Genetic regulation of the human plasma proteome in 54,306 UK Biobank participants
Open accessPreprint — published as 10.1038/s41586-023-06592-6 · Paper page
Abstract
The UK Biobank Pharma Proteomics Project (UKB-PPP) is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,306 UKB participants. Here, we describe results from the first phase of UKB-PPP, including protein quantitative trait loci (pQTL) mapping of 1,463 proteins that identifies 10,248 primary genetic associations, of which 85% are newly discovered. We also identify independent secondary associations in 92% of cis and 29% of trans loci, expanding the catalogue of genetic instruments for downstream analyses. The study provides an updated characterisation of the genetic architecture of the plasma proteome, leveraging population-scale proteomics to provide novel, extensive insights into trans pQTLs across multiple biological domains. We highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement proteins, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug target discovery by extending the genetic proxied effect of PCSK9 levels on lipid concentrations, cardio- and cerebro-vascular diseases, and additionally disentangle specific genes and proteins perturbed at COVID-19 susceptibility loci. This public-private partnership provides the scientific community with an open-access proteomics resource of unprecedented breadth and depth to help elucidate biological mechanisms underlying genetic discoveries and accelerate the development of novel biomarkers and therapeutics.
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The largest pQTL study to date was conducted with 35,571 samples and resulted in the detection of 10,248 independent autosomal pQTLs for 1463 proteins.
“We extended the analysis to include data from the largest pQTL study to date, conducted with 35,571 samples and resulting in the detection of 10,248 independent autosomal pQTLs for 1463 proteins9.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
The Discovery of pQTLs section reports 34,557 discovery participants, 14,287 significant primary associations, and 2,414 proteins with at least one pQTL—not the figures in the claim. The Replication of pQTLs section also reports an analysis of 52,363 participants, so the cited study is not described by the claimed sample size. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe 95% CSs contained an average of 15.7 variants in our study and 22.7 variants (9.6 cis and 29.4 trans) in Sun et al.
“The 95% CSs contained an average of 15.7 variants in our study and 22.7 variants (9.6 cis and 29.4 trans) in that of Sun et al.9.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
The cited paper’s “Identification and fine-mapping of independent signals” section reports an average of 20.5 variants per 95% credible set, with means of 9.7 for cis and 26.7 for trans signals—not the attributed 22.7, 9.6, and 29.4. The 15.7 figure describes the citing authors’ own study and is not attributed to this source. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredSun et al. reported that approximately 5.6% (570/10,248) and 1.5% (155/10,248) of the variants with primary associations had MAFs < 0.01 and < 0.005, respectively.
“Sun et al.9 reported that approximately 5.6% (570/10,248) and 1.5% (155/10,248) of the variants with primary associations had MAFs < 0.01 and < 0.005, respectively.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as Sun et al. [9]
The Discovery of pQTLs section (page unavailable) reports nearly the same proportions at the stated MAF thresholds, but different counts: 783/14,287 (5.5%) below 1% and 235/14,287 (1.6%) below 0.5%. It does not support the cited numerators or denominator. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe associations were strongest for significant cis-pQTL variants located nearest to the TSSs of the relevant protein genes.
“In concordance with previous studies4,9,65, there was an inverse relationship between the effect size and MAF (Fig. 2b), and the associations were the strongest for significant cis-pQTL variants located nearest to the TSSs of the relevant protein genes (Fig. 2c).”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
The Discovery of pQTLs section (page unavailable) reports that most cis associations lie within or near the relevant protein gene’s start site. It does not report that associations are strongest for variants nearest the transcription start site, so the claimed relationship between proximity and strength is unsupported. (LLM, conf=0.78)
Experimental — accuracy not yet measuredSun et al. fine mapped CSs in 127 (67.6%) genetic regions for 117 proteins, matching our findings.
