The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Comparison of Proteomic Assessment Methods in Multiple Cohort Studies
Open accessAbstract
Novel proteomics platforms, such as the aptamer‐based SOMAscan platform, can quantify large numbers of proteins efficiently and cost‐effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross‐assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from −0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population‐based Multi‐Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody‐based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta‐analyzing proteomics data across assays and studies.
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“The detection of a cis-pQTL for a given protein measure is often used as a metric to confirm on-target affinity-probe binding12,15,16; existing studies comparing SomaScan and Olink measures have established that the detection of a cis-pQTL is a strong predictor of inter-platform correlation coefficients for a given protein12,15,16.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, existing comparisons of SomaScan and Olink platforms have shown that some protein measures are not concordant (median inter-platform correlation = 0.38–0.50)11–16, which challenges the accuracy of protein associations observed, especially as there is no “gold-standard” reference measure for most proteins.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The detection of a cis-pQTL for a given protein measure is often used as a metric to confirm on-target affinity-probe binding12,15,16; existing studies comparing SomaScan and Olink measures have established that the detection of a cis-pQTL is a strong predictor of inter-platform correlation coefficients for a given protein12,15,16.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Here, and across prior studies, cis-pQTL presence/absence is thought to provide one predictor of on-target protein measures12,15,16.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [15]
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“This modest overlap is unsurprising, given that correlations between proteins measured by both platforms are highly variable 52,53 , and shared genetic signals can be masked by extreme, assay-specific binding affinities 51 .”
From Plasma proteomic associations with genetics and health in the UK Biobank (2023) · cites this paper as [53]
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Citations
Cites
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Suhre, K., Arnold, M., Bhagwat, A. M., Cotton, R. J., Engelke, R., Raffler, J., Sarwath, H., Thareja, G., Wahl, A., DeLisle, R. K., et al. (2017) · Nature Communications · doi:10.1038/ncomms14357 · Cited by 800
Ethnic Differences in Coronary Calcification: The Multi-Ethnic Study of Atherosclerosis (MESA)
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Fit-for-Purpose Method Development and Validation for Successful Biomarker Measurement
Lee, J. W., Devanarayan, V., Barrett, Y. C., WEINER, R. S., Allinson, J. L., Fountain, S. T., Keller, S., Weinryb, I., Green, M., Duan, L., et al. (2006) · Pharmaceutical Research · doi:10.1007/s11095-005-9045-3 · Cited by 771
Co-regulatory networks of human serum proteins link genetics to disease
Emilsson, V., Ilkov, M., Lamb, J. R., Finkel, N., Gudmundsson, E. F., Pitts, R., Hoover, H., Gudmundsdottir, V., Horman, S. R., Aspelund, T., et al. (2018) · Science · doi:10.1126/science.aaq1327 · Cited by 680
Plasma protein patterns as comprehensive indicators of health
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Cited by
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Plasma proteomic associations with genetics and health in the UK Biobank
Sun, B. B., Chiou, J., Traylor, M., Benner, C., Hsu, Y.-H., Richardson, T. G., Surendran, P., Mahajan, A., Robins, C., Vasquez-Grinnell, S. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06592-6 · Cited by 1,880
Self-citationLarge-scale plasma proteomics comparisons through genetics and disease associations
