CoChat
Sign Up
Cited by 14Genetic Associations and EpidemiologyAdvanced Proteomics Techniques and ApplicationsGenetic Mapping and Diversity in Plants and Animals

A genome-wide association study of mass spectrometry proteomics using a nanoparticle enrichment platform

Open access
Suhre, Karsten, Chen, Qingwen, Halama, Anna, Mendez, Kevin, Dahlin, Amber, Stephan, Nisha, Thareja, Gaurav, Sarwath, Hina +6 more·2025·Nature Genetics
DOI 10.1038/s41588-025-02413-wPMC PMC12695657
Open this paper in CoChat — free accountOpen PDF

Abstract

Most studies to date of protein quantitative trait loci (pQTLs) have relied on affinity proteomics platforms, which provide only limited information about the targeted protein isoforms and may be affected by genetic variation in their epitope binding. Here we show that mass spectrometry (MS)-based proteomics can complement these studies and provide insights into the role of specific protein isoform and epitope-altering variants. Using the Seer Proteograph nanoparticle enrichment MS platform, we identified and replicated new pQTLs in a genome-wide association study of proteins in blood plasma samples from two cohorts and evaluated previously reported pQTLs from affinity proteomics platforms. We found that >30% of the evaluated pQTLs were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein abundance, whereas another 30% could not be replicated and are possibly due to epitope effects, although alternative explanations for nonreplication need to be considered on a case-by-case basis.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41588-025-02413-w

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Several cis-pQTL signals driven by PAVs-most commonly missense variants-were observed on each platform.

    “While the majority of cis-pQTL variants were non-coding on both platforms (Supplementary Fig. 8), we observe several cis-pQTL signals driven by protein altering variants (PAVs—most commonly missense variants) on each platform, raising concerns as to whether the signal captures abundances or differences in isoform, as has been shown in other studies18,24,25.”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [18]

    The Results section, ‘Validity of the MSPA score’ (page not provided), reports candidate epitope-changing coding variants for cis-pQTLs on both SOMAscan and Olink, including detected PAV-containing peptides. This supports the presence of PAV-related signals on both platforms, but the passage does not establish that missense variants were the most common PAVs or that every candidate variant drove its signal. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • 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 [18]

    The Discussion states that PAV-containing peptides can produce an epitope-effect pQTL that does not fully reflect genotype-dependent protein abundance. The Results (Validity of the MSPA score) likewise identify coding variants that could alter epitopes. The paper discusses affinity binders targeting different isoforms as another possible explanation, but does not specifically establish that PAVs cause differential probe binding to alternate isoforms. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • PAV-driven cis-pQTL signals raise concerns as to whether the signal captures protein abundances or differences in isoform.

    “While the majority of cis-pQTL variants were non-coding on both platforms (Supplementary Fig. 8), we observe several cis-pQTL signals driven by protein altering variants (PAVs—most commonly missense variants) on each platform, raising concerns as to whether the signal captures abundances or differences in isoform, as has been shown in other studies18,24,25.”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [18]

    The Discussion says PAV-containing peptides can produce a pQTL signal that does not fully reflect genotype-dependent protein abundance. The Results section on protein isoforms separately shows that a SPINK5 pQTL can reflect differences among isoforms, but it does not identify that example as PAV-driven. The source therefore supports both interpretive concerns, but not the specific link between PAV-driven signals and isoform differences. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Recent MS-based pQTL studies have suggested that some pQTL may reflect isoform differences.

    “Recent MS-based pQTL studies10,18 have further suggested that some pQTL may reflect isoform differences (although these studies are limited in number and generally encompass fewer proteins/participants).”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [18]

  • Comparing affinity-based pQTL to pQTL identified in MS studies may facilitate signal interpretation.

    “While MS-based pQTL studies are relatively limited in size and number (with some of the broadest studies, used for pQTL comparison here, currently including 1260 Americans and 1980 proteins18, or 325 participants and ~2800 proteins10), comparing affinity-based pQTL to pQTL identified in MS studies may facilitate signal interpretation.”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [18]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 34

