Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals
Open accessAbstract
Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health. Folkersen et al. report the first results from the SCALLOP consortium, a collaborative framework for pQTL mapping and biomarker analysis of proteins on the Olink platform. A total of 315 primary and 136 secondary pQTLs for 85 circulating cardiovascular proteins from over 30,000 individuals were identified and replicated to yield new insights for translational studies and drug development.
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.
Integrity signals
Registry
Cites
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.
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?
Most pQTLs to date have been discovered via genome-wide association studies, which predominantly focus on common variants.
“To date, most pQTLs have been discovered via genome-wide association studies (GWASs), which predominantly focus on common variants3,5–8.”
From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [7]
The Results section, “Genome-wide meta-analysis of 90 proteins” (page unavailable), reports discovering hundreds of pQTLs through a GWAS meta-analysis, supporting GWAS as a pQTL discovery method. The paper does not establish that most pQTLs discovered to date came from GWAS or that GWAS predominantly focus on common variants; its Online Methods instead describe filtering by a minimum minor-allele count. (LLM, conf=0.79)
Experimental — accuracy not yet measuredSeveral studies in blood plasma have shown that pQTLs using aptamer-based and antibody-based assays are common and can explain up to 30% of protein variance.
“several studies in blood plasma have consistently shown that genetic variants associated with protein levels, known as protein quantitative trait loci (pQTL), using both aptamer‐based (Sun et al, 2018; Ferkingstad et al, 2021) and antibody‐based assays (Folkersen et al, 2020) are common and can explain up to 30% of the protein variance.”
From The genetic regulation of protein expression in cerebrospinal fluid (2023) · cites this paper as Folkersen et al, 2020
The Results section on genome-wide meta-analysis reports pQTLs for 85 of 90 plasma proteins, and Online Methods describes the antibody-based Olink assay. The Results section on heritability reports substantial variation explained by genetic scores, but our copy does not substantiate the specific claim that pQTLs explain up to 30% of protein variance; it gives different summary figures. (LLM, conf=0.86)
Experimental — accuracy not yet measuredProtein quantitative trait loci (pQTL) are commonly detected in large-scale studies.
“Protein quantitative trait loci (pQTL), or associations between circulating protein abundances and genetic variants either within or nearby (cis-) to the protein-encoding gene or elsewhere in the genome (trans-), are commonly detected in large-scale studies1–6.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [2]
Antibody-based genetic studies have focused on narrower collections of proteins at large scale or broad collections of proteins at smaller scale.
“Antibody-based genetic studies have focused on narrower collections of proteins at large scale 22 or broad collections of proteins at smaller scale 23 .”
From Plasma proteomic associations with genetics and health in the UK Biobank (2023) · cites this paper as [22]
The use of covariate adjustment in the pQTL analyses follows standard procedures.
“This use of covariate adjustment follows standard procedures (Folkersen et al, 2020) in pQTL analyses.”
From The genetic regulation of protein expression in cerebrospinal fluid (2023) · cites this paper as Folkersen et al, 2020
To flag potential pseudo-pQTL signals caused by the epitope effect, the study followed the strategy described by Folkersen et al.
“To flag potential ‘pseudo-pQTL’ signals caused by the epitope effect, i.e. altered assay binding affinity due to a change in protein structure instead of an actual change in protein expression level, we followed the strategy described by Folkersen et al.6.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as Folkersen et al. [6]
The largest pQTL meta-analysis published to date (n = 30,931) was not usable due to sample overlap with the current study.
“The largest pQTL meta-analysis published to date (n = 30,931)6 was conducted through the SCALLOP consortium and was not usable due to sample overlap with the current study.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [6]
Most pQTL studies have been conducted using serum or plasma measurements from blood samples, and only a limited number of studies has involved examination of liver and brain tissue-specific pQTLs.
“Second, most pQTL studies have been conducted using serum or plasma measurements from blood samples4,6,8,51 and only a limited number of studies has involved the examination of liver and brain tissue–specific pQTLs88,89.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [6]
The largest proportion of cis-pQTLs was located in intronic and intergenic regions.
