CoChat
Sign Up
Cited by 343Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsBioinformatics and Genomic Networks

Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease

Open access
Folkersen, Lasse, Fauman, Eric, Sabater-Lleal, Maria, Strawbridge, Rona J., Frånberg, Mattias, Sennblad, Bengt, Baldassarre, Damiano, Veglia, Fabrizio +26 more·2017·PLOS Genetics
DOI 10.1371/journal.pgen.1006706PMC PMC5393901
Open this paper in CoChat — free accountOpen PDF

Abstract

Recent advances in highly multiplexed immunoassays have allowed systematic large-scale measurement of hundreds of plasma proteins in large cohort studies. In combination with genotyping, such studies offer the prospect to 1) identify mechanisms involved with regulation of protein expression in plasma, and 2) determine whether the plasma proteins are likely to be causally implicated in disease. We report here the results of genome-wide association (GWA) studies of 83 proteins considered relevant to cardiovascular disease (CVD), measured in 3,394 individuals with multiple CVD risk factors. We identified 79 genome-wide significant (p<5e-8) association signals, 55 of which replicated at P<0.0007 in separate validation studies (n = 2,639 individuals). Using automated text mining, manual curation, and network-based methods incorporating information on expression quantitative trait loci (eQTL), we propose plausible causal mechanisms for 25 trans-acting loci, including a potential post-translational regulation of stem cell factor by matrix metalloproteinase 9 and receptor-ligand pairs such as RANK-RANK ligand. Using public GWA study data, we further evaluate all 79 loci for their causal effect on coronary artery disease, and highlight several potentially causal associations. Overall, a majority of the plasma proteins studied showed evidence of regulation at the genetic level. Our results enable future studies of the causal architecture of human disease, which in turn should aid discovery of new drug targets.

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 · 10.1371/journal.pgen.1006706

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?

  • IL6R was selected because it has been widely reported by previous pQTL studies as an example of the successful linking of molecular traits and diseases to discover drug targets.

    “IL6R was selected because it has been widely reported by previous pQTL studies as an example of the successful linking of molecular traits and diseases to discover drug targets50,59.”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [50]

    The Discussion (‘Insights into potential causal involvement of the plasma proteins in CVD’; page unavailable) identifies IL6R as a protein with evidence of potentially causal involvement in coronary artery disease and notes trials of an IL6R inhibitor. The Discussion also says the IL6R pQTL association was previously known and replicated. It does not establish that previous pQTL studies widely reported IL6R as a successful example of drug-target discovery; the inhibitor trials were still underway. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Of the 278 pQTLs, 34 (12.23%) were tested in the Folkersen et al. study; 29 (85.29%) were significant at Benjamini-Hochberg FDR < 0.05 and all significant pQTLs were directionally concordant.

    “12.23% (34/278) pQTLs were tested in the Folkersen et al. study50 and 85.29% (29/34) of the pQTLs were significant (Benjamini–Hochberg FDR < 0.05) and all the significant pQTLs were also directionally concordant with the current study (Supplementary Table S2).”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [50]