Eldjarn, G. H., Ferkingstad, E., Lund, S. H., Helgason, H., Magnusson, O. T., Gunnarsdottir, K., Olafsdottir, T. A., Halldorsson, B. V., Olason, P. I., Zink, F., et al. (2023) · Nature · doi:10.1038/s41586-023-06563-x · Cited by 583
High-throughput proteomics: a methodological mini-review
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A genome-wide association study of serum proteins reveals shared loci with common diseases
Guðjónsson, A., Guðmundsdóttir, V., Axelsson, G. T., Gudmundsson, E. F., Jonsson, B. G., Launer, L. J., Lamb, J. R., Jennings, L. L., Aspelund, T., Emilsson, V., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27850-z · Cited by 290
Advances and Utility of the Human Plasma Proteome
Deutsch, E. W., Omenn, G. S., Sun, Z., Maes, M., Pernemalm, M., Palaniappan, K. K., Letunica, N., Vandenbrouck, Y., Brun, V., Tao, S., et al. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00657 · Cited by 246
Metadata
- Authors
- Raffield, L. M., Dang, H., Pratte, K. A., Jacobson, S., Gillenwater, L. A., Ampleford, E., Barjaktarevic, I., Basta, P., Clish, C. B., Comellas, A. P., Cornell, E., Curtis, J. L., Doerschuk, C., Durda, P., Emson, C., Freeman, C. M., Guo, X., Hastie, A. T., Hawkins, G. A., Herrera, J., Johnson, W. C., Labaki, W. W., Liu, Y., Masters, B., Miller, M., Ortega, V. E., Papanicolaou, G., Peters, S., Taylor, K. D., Rich, S. S., Rotter, J. I., Auer, P., Reiner, A. P., Tracy, R. P., Ngo, D., Gerszten, R. E., O'Neal, W. K., Bowler, R. P., & NHLBI Trans‐Omics for Precision Medicine (TOPMed) Consortium
- Affiliations
- University of North Carolina at Chapel Hill (US)
- National Jewish Health (US)
- Wake Forest University Health Sciences (US)
- UCLA Division of Pulmonary and Critical Care David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095
- Broad Institute (US)
- University of Iowa (US)
- University of Vermont (US)
- University of Michigan (US)
- AstraZeneca (United States) (US)
- The Lundquist Institute (US)
- ProterixBio (United States) (US)
- University of Washington (US)
- National Heart, Lung, and Blood Institute (US)
- University of Virginia (US)
- University of Wisconsin–Milwaukee (US)
- Beth Israel Deaconess Medical Center (US)
- Marsico Lung Institute University of North Carolina at Chapel Hill Chapel Hill NC 27599
- Corresponding author
- Raffield, Laura M.
- ORCID
- Raffield, Laura M.
- Dang, Hong
- Pratte, Katherine A.
- Jacobson, Sean
- Gillenwater, Lucas A.
- Basta, Patricia
- Clish, Clary B.
- Comellas, Alejandro P.
- Curtis, Jeffrey L.
- Doerschuk, Claire
- Durda, Peter
- Emson, Claire
- Freeman, Christine M.
- Guo, Xiuqing
- Hastie, Annette T.
- Johnson, W. Craig
- Labaki, Wassim W.
- Miller, Michael
- Ortega, Victor E.
- Papanicolaou, George
- Peters, Stephen
- Taylor, Kent D.
- Rich, Stephen S.
- Rotter, Jerome I.
- Auer, Paul
- Reiner, Alex P.
- Tracy, Russell P.
- Ngo, Debby
- Gerszten, Robert E.
- Bowler, Russell P.
- Venue
- PROTEOMICS
- Publisher
- Wiley
- Published
- 2020-06
- Volume
- 20
- Issue
- 12
- Pages
- e1900278-e1900278
- ISSN
- 1615-9853 (ISSN-L)
- 1615-9861
- PMC
- PMC7425176
- OpenAlex
- W3022509738
- Cited by
- 184
- References
- 36
- Citations by year
- 2026: 33 · 2025: 31 · 2024: 33 · 2023: 37 · 2022: 29 · 2021: 19 · 2020: 1
- FWCI
- 6.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- PROTEOMICS (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs (Merit Review I01 CX000911)
- National Heart, Lung, and Blood Institute (R01 HL132947, T32 HL007749, T32 HL129982)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Keywords
- SomaScan, aptamer-based proteomics, relative protein quantification, proteomics data integration, chronic obstructive pulmonary disease, myocardial infarction, cis-pQTL
- MeSH terms
- Smokers, Adult, Aged, Aged, 80 and over, Female, Humans, Immunoassay, Male, Middle Aged, Myocardial Infarction, Cohort Studies, Proteome, Pulmonary Disease, Chronic Obstructive, Proteomics
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