“Consistent with previous findings4,6,8, the largest proportion of cis-pQTLs was located in intronic and intergenic regions.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [6]
The Results section on cis-acting pQTLs reports how many were linked to protein-altering variants, but does not give a distribution across intronic and intergenic regions. Searches for “intronic,” “intergenic,” and genomic annotation of cis-pQTLs found no passage establishing which regions contained the largest proportion. (LLM, conf=0.73)
Experimental — accuracy not yet measured
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
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
Self-citationMETAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · see citing works
Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Kasela, S., et al. (2018) · doi:10.1101/447367 · Cited by 2,607 · see citing works
Self-citationGenetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 43
- [1]Chames P Van Regenmortel M Weiss E Baty D Therapeutic antibodies: successes, limitations and hopes for the future Br J Pharmacol 2009 157 220 233 19459844 PMC2697811
- [2]Holmes MV Ala-Korpela M Smith GD Mendelian randomization in cardiometabolic disease: challenges in evaluating causality Nat Rev Cardiol 2017 14 577 590 28569269 PMC5600813
- [3]Folkersen L Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease PLoS Genet 2017 13 e1006706 28369058 PMC5393901
- [4]Sun BB Genomic atlas of the human plasma proteome Nature 2018 558 73 79 29875488 PMC6697541
- [5]Williams SA Plasma protein patterns as comprehensive indicators of health Nat Med 2019 25 1851 1857 31792462 PMC6922049
- [6]Lehallier B Undulating changes in human plasma proteome profiles across the lifespan Nat Med 2019 25 1843 1850 31806903 PMC7062043
- [7]Enroth S Johansson A Enroth SB Gyllensten U Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs Nat Commun 2014 5 4684 25147954 PMC4143927
- [8]Emilsson V Co-regulatory networks of human serum proteins link genetics to disease Science 2018 361 769 773 30072576 PMC6190714
- [9]Melzer D A genome-wide association study identifies protein quantitative trait loci (pQTLs) PLoS Genet 2008 4 e1000072 18464913 PMC2362067
- [10]Assarsson E Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability PLoS One 2014 9 e95192 24755770 PMC3995906
- [11]Gamazon ER A gene-based association method for mapping traits using reference transcriptome data Nat Genet 2015 47 1091 1098 26258848 PMC4552594
- [12]Zhu Z Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets Nat Genet 2016 48 481 487 27019110
- [13]Sun W Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD PLoS Genet 2016 12 e1006011 27532455 PMC4988780
- [14]Chick JM Defining the consequences of genetic variation on a proteome-wide scale Nature 2016 534 500 505 27309819 PMC5292866
- [15]Zhernakova DV Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome Nat Genet 2018 50 1524 1532 30250126 PMC6241851
- [16]Solomon T Identification of Common and Rare Genetic Variation Associated With Plasma Protein Levels Using Whole-Exome Sequencing and Mass Spectrometry Circ Genom Precis Med 2018 11 e002170 30562114 PMC6301071
- [17]Cabre A Fatty acid binding protein 4 is increased in metabolic syndrome and with thiazolidinedione treatment in diabetic patients Atherosclerosis 2007 195 e150 158 17553506
- [18]Nishimoto N Mechanisms and pathologic significances in increase in serum interleukin-6 (IL-6) and soluble IL-6 receptor after administration of an anti-IL-6 receptor antibody, tocilizumab, in patients with rheumatoid arthritis and Castleman disease Blood 2008 112 3959 3964 18784373
- [19]Gustot T Profile of soluble cytokine receptors in Crohn’s disease Gut 2005 54 488 495 15753533 PMC1774443
- [20]Gale JD Effect of PF-04634817, an Oral CCR2/5 Chemokine Receptor Antagonist, on Albuminuria in Adults with Overt Diabetic Nephropathy Kidney Int Rep 2018 3 1316 1327 30450458 PMC6224665
- [21]Bashore AC Targeted Deletion of Hepatocyte Abca1 Increases Plasma HDL (High-Density Lipoprotein) Reverse Cholesterol Transport via the LDL (Low-Density Lipoprotein) Receptor Arterioscler Thromb Vasc Biol 2019 39 1747 1761 31167565 PMC6703909
