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WIREs Syst Biol Med
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Systems biology approaches to epidemiological studies of complex diseases

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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. WIREs Syst Biol Med 2013, 5:677–686. doi: 10.1002/wsbm.1242 This article is categorized under: Analytical and Computational Methods > Analytical Methods Laboratory Methods and Technologies > Genetic/Genomic Methods

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Matching patterns of GWAS signals and eQTL signals. Left plot: Z‐scores for SNP–phenotype association; right plot: Z‐score for eQTL association for a given gene transcript. Peaks labeled with * are those SNPs that are shared between phenotype and gene expression. The peak matching provides evidence for gene–phenotype association.
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Simultaneous consideration of all genetic variants (Gvg), methylation states (Meg), and histone modifications (Hmg) for gene g in association analysis of complex phenotypes.
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A causal diagram showing the relationships between two genotypes z1 and z2, two gene expression levels x1 and x2, a clinical phenotype y, and a confounding phenotype w.
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