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Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD.

ABSTRACT: The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes.We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches.Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.

SUBMITTER: Chu JH

PROVIDER: S-EPMC4105829 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD.

Chu Jen-hwa JH Hersh Craig P CP Castaldi Peter J PJ Cho Michael H MH Raby Benjamin A BA Laird Nan N Bowler Russell R Rennard Stephen S Loscalzo Joseph J Quackenbush John J Silverman Edwin K EK

BMC systems biology 20140625

<h4>Background</h4>The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes.<h4>Results</h4>We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease s ...[more]

PMID: 24964944

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Project description:Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA-mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA-mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA-mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV1) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA-mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.

Dataset Information

Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD.

Publications

Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD.

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