- [22]Burkhardt R Trib1 is a lipid- and myocardial infarction-associated gene that regulates hepatic lipogenesis and VLDL production in mice J Clin Invest 2010 120 4410 4414 21084752 PMC2993600
- [23]Rosa M A Mendelian randomization study of IL6 signaling in cardiovascular diseases, immune-related disorders and longevity NPJ Genom Med 2019 4 23 31552141 PMC6754413
- [24]Interleukin 1 Genetics, C Cardiometabolic effects of genetic upregulation of the interleukin 1 receptor antagonist: a Mendelian randomisation analysis Lancet Diabetes Endocrinol 2015 3 243 253 25726324 PMC4648058
- [25]Mahdessian H Integrative studies implicate matrix metalloproteinase-12 as a culprit gene for large-artery atherosclerotic stroke J Intern Med 2017 282 429 444 28734077
- [26]Kaplanski G Interleukin-18: Biological properties and role in disease pathogenesis Immunol Rev 2018 281 138 153 29247988 PMC7165732
- [27]Heilig R The Gasdermin-D pore acts as a conduit for IL-1beta secretion in mice Eur J Immunol 2018 48 584 592 29274245
- [28]Autiero M Role of PlGF in the intra- and intermolecular cross talk between the VEGF receptors Flt1 and Flk1 Nat Med 2003 9 936 943 12796773
- [29]Dri P TNF-Induced shedding of TNF receptors in human polymorphonuclear leukocytes: role of the 55-kDa TNF receptor and involvement of a membrane-bound and non-matrix metalloproteinase J Immunol 2000 165 2165 2172 10925303
- [30]Tenenhouse HS Sabbagh Y Novel phosphate-regulating genes in the pathogenesis of renal phosphate wasting disorders Pflugers Arch 2002 444 317 326 12111239
- [31]Xie JH Engineering of a novel anti-CD40L domain antibody for treatment of autoimmune diseases J Immunol 2014 192 4083 4092 24670803
- [32]de Miguel D Lemke J Anel A Walczak H Martinez-Lostao L Onto better TRAILs for cancer treatment Cell Death Differ 2016 23 733 747 26943322 PMC4832109
- [33]Holmes MV Davey Smith G Can Mendelian Randomization Shift into Reverse Gear? Clin Chem 2019 65 363 366 30692117
- [34]McCarthy CP Januzzi JL Jr Soluble ST2 in Heart Failure Heart Fail Clin 2018 14 41 48 29153199
- [36]Yang J Lee SH Goddard ME Visscher PM GCTA: a tool for genome-wide complex trait analysis Am J Hum Genet 2011 88 76 82 21167468 PMC3014363
- [38]Tigchelaar EF Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics BMJ Open 2015 5 e006772 PMC4554905 26319774
- [39]Consortium GT Genetic effects on gene expression across human tissues Nature 2017 550 204 213 29022597 PMC5776756
- [41]Westra HJ Systematic identification of trans eQTLs as putative drivers of known disease associations Nat Genet 2013 45 1238 1243 24013639 PMC3991562
- [42]Lloyd-Jones LR The Genetic Architecture of Gene Expression in Peripheral Blood Am J Hum Genet 2017 100 371 PMC5802995 28157541
- [43]McRae AF Identification of 55,000 Replicated DNA Methylation QTL Scientific Reports 2018 8 Article number: 17605 PMC6279736 30514905
- [45]Vilhjalmsson BJ Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores Am J Hum Genet 2015 97 576 592 26430803 PMC4596916
- [46]Sudlow C UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age PLoS Med 2015 12 e1001779 25826379 PMC4380465
- [47]Bycroft C The UK Biobank resource with deep phenotyping and genomic data Nature 2018 562 203 209 30305743 PMC6786975
Cited by
Sorted by citations
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 1,740 · see citing works
Self-citationLarge-scale integration of the plasma proteome with genetics and disease
Ferkingstad, E., Sulem, P., Atlason, B. A., Sveinbjornsson, G., Magnusson, M. I., Styrmisdottir, E. L., Gunnarsdottir, K., Helgason, A., Oddsson, A., Halldorsson, B. V., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00978-w · Cited by 1,697 · see citing works
Self-citationSynergistic insights into human health from aptamer- and antibody-based proteomic profiling
Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Kerrison, N. D., Oerton, E., Koprulu, M., Luan, J., Hingorani, A. D., Williams, S. A., Wareham, N. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27164-0 · Cited by 234 · see citing works
Rare variant associations with plasma protein levels in the UK Biobank
