Genetics meets proteomics: perspectives for large population-based studies
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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Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets.
“Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1–4.”
From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [1]
Major developments in mass spectrometry-based, antibody-based and aptamer-based proteomics over the past decade have enabled the robust quantification of thousands of proteins in single samples.
“Major developments in mass spectrometry-based, antibody-based and aptamer-based proteomics over the past decade have enabled the robust quantification of thousands of proteins in single samples87–90.”
From Measuring biological age using omics data (2022) · cites this paper as [88]
Protein levels are likely to be regulated post-transcriptionally rather than by regulating mRNA level.
“The enrichment of coding-variants for pQTLs in cis and trans (Extended Data Fig.10c, Tables S3, S4, S5) suggests that protein levels are likely to be regulated post-transcriptionally rather than by regulating mRNA level30.”
From Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders (2021) · cites this paper as [30]
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“The study and validation of biomarkers as well as the discovery and development of new medications might both benefit from proteomics [14].”
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- Authors
- Suhre, K., McCarthy, M. I., & Schwenk, J. M.
- Affiliations
- Weill Cornell Medical College in Qatar (QA)
- Centre for Human Genetics (GB)
- Science for Life Laboratory (SE)
- Corresponding author
- Suhre, Karsten
- Schwenk, Jochen M.
- Venue
- Nature Reviews Genetics
- Published
- 2020-08-28
- Volume
- 22
- Issue
- 1
- Pages
- 19-37
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- OpenAlex
- W3082024297
- Semantic Scholar
- d4cce42b453dc99f69e976c938130013e22924a0
- Cited by
- 478
- References
- 151
- Citations by year
- 2026: 76 · 2025: 111 · 2024: 96 · 2023: 79 · 2022: 62 · 2021: 50 · 2020: 4
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute for Health and Care Research (NF-SI-0617-10090)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- plasma proteome, proteogenomics, genome-wide association study, endophenotypes, biomarkers, therapeutic target, biobanks, genetic variants, circulating proteins, disease mechanisms
- MeSH terms
- Blood Proteins, Humans, Phenotype, Biomarkers, Proteome, Proteomics, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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