Genetics meets proteomics: perspectives for large population-based studies
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Updated: Dec 10, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Karsten Suhre1, Mark I McCarthy2,3,4,5, Jochen M Schwenk6
1Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar. kas2049@qatar-med.cornell.edu.
Abstract:
Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.
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