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Turn genetic associations into pathway- and system-level insights

Proteomics built for scale, reproducibility, and cross-study consistency

Proteogenomics of Complex Disease Research: Integrating pQTLs, SomaScan Proteomics, and Multi-Omics Networks to Resolve Genetic Risk in COPD and Beyond

June 14, 2026
12:15–1:30 PM | Room A7

Russell Bowler, MD, PhD
Department Chair in Genomic Sciences and Systems Biology | The Charis Eng MD, PhD Chair in Genomic Medicine | Professor of Molecular Medicine, Case Western Reserve University | Cleveland Clinic Research

This presentation will highlight how large-scale proteomics, integrated with genetic and multiomics data, can advance mechanistic understanding and risk stratification in complex human diseases research. Using chronic obstructive pulmonary disease (COPD) as a paradigm, we will demonstrate how SomaScan™️ Assay-based proteomic profiling across deeply phenotyped cohorts (e.g., COPDGene, SPIROMICS, and TOPMed studies) enables identification of robust biomarkers of disease severity and progression that replicate across populations.

A central focus will be the role of protein quantitative trait loci (pQTLs) in bridging genetic association signals to downstream biology. We will present results from multi-ancestry pQTL analyses showing that a substantial proportion of circulating proteins are genetically regulated, with both cis- and trans-acting variants. Importantly, we will highlight how pQTLs complement eQTLs, particularly for proteins where mRNA levels poorly predict abundance, and how ancestry-specific linkage disequilibrium patterns influence causal variant interpretation. We will further illustrate how integrating pQTLs with GWAS through Mendelian randomization and colocalization frameworks enables inference of causal pathways and identification of therapeutic targets.

We will compare the SomaScan Assay with other proteomic platforms, emphasizing reproducibility and scalability, and discuss the development of protein risk scores using penalized regression approaches. Finally, we will present multiomics network integration approaches, including SmCCNet and related frameworks, to identify coordinated molecular modules that link genetic variation to disease phenotypes. Together, these approaches define a scalable strategy for translating genomic discoveries into biologically grounded insights and clinically actionable biomarkers.

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