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: 35

  1. [2]Gashaw I, Ellinghaus P, Sommer A, Asadullah K (2012) What makes a good drug target? Drug Discov Today 17 Suppl: S24– S30. 22155646
  2. [3]Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, Klemm A, Flicek P, Manolio T, Hindorff L, Parkinson H (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42: D1001– D1006. 10.1093/nar/gkt1229 24316577 PMC3965119 · DOI · Paper page
  3. [4]Enroth S, Johansson A, Enroth SB, Gyllensten U (2014) Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs. Nat Commun 5: 4684 10.1038/ncomms5684 25147954 PMC4143927 · DOI
  4. [5]Enroth S, Bosdotter ES, Johansson A, Gyllensten U (2015) Effect of genetic and environmental factors on protein biomarkers for common non-communicable disease and use of personally normalized plasma protein profiles (PNPPP). Biomarkers 20: 355– 364. 10.3109/1354750X.2015.1093546 26551787 · DOI
  5. [7]Hause RJ, Stark AL, Antao NN, Gorsic LK, Chung SH, Brown CD, Wong SS, Gill DF, Myers JL, To LA, White KP, Dolan ME, Jones RB (2014) Identification and validation of genetic variants that influence transcription factor and cell signaling protein levels. Am J Hum Genet 95: 194– 208. 10.1016/j.ajhg.2014.07.005 25087611 PMC4129400 · DOI · Paper page
  6. [8]Foss EJ, Radulovic D, Shaffer SA, Goodlett DR, Kruglyak L, Bedalov A (2011) Genetic variation shapes protein networks mainly through non-transcriptional mechanisms. PLoS Biol 9: e1001144 10.1371/journal.pbio.1001144 21909241 PMC3167781 · DOI
  7. [9]Sun W, Kechris K, Jacobson S, Drummond MB, Hawkins GA, Yang J, Chen TH, Quibrera PM, Anderson W, Barr RG, Basta PV, Bleecker ER, Beaty T, Casaburi R, Castaldi P, Cho MH, Comellas A, Crapo JD, Criner G, Demeo D, Christenson SA, Couper DJ, Curtis JL, Doerschuk CM, Freeman CM, Gouskova NA, Han MK, Hanania NA, Hansel NN, Hersh CP, Hoffman EA, Kaner RJ, Kanner RE, Kleerup EC, Lutz S, Martinez FJ, Meyers DA, Peters SP, Regan EA, Rennard SI, Scholand MB, Silverman EK, Woodruff PG, O'Neal WK, Bowler RP (2016) Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD. PLoS Genet 12: e1006011 10.1371/journal.pgen.1006011 27532455 PMC4988780 · DOI
  8. [10]Casas JP, Shah T, Cooper J, Hawe E, McMahon AD, Gaffney D, Packard CJ, O'Reilly DS, Juhan-Vague I, Yudkin JS, Tremoli E, Margaglione M, Di MG, Hamsten A, Kooistra T, Stephens JW, Hurel SJ, Livingstone S, Colhoun HM, Miller GJ, Bautista LE, Meade T, Sattar N, Humphries SE, Hingorani AD (2006) Insight into the nature of the CRP-coronary event association using Mendelian randomization. Int J Epidemiol 35: 922– 931. 10.1093/ije/dyl041 16565153 · DOI
  9. [11]Wensley F, Gao P, Burgess S, Kaptoge S, Di AE, Shah T, Engert JC, Clarke R, Davey-Smith G, Nordestgaard BG, Saleheen D, Samani NJ, Sandhu M, Anand S, Pepys MB, Smeeth L, Whittaker J, Casas JP, Thompson SG, Hingorani AD, Danesh J (2011) Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data. BMJ 342: d548 10.1136/bmj.d548 21325005 PMC3039696 · DOI · Paper page