Dhindsa, R. S., Burren, O. S., Sun, B. B., Prins, B. P., Matelska, D., Wheeler, E., Mitchell, J., Oerton, E., Hristova, V. A., Smith, K. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06547-x · Cited by 193 · see citing works
Self-citationHolmes, M. V., Richardson, T. G., Ference, B. A., Davies, N. M., & Davey Smith, G. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00493-1 · Cited by 184 · see citing works
Self-citationRepurposing drugs to treat cardiovascular disease in the era of precision medicine
Abdelsayed, M., Kort, E. J., Jovinge, S., & Mercola, M. (2022) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00717-6 · Cited by 93 · see citing works
Schuermans, A., Pournamdari, A. B., Lee, J., Bhukar, R., Ganesh, S., Darosa, N., Small, A. M., Yu, Z., Hornsby, W., Koyama, S., et al. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00567-0 · Cited by 71 · see citing works
Self-citationHuang, Y., Liu, Y., Ma, Y., Tu, T., Liu, N., Bai, F., Xiao, Y., Liu, C., Hu, Z., Lin, Q., et al. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.840866 · Cited by 54 · see citing works
The genetic regulation of protein expression in cerebrospinal fluid
Hansson, O., Kumar, A., Janelidze, S., Stomrud, E., Insel, P. S., Blennow, K., Zetterberg, H., Fauman, E., Hedman, Å. K., Nagle, M. W., et al. (2023) · EMBO Molecular Medicine · doi:10.15252/emmm.202216359 · Cited by 56 · see citing works
Self-citationTeyssonnière, E. M., Trébulle, P., Muenzner, J., Loegler, V., Ludwig, D., Amari, F., Mülleder, M., Friedrich, A., Hou, J., Ralser, M., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2319211121 · Cited by 40 · see citing works
Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers
Smith-Byrne, K., Hedman, Å., Dimitriou, M., Desai, T., Sokolov, A. V., Schioth, H. B., Koprulu, M., Pietzner, M., Langenberg, C., Atkins, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46834-3 · Cited by 33 · see citing works
Self-citationLemmelä, S., Wigmore, E. M., Benner, C., Havulinna, A. S., Ong, R. M., Kempf, T., Wollert, K. C., Blankenberg, S., Zeller, T., Peters, J. E., et al. (2022) · eLife · doi:10.7554/elife.76272 · Cited by 28 · see citing works
Self-citationKierczak, M., Rafati, N., Höglund, J., Gourlé, H., Lo Faro, V., Schmitz, D., Ek, W. E., Gyllensten, U., Enroth, S., Ekman, D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30208-8 · Cited by 22 · see citing works
Self-citationAn in-depth review of AI-powered advancements in cancer drug discovery
Le, M. H. N., Nguyen, P. K., Nguyen, T. P. T., Nguyen, H. Q., Tam, D. N. H., Huynh, H. H., Huynh, P. K., & Le, N. Q. K. (2025) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2025.167680 · Cited by 15 · see citing works
Cross-ancestry comparison of aptamer and antibody protein measures
Nicholas, J. C., Katz, D. H., Tahir, U. A., Debban, C. L., Aguet, F., Blackwell, T., Bowler, R. P., Broadaway, K. A., Chen, J., Clish, C. B., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-67814-1 · Cited by 13 · see citing works
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
Self-citation
- Authors
- Folkersen, L., Gustafsson, S., Wang, Q., Hansen, D. H., Hedman, Å. K., Schork, A., Page, K., Zhernakova, D. V., Wu, Y., Peters, J., Eriksson, N., Bergen, S. E., Boutin, T. S., Bretherick, A. D., Enroth, S., Kalnapenkis, A., Gådin, J. R., Suur, B. E., Chen, Y., Matic, L., Gale, J. D., Lee, J., Zhang, W., Quazi, A., Ala-Korpela, M., Choi, S. H., Claringbould, A., Danesh, J., Davey Smith, G., de Masi, F., Elmståhl, S., Engström, G., Fauman, E., Fernandez, C., Franke, L., Franks, P. W., Giedraitis, V., Haley, C., Hamsten, A., Ingason, A., Johansson, Å., Joshi, P. K., Lind, L., Lindgren, C. M., Lubitz, S., Palmer, T., Macdonald-Dunlop, E., Magnusson, M., Melander, O., Michaelsson, K., Morris, A. P., Mägi, R., Nagle, M. W., Nilsson, P. M., Nilsson, J., Orho-Melander, M., Polasek, O., Prins, B., Pålsson, E., Qi, T., Sjögren, M., Sundström, J., Surendran, P., Võsa, U., Werge, T., Wernersson, R., Westra, H.-J., Yang, J., Zhernakova, A., Ärnlöv, J., Fu, J., Smith, J. G., Esko, T., Hayward, C., Gyllensten, U., Landen, M., Siegbahn, A., Wilson, J. F., Wallentin, L., Butterworth, A. S., Holmes, M. V., Ingelsson, E., & Mälarstig, A.