“and Sun et al.9 fine mapped CSs in 127 (67.6%) genetic regions for 117 proteins, matching our findings.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
The ‘Identification and fine-mapping of independent signals’ section (page unavailable) says Sun et al. fine-mapped 95% credible sets for pQTL signals. It does not report the attributed figures of 127 regions, 67.6%, or 117 proteins; those specific figures are unsupported by our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
The study detected 76 tagging SNP-CNV pairs for 33 unique CNVs and 72 proteins from the Sun et al. 2022 study.
“We also detected 76 tagging SNP–CNV pairs for 33 unique CNVs and 72 proteins (Supplementary Table S18) from a more recent Sun et al. 2022 study9.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
There was an inverse relationship between effect size and MAF.
“In concordance with previous studies4,9,65, there was an inverse relationship between the effect size and MAF (Fig. 2b), and the associations were the strongest for significant cis-pQTL variants located nearest to the TSSs of the relevant protein genes (Fig. 2c).”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
Only a limited number of pQTL studies have conducted fine mapping as a post-GWAS analysis.
“Only a limited number of pQTL studies have conducted fine mapping9,71 as one of the post-GWAS analyses.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
Of the 29 proteins from Sun et al. 2022 also measured in EstBB, six had significant CNV pQTLs (P < 1.12 × 10-7).
“Twenty-nine (40.3%) of the proteins were also measured in the EstBB cohort, of which six proteins had significant CNV pQTLs (P < 1.12 × 10–7).”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe SIRPB1 protein level was associated with a different CNV than was its gene expression.
“based on Sun et al. tagging-CNV analysis, the SIRPB1 protein level was associated with a different CNV than was its gene expression.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
The Identification and fine-mapping of independent signals and Co-localization with expression QTLs sections discuss protein and gene-expression associations generally, but do not identify different CNVs associated with SIRPB1 protein level and gene expression. Searches for SIRPB1, tagging CNVs, and copy-number variation found no passage addressing that comparison in our copy; page numbers were unavailable. (LLM, conf=0.72)
Experimental — accuracy not yet measuredOnly SNP-based cis associations with GDF-15 itself have been identified in previous pQTL studies.
“only SNP-based cis associations with GDF-15 itself have been identified in previous pQTL studies9,86.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
The 'Comparisons with previous pQTL studies' section reports prior cis and trans associations in aggregate, but does not identify which were associated with GDF-15. Searches for GDF15, GDF-15, and growth differentiation factor 15 found no passage in our copy establishing that previous studies identified only cis associations for this protein. (LLM, conf=0.76)
Experimental — accuracy not yet measuredCNV-based pQTLs of the MICA-MICB heterodimer and SIRPA were not associated with the same CNVs in the EstBB cohort as tagged by SNPs in Sun et al. 2022.
“However, CNV-based pQTLs of the MICA-MICB heterodimer and SIRPA were not associated with the same CNVs in the EstBB cohort as tagged by SNPs in Sun et al.’s9 study.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [9]
The ‘Identification and fine-mapping of independent signals’ section reports genotype-based pQTL analyses, but the passages retrieved do not identify SNPs tagging CNVs for MICA-MICB or SIRPA. Searches for those proteins, CNVs, and EstBB found no passage addressing the specific comparison in the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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Self-citationGenetic determinants of plasma protein levels in the Estonian population
Kalnapenkis, A., Jõeloo, M., Lepik, K., Kukuškina, V., Kals, M., Alasoo, K., Estonian Biobank Research Team, Metspalu, A., Milani, L., Nelis, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-57966-3 · Cited by 11 · see citing works
- Authors
- 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., Hou, L., Kvikstad, E. M., Burren, O. S., Cule, M., Davitte, J., Ferber, K. L., Gillies, C. E., Hedman, Å. K., Hu, S., Lin, T., Mikkilineni, R., Pendergrass, R. K., Pickering, C., Prins, B., Raj, A., Robinson, J., Sethi, A., Ward, L. D., Welsh, S., Willis, C. M., Genetics, A. H., Initiative, A. G., Biogen Biobank Team, Squibb, B. M., Genetics, G. H., Sciences, G. G., Biology, P. I., Sciences, P. A. O. J. D., Regeneron Genetics Center, Burkitt-Gray, L., Black, M. H., Fauman, E. B., Howson, J. M. M., Kang, H. M., McCarthy, M. I., Melamud, E., Nioi, P., Petrovski, S., Scott, R. A., Smith, E. N., Szalma, S., Waterworth, D. M., Mitnaul, L. J., Szustakowski, J. D., Gibson, B. W., Miller, M. R., & Whelan, C. D.