  10. [12]Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, Hindy G, Holm H, Ding EL, Johnson T, Schunkert H, Samani NJ, Clarke R, Hopewell JC, Thompson JF, Li M, Thorleifsson G, Newton-Cheh C, Musunuru K, Pirruccello JP, Saleheen D, Chen L, Stewart A, Schillert A, Thorsteinsdottir U, Thorgeirsson G, Anand S, Engert JC, Morgan T, Spertus J, Stoll M, Berger K, Martinelli N, Girelli D, McKeown PP, Patterson CC, Epstein SE, Devaney J, Burnett MS, Mooser V, Ripatti S, Surakka I, Nieminen MS, Sinisalo J, Lokki ML, Perola M, Havulinna A, de FU, Gigante B, Ingelsson E, Zeller T, Wild P, de Bakker PI, Klungel OH, Maitland-van der Zee AH, Peters BJ, de BA, Grobbee DE, Kamphuisen PW, Deneer VH, Elbers CC, Onland-Moret NC, Hofker MH, Wijmenga C, Verschuren WM, Boer JM, van der Schouw YT, Rasheed A, Frossard P, Demissie S, Willer C, Do R, Ordovas JM, Abecasis GR, Boehnke M, Mohlke KL, Daly MJ, Guiducci C, Burtt NP, Surti A, Gonzalez E, Purcell S, Gabriel S, Marrugat J, Peden J, Erdmann J, Diemert P, Willenborg C, Konig IR, Fischer M, Hengstenberg C, Ziegler A, Buysschaert I, Lambrechts D, Van de Werf F, Fox KA, El Mokhtari NE, Rubin D, Schrezenmeir J, Schreiber S, Schafer A, Danesh J, Blankenberg S, Roberts R, McPherson R, Watkins H, Hall AS, Overvad K, Rimm E, Boerwinkle E, Tybjaerg-Hansen A, Cupples LA, Reilly MP, Melander O, Mannucci PM, Ardissino D, Siscovick D, Elosua R, Stefansson K, O'Donnell CJ, Salomaa V, Rader DJ, Peltonen L, Schwartz SM, Altshuler D, Kathiresan S (2012) Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 380: 572– 580. 10.1016/S0140-6736(12)60312-2 22607825 PMC3419820 · DOI · Paper page
  11. [13]Assarsson E, Lundberg M, Holmquist G, Bjorkesten J, Thorsen SB, Ekman D, Eriksson A, Rennel DE, Ohlsson S, Edfeldt G, Andersson AC, Lindstedt P, Stenvang J, Gullberg M, Fredriksson S (2014) Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One 9: e95192 10.1371/journal.pone.0095192 24755770 PMC3995906 · DOI · Paper page
  12. [14]Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88: 76– 82. 10.1016/j.ajhg.2010.11.011 21167468 PMC3014363 · DOI · Paper page
  13. [15]Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM, Yang J (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 48: 481– 487. 10.1038/ng.3538 27019110 · DOI · Paper page
  14. [16]Folkersen L, van't Hooft F, Chernogubova E, Agardh HE, Hansson GK, Hedin U, Liska J, Syvanen AC, Paulssson-Berne G, Franco-Cereceda A, Hamsten A, Gabrielsen A, Eriksson P (2010) Association of Genetic Risk Variants With Expression of Proximal Genes Identifies Novel Susceptibility Genes for Cardiovascular Disease. Circulation-Cardiovascular Genetics 3: 365– U306. 10.1161/CIRCGENETICS.110.948935 20562444 · DOI
  15. [17]de Boer RA, Verweij N, van Veldhuisen DJ, Westra HJ, Bakker SJ, Gansevoort RT, Muller Kobold AC, van Gilst WH, Franke L, Mateo L, I, van der Harst P (2012) A genome-wide association study of circulating galectin-3. PLoS One 7: e47385 10.1371/journal.pone.0047385 23056639 PMC3467202 · DOI
  16. [20]Melzer D, Perry JR, Hernandez D, Corsi AM, Stevens K, Rafferty I, Lauretani F, Murray A, Gibbs JR, Paolisso G, Rafiq S, Simon-Sanchez J, Lango H, Scholz S, Weedon MN, Arepalli S, Rice N, Washecka N, Hurst A, Britton A, Henley W, van de Leemput J, Li R, Newman AB, Tranah G, Harris T, Panicker V, Dayan C, Bennett A, McCarthy MI, Ruokonen A, Jarvelin MR, Guralnik J, Bandinelli S, Frayling TM, Singleton A, Ferrucci L (2008) A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet 4: e1000072 10.1371/journal.pgen.1000072 18464913 PMC2362067 · DOI · Paper page