- Affiliations
- Karolinska Institutet (SE)
- Uppsala University (SE)
- Baker Heart and Diabetes Institute (AU)
- Pfizer (United States) (US)
- Translational Genomics Research Institute (US)
- University Medical Center Groningen (NL)
- The University of Queensland (AU)
- University of Cambridge (GB)
- Western General Hospital (GB)
- University of Tartu (EE)
- Broad Institute (US)
- Lund University (SE)
- University of Edinburgh (GB)
- Lancaster University (GB)
- Centre for Human Genetics (GB)
- University of Gothenburg (SE)
- Sankt Hans Hospital (DK)
- University of Oxford (GB)
- Stanford Medicine (US)
- Corresponding author
- Mälarstig, Anders
- ORCID
- Folkersen, Lasse
- Gustafsson, Stefan
- Wang, Qin
- Hansen, Daniel Hvidberg
- Hedman, Åsa K.
- Schork, Andrew
- Page, Karen
- Zhernakova, Daria V.
- Wu, Yang
- Peters, James
- Eriksson, Niclas
- Bergen, Sarah E.
- Boutin, Thibaud S.
- Bretherick, Andrew D.
- Enroth, Stefan
- Kalnapenkis, Anette
- Gådin, Jesper R.
- Suur, Bianca E.
- Chen, Yan
- Matic, Ljubica
- Gale, Jeremy D.
- Lee, Julie
- Zhang, Weidong
- Ala-Korpela, Mika
- Choi, Seung Hoan
- Claringbould, Annique
- Danesh, John
- Davey Smith, George
- Elmståhl, Sölve
- Engström, Gunnar
- Fauman, Eric
- Fernandez, Celine
- Franke, Lude
- Franks, Paul W.
- Giedraitis, Vilmantas
- Haley, Chris
- Hamsten, Anders
- Johansson, Åsa
- Joshi, Peter K.
- Lind, Lars
- Lindgren, Cecilia M.
- Lubitz, Steven
- Palmer, Tom
- Macdonald-Dunlop, Erin
- Magnusson, Martin
- Melander, Olle
- Michaelsson, Karl
- Morris, Andrew P.
- Mägi, Reedik
- Nagle, Michael W.
- Nilsson, Peter M.
- Nilsson, Jan
- Prins, Bram
- Pålsson, Erik
- Qi, Ting
- Sundström, Johan
- Surendran, Praveen
- Võsa, Urmo
- Werge, Thomas
- Wernersson, Rasmus
- Westra, Harm-Jan
- Yang, Jian
- Zhernakova, Alexandra
- Ärnlöv, Johan
- Fu, Jingyuan
- Esko, Tõnu
- Hayward, Caroline
- Gyllensten, Ulf
- Landen, Mikael
- Siegbahn, Agneta
- Wilson, James F.
- Wallentin, Lars
- Butterworth, Adam S.
- Holmes, Michael V.