  1. [1]Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581–594 (2013). 23868113 · DOI · Paper page
  2. [2]Mountjoy, E. et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat. Genet. 53, 1527–1533 (2021). 34711957 PMC7611956 · DOI · Paper page
  3. [3]Minikel, E. V., Painter, J. L., Dong, C. C. & Nelson, M. R. Refining the impact of genetic evidence on clinical success. Nature 629, 624–629 (2024). 38632401 PMC11096124 · DOI
  4. [4]Carss, K. J. et al. Using human genetics to improve safety assessment of therapeutics. Nat. Rev. Drug Discov. 22, 145–162 (2023). 36261593 · DOI
  5. [5]Suhre, K., McCarthy, M. I. & Schwenk, J. M. Genetics meets proteomics: perspectives for large population-based studies. Nat. Rev. Genet. 22, 19–37 (2021). 32860016 · DOI · Paper page
  6. [6]Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017). 28240269 PMC5333359 · DOI · Paper page
  7. [7]Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018). 29875488 PMC6697541 · DOI · Paper page
  8. [8]Lourdusamy, A. et al. Identification of cis-regulatory variation influencing protein abundance levels in human plasma. Hum. Mol. Genet. 21, 3719–3726 (2012). 22595970 PMC6446535 · DOI
  9. [9]Enroth, S., Johansson, A., Enroth, S. B. & Gyllensten, U. Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs. Nat. Commun. 5, 4684 (2014). 25147954 PMC4143927 · DOI
  10. [10]Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018). 30072576 PMC6190714 · DOI · Paper page
  11. [11]Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021). 34648354 PMC9904207 · DOI · Paper page
  12. [12]Gudjonsson, A. et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nat. Commun. 13, 480 (2022). 35078996 PMC8789779 · DOI
  13. [13]Zhao, J. H. et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat. Immunol. 24, 1540–1551 (2023). 37563310 PMC10457199 · DOI
  14. [14]Carland, C. et al. Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases. Clin. Proteom. 20, 31 (2023). PMC10405520 37550624 · DOI
  15. [15]Folkersen, L. et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat. Metab. 2, 1135–1148 (2020). 33067605 PMC7611474 · DOI · Paper page
  16. [16]Sun, B. B., Suhre, K. & Gibson, B. W. Promises and challenges of populational proteomics in health and disease. Mol. Cell Proteom. 23, 100786 (2024). PMC11193116 38761890 · DOI
  17. [20]Xu, F. et al. Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility. Nat. Commun. 14, 896 (2023). 36797296 PMC9935862 · DOI
  18. [21]Niu, L. et al. Plasma proteome variation and its genetic determinants in children and adolescents. Nat. Genet. 57, 635–646 (2025). 39972214 PMC11906355 · DOI
  19. [22]Newton-Cheh, C. et al. Association of common variants in NPPA and NPPB with circulating natriuretic peptides and blood pressure. Nat. Genet. 41, 348–353 (2009). 19219041 PMC2664511 · DOI
  20. [23]Suhre, K. et al. Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping. Nat. Commun. 15, 989 (2024). 38307861 PMC10837160 · DOI · Paper page
  21. [24]Liu, Y. et al. Nano-bio interactions in cancer: from therapeutics delivery to early detection. Acc. Chem. Res. 54, 291–301 (2021). 33180454 · DOI
  22. [25]Ferdosi, S. et al. Enhanced competition at the nano-bio interface enables comprehensive characterization of protein corona dynamics and deep coverage of proteomes. Adv. Mater. 34, e2206008 (2022). 35986672 · DOI
  23. [26]Nonnecke, E. B. et al. Human intelectin-1 (ITLN1) genetic variation and intestinal expression. Sci. Rep. 11, 12889 (2021). 34145348 PMC8213764 · DOI
  24. [27]Ochoa, D. et al. Open Targets Platform: supporting systematic drug-target identification and prioritisation. Nucleic Acids Res. 49, D1302–D1310 (2021). 33196847 PMC7779013 · DOI
  25. [28]Wang, H. et al. Genome-wide interaction analysis of pathological hallmarks in Alzheimer’s disease. Neurobiol. Aging 93, 61–68 (2020). 32450446 PMC9795865 · DOI
  26. [30]Sun, Q. et al. The genomic and phenotypic landscape of ichthyosis: an analysis of 1000 kindreds. JAMA Dermatol. 158, 16–25 (2022). 34851365 PMC8637393 · DOI
  27. [31]Tartaglia-Polcini, A. et al. SPINK5, the defective gene in Netherton syndrome, encodes multiple LEKTI isoforms derived from alternative pre-mRNA processing. J. Invest. Dermatol. 126, 315–324 (2006). 16374478 · DOI
  28. [32]Martin, F. J. et al. Ensembl 2023. Nucleic Acids Res. 51, D933–D941 (2022). PMC9825606 36318249 · DOI
  29. [33]Chen, Q. et al. OMICmAge: an integrative multi-omics approach to quantify biological age with electronic medical records. Preprint at bioRxiv10.1101/2023.10.16.562114 (2023).
  30. [34]Mook-Kanamori, D. O. et al. 1,5-Anhydroglucitol in saliva is a noninvasive marker of short-term glycemic control. J. Clin. Endocrinol. Metab. 99, E479–E483 (2014). 24423354 · DOI
  31. [35]Yousri, N. A. et al. A systems view of type 2 diabetes-associated metabolic perturbations in saliva, blood and urine at different timescales of glycaemic control. Diabetologia 58, 1855–1867 (2015). 26049400 PMC4499109 · DOI
  32. [36]Meier, F. et al. diaPASEF: parallel accumulation—serial fragmentation combined with data-independent acquisition. Nat. Methods 17, 1229–1236 (2020). 33257825 · DOI
  33. [37]Demichev, V. et al. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nat. Commun. 13, 3944 (2022). 35803928 PMC9270362 · DOI
  34. [38]Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014). 24830394 PMC4022491 · DOI · Paper page

Cited by

Sorted by citations

Authors
Suhre, K., Chen, Q., Halama, A., Mendez, K., Dahlin, A., Stephan, N., Thareja, G., Sarwath, H., Guturu, H., Dwaraka, V. B., Smith, R., Batzoglou, S., Schmidt, F., & Lasky-Su, J. A.
Affiliations
  • Weill Cornell Medical College in Qatar (QA)
  • Brigham and Women's Hospital (US)
Corresponding author
  • Suhre, Karsten
  • Lasky-Su, Jessica A.
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2025-11-27
Volume
57
Issue
12
Pages
2987-2996
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-025-02413-w
PMC
PMC12695657
OpenAlex
W4416735647
Cited by
14
References
4
Citations by year
2026: 13 · 2025: 1
FWCI
8.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Genetics (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
Funders
U.S. Department of Health & Human Services | National Institutes of Health (R01HL155742)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
protein quantitative trait loci, pQTL, mass spectrometry-based proteomics, nanoparticle enrichment, plasma proteins, protein isoforms, genetic variants, protein abundance
MeSH terms
Humans, Mass Spectrometry, Protein Isoforms, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Proteomics, Nanoparticles, 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 ›

Are you an author, or do you see a problem with this page? Request a review