  17. [21]Jiang DK, Ma XP, Yu H, Cao G, Ding DL, Chen H, Huang HX, Gao YZ, Wu XP, Long XD, Zhang H, Zhang Y, Gao Y, Chen TY, Ren WH, Zhang P, Shi Z, Jiang W, Wan B, Saiyin H, Yin J, Zhou YF, Zhai Y, Lu PX, Zhang H, Gu X, Tan A, Wang JB, Zuo XB, Sun LD, Liu JO, Yi Q, Mo Z, Zhou G, Liu Y, Sun J, Shugart YY, Zheng SL, Zhang XJ, Xu J, Yu L (2015) Genetic variants in five novel loci including CFB and CD40 predispose to chronic hepatitis B. Hepatology 62: 118– 128. 10.1002/hep.27794 25802187 · DOI
  18. [22]Cheng DT, Kim DK, Cockayne DA, Belousov A, Bitter H, Cho MH, Duvoix A, Edwards LD, Lomas DA, Miller BE, Reynaert N, Tal-Singer R, Wouters EF, Agusti A, Fabbri LM, Rames A, Visvanathan S, Rennard SI, Jones P, Parmar H, MacNee W, Wolff G, Silverman EK, Mayer RJ, Pillai SG (2013) Systemic soluble receptor for advanced glycation endproducts is a biomarker of emphysema and associated with AGER genetic variants in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 188: 948– 957. 10.1164/rccm.201302-0247OC 23947473 · DOI
  19. [23]Lieb W, Chen MH, Larson MG, Safa R, Teumer A, Baumeister SE, Lin H, Smith HM, Koch M, Lorbeer R, Volker U, Nauck M, Volzke H, Wallaschofski H, Sawyer DB, Vasan RS (2015) Genome-wide association study for endothelial growth factors. Circ Cardiovasc Genet 8: 389– 397. 10.1161/CIRCGENETICS.114.000597 25552591 PMC4406801 · DOI
  20. [24]Qi L, Cornelis MC, Kraft P, Jensen M, van Dam RM, Sun Q, Girman CJ, Laurie CC, Mirel DB, Hunter DJ, Rimm E, Hu FB (2010) Genetic variants in ABO blood group region, plasma soluble E-selectin levels and risk of type 2 diabetes. Hum Mol Genet 19: 1856– 1862. 10.1093/hmg/ddq057 20147318 PMC2850622 · DOI
  21. [25]Sanna S, Li B, Mulas A, Sidore C, Kang HM, Jackson AU, Piras MG, Usala G, Maninchedda G, Sassu A, Serra F, Palmas MA, Wood WH III, Njolstad I, Laakso M, Hveem K, Tuomilehto J, Lakka TA, Rauramaa R, Boehnke M, Cucca F, Uda M, Schlessinger D, Nagaraja R, Abecasis GR (2011) Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS Genet 7: e1002198 10.1371/journal.pgen.1002198 21829380 PMC3145627 · DOI
  22. [26]Oriss TB, Krishnamoorthy N, Raundhal M, Morse C, Chakraborty K, Khare A, Huff R, Ray P, Ray A (2014) Cutting Edge: MMP-9 inhibits IL-23p19 expression in dendritic cells by targeting membrane stem cell factor affecting lung IL-17 response. J Immunol 192: 5471– 5475. 10.4049/jimmunol.1303183 24829419 PMC4063408 · DOI
  23. [27]Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T, Sachs KV, Li X, Li H, Kuperwasser N, Ruda VM, Pirruccello JP, Muchmore B, Prokunina-Olsson L, Hall JL, Schadt EE, Morales CR, Lund-Katz S, Phillips MC, Wong J, Cantley W, Racie T, Ejebe KG, Orho-Melander M, Melander O, Koteliansky V, Fitzgerald K, Krauss RM, Cowan CA, Kathiresan S, Rader DJ (2010) From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466: 714– 719. 10.1038/nature09266 20686566 PMC3062476 · DOI · Paper page
  24. [29]Burgess S, Malarstig A (2013) Using Mendelian randomization to assess and develop clinical interventions: limitations and benefits. J Comp Eff Res 2: 209– 212. 10.2217/cer.13.14 24236616 · DOI
  25. [30]Strawbridge RJ, Deleskog A, McLeod O, Folkersen L, Kavousi M, Gertow K, Baldassarre D, Veglia F, Leander K, Gigante B, Kauhanen J, Rauramaa R, Smit AJ, Mannarino E, Giral P, Dehghan A, Hofman A, Franco OH, Humphries SE, Tremoli E, de FU, Gustafsson S, Ostensson CG, Eriksson P, Ohrvik J, Hamsten A (2014) A serum 25-hydroxyvitamin D concentration-associated genetic variant in DHCR7 interacts with type 2 diabetes status to influence subclinical atherosclerosis (measured by carotid intima-media thickness). Diabetologia 57: 1159– 1172. 10.1007/s00125-014-3215-y 24663808 · DOI