- Ingelsson, Erik
- Mälarstig, Anders
- Venue
- Nature Metabolism
- Published
- 2020-10-16
- Volume
- 2
- Issue
- 10
- Pages
- 1135-1148
- ISSN
- 2522-5812 (ISSN-L)
- PMC
- PMC7611474
- OpenAlex
- W3093451235
- Cited by
- 705
- References
- 4
- Citations by year
- 2026: 83 · 2025: 139 · 2024: 188 · 2023: 148 · 2022: 102 · 2021: 43 · 2020: 1
- FWCI
- 43.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Metabolism (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- Explore Bristol Research (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Bristol Research (University of Bristol) (acceptedVersion, other-oa)
- Lund University Publications (Lund University) (submittedVersion, other-oa)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Boloka Institutional Repository (North-west University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Apollo
- Funders
- American Heart Association (H2020, 18SFRN34250007)
- University of Pennsylvania
- NHS Blood and Transplant (NIHR BTRU-2014-10024)
- Wellcome Trust (204979/Z/16/Z)
- Scottish Government (CZB/4/710)
- National Institute for Health and Care Research (NIHR BTRU‐2014‐10024, BTRU-2014–10024, MC_UU_00011/1)
- British Heart Foundation (MC_UU_00011/1, RG/18/13/33946, 18/23/33512, SP/09/002, RG/13/13/30194, FS/18/23/33512, 2009-1039)
- University of Cambridge
- European Commission (018947, 2014‐2020, 2020 ‘, EU-H2020, 715772, H2020, CoG-2015_681742_NASCENT, 692145, LSHG-CT-2006-018947)
- University of Bristol (MC_UU_00011/1)
- Stiftelsen för Strategisk Forskning (Dnr 349-2006-237, 2009-1039, 349-2006-237, Dnr 2009-1039, Dnr IRC15‐0067, IRC15-0067)
- Eesti Teadusagentuur (2014-2020.4.01.16-0125, 2014-2020, 2014-2020.4.01.15-0012, PUT1660)
- Cambridge University Hospitals
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VI.Veni.194.006, 024.003.001, 194.006, 864.13.013, Exposome‐NL 024.004.017, 024.003, 016.178.056, 917.14.374, 024.004.017, 637640)
- Lunds Universitet (2009-1039, IRC15-0067)
- Lundbeckfonden (R155-2014-1724, R302-2018-2155)
- Hjärt-Lungfonden (2016-0134, 2017-00644, 2015-03257, 2017-02554, 2017-06100, 2016-0315)
- Knut och Alice Wallenbergs Stiftelse
- Stiftelsen Olle Engkvist Byggmästare (2017/49)
- Vetenskapsrådet (2017-02554, 349-2006-237, 2015–03257, 2017-06100, 2006–237, 2009-1039, IRC15-0067, 2017-00644, 2015-05997, Dnr 349-2006-237, Dnr2009‐1039, 2018-, Dnr IRC15-0067)
- Forskningsrådet om Hälsa, Arbetsliv och Välfärd (2009-1039, 2017‐06100, 2017‐00644, 2017-00721, 349-2006-237)
- Tartu Ülikool
- Novo Nordisk Fonden (NNF17OC0027034)
- Skånes universitetssjukhus (2009-1039, IRC15-0067)
- Versus Arthritis
- European Research Area Network on Cardiovascular Diseases
- Oncode Institute
- National Institutes of Health (2009-1039, FS/18/23/33512, 024.004.017, 18SFRN34250007, H2020)
- Perelman School of Medicine, University of Pennsylvania
- Wake Forest School of Medicine
- Horizon 2020 Framework Programme (692145, EU‐H2020)
- NIHR BioResource
- Medical Research Council (RG/13/13/30194, LSHG-CT-2006-018947, MR/L003120/1, MR/S003746/1, MR/S004068/2, 1938560, MR/S004068/1, MC_UU_00007/10, MC_UU_00011/1, MR/L003120, MC_UU_00011)
- National Health and Medical Research Council (1158958, MC_UU_00011/1)
- European Regional Development Fund (2014-2020’, 2014-2020.4.01.16-0125, H2020, 2014-2020.4.01.15-0012)
- NIHR Cambridge Biomedical Research Centre
- National Heart, Lung, and Blood Institute (R01HL139731)
- H2020 European Research Council (637640, 715772, 679242)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- protein quantitative trait loci, circulating proteins, pathway mapping, drug repurposing, Mendelian randomization, biomarkers, ABCA1, TRIB1, CCR2, CCR5, epidermal growth factor, interleukin-16, PAPP-A, SPON1, F3, ADM, YKL-40, CXCL16, GDF15, MMP-12
- MeSH terms
- Interleukin-1 Receptor-Like 1 Protein, Asthma, Cardiovascular System, Chromosome Mapping, Humans, Inflammatory Bowel Diseases, Linkage Disequilibrium, Drug Delivery Systems, Protein Serine-Threonine Kinases, Receptors, CCR5, Proteome, Genomics, Quantitative Trait Loci, Intracellular Signaling Peptides and Proteins, Receptors, CCR2, Genome-Wide Association Study, Gene Knockdown Techniques, Mendelian Randomization Analysis, ATP Binding Cassette Transporter 1
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
