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Large-scale plasma proteomics comparisons through genetics and disease associations

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Eldjarn, Grimur Hjorleifsson, Ferkingstad, Egil, Lund, Sigrun H., Helgason, Hannes, Magnusson, Olafur Th., Gunnarsdottir, Kristbjorg, Olafsdottir, Thorunn A., Halldorsson, Bjarni V. +25 more·2023·Nature
DOI 10.1038/s41586-023-06563-xPMC PMC10567571
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Abstract

High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project 1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people 2 , for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.

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  • PAVs may result in pQTL associations that reflect differential probe binding to alternate isoforms rather than differential protein abundances.

    “Nevertheless, interpreting cis- and trans-pQTL remains complicated, as PAVs may result in pQTL associations that reflect differential probe binding to alternate isoforms12,17–19 rather than differential protein abundances.”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [17]

    The ‘Relationship of pQTLs between platforms’ section says PAV-linked cis-pQTLs are likely to reflect epitope effects rather than variation in protein levels. The Discussion also says the platforms may target different proteoforms, but it does not specifically link PAV-driven binding differences to alternate isoforms. Page numbers were not available in our copy. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Platform-concordant cis-pQTL signals were most likely to contain a GTEx v8 eQTL lead variant for the protein-encoding gene in the credible set, compared to platform-specific or platform-discordant signals.

    “Consistent with prior reports17,19, we found that platform-concordant cis-pQTL signals were most likely to contain a GTEx v8 eQTL lead variant for the protein-encoding gene in the credible set, compared to platform-specific or platform-discordant signals.”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [17]

    The ‘Relationship of pQTLs between platforms’ section reports that some cis-pQTL variants are in high LD with cis-eQTLs for the targeted protein’s gene. The Methods also say the authors checked correspondence with top cis-eQTLs from sources including GTEx. Our copy does not report that platform-concordant signals are more likely than platform-specific or discordant signals to contain a GTEx v8 eQTL lead variant in a credible set. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • In the UK BioBank/deCODE SomaScan and Olink platform-comparison, significant platform-specific pleiotropic trans-pQTL were seen at some of the same regions, such as CFH and VTN for SomaScan protein measures, and proteins associated with these loci often showed lower inter-platform correlation compared to other proteins.

    “The same trend has been observed in the UK BioBank/deCODE SomaScan and Olink platform-comparison, where significant platform-specific pleiotropic trans-pQTL were seen at some of the same regions, such as complement factor gene CFH and glycoprotein-encoding VTN for SomaScan protein measures, and proteins associated with these loci often showed lower inter-platform correlation compared to other”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [17]

    Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • The 644 proteins with a cis-pQTL detected on both platforms had higher inter-platform correlation (mean Pearson r = 0.58, SD = 0.20) than proteins with no significant cis-pQTL (mean r = 0.21, SD = 0.30).

    “Similar to prior studies12,17, the 644 proteins with a cis-pQTL detected on both platforms had higher inter-platform correlation (mean Pearson’s r = 0.58, SD = 0.20) than proteins with no significant cis-pQTL (mean Pearson’s r = 0.21, SD = 0.30)(Fig. 3C and Supplementary Data 7).”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [17]

  • Similar proportions of protein measures were associated with any cis-pQTL on each platform (39% SomaScan, 48% Olink), with more distinct cis-pQTL identified for Olink overall.

    “We observed similar proportions of protein measures associated with any cis-pQTL on each platform (39% SomaScan, 48% Olink), with more distinct cis-pQTL identified for Olink overall (for the set of 2157 proteins measured on both platforms), consistent with single-ancestry genetic comparisons of earlier versions of SomaScan and Olink platforms12,16,17.”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [17]

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Authors
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., Gudjonsson, S. A., Sveinbjornsson, G., Magnusson, M. I., Helgason, A., Oddsson, A., Halldorsson, G. H., Magnusson, M. K., Saevarsdottir, S., Eiriksdottir, T., Masson, G., Stefansson, H., Jonsdottir, I., Holm, H., Rafnar, T., Melsted, P., Saemundsdottir, J., Norddahl, G. L., Thorleifsson, G., Ulfarsson, M. O., Gudbjartsson, D. F., Thorsteinsdottir, U., Sulem, P., & Stefansson, K.
Affiliations
deCODE Genetics (Iceland) (IS)
Corresponding author
Sulem, Patrick
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2023-10-04
Volume
622
Issue
7982
Pages
348-358
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-023-06563-x
PMC
PMC10567571
OpenAlex
W4387344358
Cited by
583
References
15
Citations by year
2026: 245 · 2025: 217 · 2024: 111 · 2023: 10
FWCI
96.95
Type
journal article
Language
en
Update notices
correction (2024-05-22)
Open access
Licence
Locations
  • Nature (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Opin vísindi (Opin vísindi) (submittedVersion, other-oa)
  • Opin vísindi (Opin vísindi) (submittedVersion, cc-by-nc-sa)
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
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
Keywords
plasma proteomics, protein quantitative trait loci, genetic association studies, UK Biobank, Icelandic population, cis-eQTL, disease association
MeSH terms
Asia, Southern, Africa, Blood Proteins, Disease Susceptibility, Genotype, United Kingdom, Humans, Iceland, Ireland, Phenotype, Plasma, Genome, Human, Biological Specimen Banks, Proteome, Genomics, Quantitative Trait Loci, Proteomics, Datasets as Topic

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