Lung Research Poster Session

Yu-Hang Zhang, PHD

Postdoctoral Research Fellow
Channing Division of Network Medicine
Yu-Hang Zhang*, Michael H. Cho, Peter J. Castaldi, Craig P. Hersh, Mukul K. Midha, Michael R. Hoopmann, Robert L. Moritz, Edwin K. Silverman
Integrating Genetics, Transcriptomics, and Proteomics in Lung Tissue to Investigate COPD

Rationale: Omics data at multiple biological levels can enable comprehensive analyses of biological processes. We integrated lung tissue transcriptomics and proteomics data with genetic variation to provide new insights into COPD pathogenesis.

Methods: We included 98 subjects from the Lung Tissue Research Consortium (LTRC) with whole-genome sequencing and matched lung tissue transcriptomics (RNA-Seq) and proteomics (mass spectrometry) data. We identified cis- and trans- quantitative trait loci associated within previously reported COPD GWAS loci or near COPD-associated proteins. We performed mediation analysis linking COPD GWAS loci, COPD biomarker gene expression, and COPD. We explored colocalized effects between GWAS, eQTL, and pQTL signals. Weighted Gene Correlation Network Analysis (WGCNA) was applied to find preserved COPD-associated network modules.

Results: Low correlations between transcriptomics and proteomics were observed (mean 0.054). Significant cis-QTL effects were observed near multiple COPD biomarkers, including 11 cis-eQTLs and five cis-pQTLs; none of which overlapped. One transcriptomics and three proteomics suggestive mediation effects were found. A suggestive colocalization with common causal variants between the pQTL and COPD GWAS signal near RHOB was identified. Two preserved network modules (gene clusters) generated by WGCNA were associated with COPD (FDR < 0.05). One module is related to the catenin complex and the other module to plasma membrane components.

Conclusions: We evaluated associations between lung tissue Omics. Despite insufficient power to detect long-range genetic effects, multiple cis-acting QTL effects were identified. Colocalization analysis, mediation analysis, and correlation-based network analysis of multiple Omics data may identify key genes and proteins that influence COPD pathogenesis.