- Affiliations
- Biogen (United States) (US)
- Pfizer (United States) (US)
- Genentech (US)
- Amgen (United States) (US)
- GlaxoSmithKline (United Kingdom) (GB)
- GlaxoSmithKline (United States) (US)
- Janssen Research & Development (United States) (US)
- Bristol-Myers Squibb (United States) (US)
- AstraZeneca (United Kingdom) (GB)
- Calico Life Sciences (US)
- Regeneron (United States) (US)
- Pfizer (Sweden) (SE)
- Takeda (United States) (US)
- UK Biobank (GB)
- Alnylam Pharmaceuticals (United States) (US)
- Corresponding author
- Sun, Benjamin B.
- Whelan, Christopher D.
- ORCID
- Sun, Benjamin B.
- Chiou, Joshua
- Traylor, Matthew
- Benner, Christian
- Hsu, Yi-Hsiang
- Richardson, Tom G.
- Surendran, Praveen
- Mahajan, Anubha
- Robins, Chloe
- Vasquez-Grinnell, Steven G.
- Hou, Liping
- Kvikstad, Erika M.
- Burren, Oliver S.
- Cule, Madeleine
- Davitte, Jonathan
- Ferber, Kyle L.
- Gillies, Christopher E.
- Hedman, Åsa K.
- Hu, Sile
- Lin, Tinchi
- Mikkilineni, Rajesh
- Pendergrass, Rion K.
- Pickering, Corran
- Prins, Bram
- Raj, Anil
- Robinson, Jamie
- Sethi, Anurag
- Ward, Lucas D.
- Welsh, Samantha
- Willis, Carissa M.
- Burkitt-Gray, Lucy
- Black, Mary Helen
- Fauman, Eric B.
- Howson, Joanna M. M.
- Kang, Hyun Min
- McCarthy, Mark I.
- Melamud, Eugene
- Nioi, Paul
- Petrovski, Slavé
- Scott, Robert A.
- Smith, Erin N.
- Szalma, Sándor
- Waterworth, Dawn M.
- Szustakowski, Joseph D.
- Gibson, Bradford W.
- Miller, Melissa R.
- Whelan, Christopher D.
- Venue
- Nature
- Publisher
- openRxiv
- Published
- 2022-06-18
- Volume
- 622
- Issue
- 7982
- Pages
- 329-338
- ISSN
- 0028-0836 (ISSN-L)
- PMC
- PMC10567551
- OpenAlex
- W4387344289
- Cited by
- 1,833
- References
- 1
- Citations by year
- 2026: 777 · 2025: 688 · 2024: 311 · 2023: 52 · 2022: 5
- FWCI
- 292.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
- Funders
- Amgen
- Bristol-Myers Squibb
- Pfizer
- AstraZeneca
- GlaxoSmithKline
- Biogen
- Alnylam Pharmaceuticals
- Regeneron Pharmaceuticals
- Novo Nordisk
- Genentech
- Medical Research Council (MC_PC_17228)
- 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 › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- plasma proteome, protein quantitative trait loci, ligand-receptor interactions, cytokines, complement system, ABO blood group system, PCSK9, COVID-19 susceptibility, disease signatures, predictive modeling, UK Biobank
- MeSH terms
- ABO Blood-Group System, Proprotein Convertase 9, COVID-19, Galactoside 2-alpha-L-fucosyltransferase, Blood Proteins, Epistasis, Genetic, Fucosyltransferases, United Kingdom, Health, Humans, Plasma, Databases, Factual, Biological Specimen Banks, Genetic Predisposition to Disease, Proteome, Genomics, Quantitative Trait Loci, Proteomics, Drug Discovery, Public-Private Sector Partnerships
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