The integration of transcriptomic and proteomic data from lung tissue with chronic obstructive pulmonary disease (COPD)-associated genetic variants could provide insight into the biological mechanisms for COPD. Here, we assessed associations between lung transcriptomics and proteomics with COPD in 98 subjects from the Lung Tissue Research Consortium. Low correlations between transcriptomics and proteomics were generally observed, but higher correlations were found for COPD-associated proteins. We integrated COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins to identify regulatory cis quantitative trait loci (QTLs). Significant expression QTLs (eQTLs) and protein QTLs (pQTLs) were found regulating multiple COPD-associated biomarkers. We investigated mediated associations from significant protein quantitative trait loci through transcripts to protein levels of COPD-associated proteins. We also attempted to identify colocalized effects between GWAS, eQTL, and pQTL signals. Evidence for colocalization between COPD GWAS signals and pQTL for RHOB and eQTL for DSP was found. We applied Weighted Gene Co-Expression Network Analysis (WGCNA) to find consensus COPD-associated network modules. Two network modules generated by consensus WGCNA were associated with COPD with FDR < 0.05. One network module is related to the catenin complex, and the other module is related to plasma membrane components. In summary, multiple cis-acting effects for transcripts and proteins associated with COPD were identified. Colocalization analysis, mediation analysis, and correlation-based network analysis of multiple Omics data may identify key genes and proteins that work together to influence COPD pathogenesis.
Am J Respir Cell Mol Biol
Integrating Genetics, Transcriptomics, and Proteomics in Lung Tissue to Investigate COPD.