approaches for genome-wide genetic variants and gene expression data, methods for joint analysis of genetic and epigenetic variants, and methods for analysis of microbiome data. Statistical methods such as mediation analysis, high-dimensional instrumental variable regression, sparse signal recovery, and compositional data regression provide potential frameworks for integrative analysis of these high-dimensional genomic data.
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Hongzhe
authors.initials: H
authors.lastname: Li
mesh_terms: D000818:Animals; D018562:Disease Transmission, Infectious; D057890:Epigenomics / Q000379:methods; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000379:methods; D006801:Humans; D056186:Metagenomics / Q000379:methods; D064307:Microbiota; D008954:Models, Biological; D049490:Systems Biology / Q000379:methods*
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