  26. [31]Nowak C, Sundstrom J, Gustafsson S, Giedraitis V, Lind L, Ingelsson E, Fall T (2016) Protein Biomarkers for Insulin Resistance and Type 2 Diabetes Risk in Two Large Community Cohorts. Diabetes 65: 276– 284. 10.2337/db15-0881 26420861 PMC5860375 · DOI
  27. [32]Igl W, Johansson A, Gyllensten U (2010) The Northern Swedish Population Health Study (NSPHS)—a paradigmatic study in a rural population combining community health and basic research. Rural Remote Health 10: 1363 20568910
  28. [33]Baldassarre D, Nyyssonen K, Rauramaa R, de FU, Hamsten A, Smit AJ, Mannarino E, Humphries SE, Giral P, Grossi E, Veglia F, Paoletti R, Tremoli E (2010) Cross-sectional analysis of baseline data to identify the major determinants of carotid intima-media thickness in a European population: the IMPROVE study. Eur Heart J 31: 614– 622. 10.1093/eurheartj/ehp496 19952003 · DOI
  29. [34]Isgren A, Jakobsson J, Palsson E, Ekman CJ, Johansson AG, Sellgren C, Blennow K, Zetterberg H, Landen M (2015) Increased cerebrospinal fluid interleukin-8 in bipolar disorder patients associated with lithium and antipsychotic treatment. Brain Behav Immun 43: 198– 204. 10.1016/j.bbi.2014.10.001 25451615 · DOI
  30. [35]Folkersen L, Wagsater D, Paloschi V, Jackson V, Petrini J, Kurtovic S, Maleki S, Eriksson MJ, Caidahl K, Hamsten A, Michel JB, Liska J, Gabrielsen A, Franco-Cereceda A, Eriksson P (2011) Unraveling divergent gene expression profiles in bicuspid and tricuspid aortic valve patients with thoracic aortic dilatation: the ASAP study. Mol Med 17: 1365– 1373. 10.2119/molmed.2011.00286 21968790 PMC3321821 · DOI
  31. [36]Fairfax BP, Makino S, Radhakrishnan J, Plant K, Leslie S, Dilthey A, Ellis P, Langford C, Vannberg FO, Knight JC (2012) Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat Genet 44: 502– 510. 10.1038/ng.2205 22446964 PMC3437404 · DOI · Paper page
  32. [37]Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, Lau E, Jostins L, Plant K, Andrews R, McGee C, Knight JC (2014) Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343: 1246949 10.1126/science.1246949 24604202 PMC4064786 · DOI · Paper page
  33. [38]Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34: 816– 834. 10.1002/gepi.20533 21058334 PMC3175618 · DOI · Paper page
  34. [39]Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, Boehnke M, Abecasis GR, Willer CJ (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26: 2336– 2337. 10.1093/bioinformatics/btq419 20634204 PMC2935401 · DOI · Paper page
  35. [40]Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von MC (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43: D447– D452. 10.1093/nar/gku1003 25352553 PMC4383874 · DOI · Paper page

Cited by

Sorted by citations

Authors
Folkersen, L., Fauman, E., Sabater-Lleal, M., Strawbridge, R. J., Frånberg, M., Sennblad, B., Baldassarre, D., Veglia, F., Humphries, S. E., Rauramaa, R., de Faire, U., Smit, A. J., Giral, P., Kurl, S., Mannarino, E., Enroth, S., Johansson, Å., Enroth, S. B., Gustafsson, S., Lind, L., Lindgren, C., Morris, A. P., Giedraitis, V., Silveira, A., Franco-Cereceda, A., Tremoli, E., IMPROVE study group, Gyllensten, U., Ingelsson, E., Brunak, S., Eriksson, P., Ziemek, D., Hamsten, A., & Mälarstig, A.
Affiliations
  • Karolinska Institutet (SE)
  • Pfizer (United States) (US)
  • University of Milan (IT)
  • Centro Cardiologico Monzino (IT)
  • British Heart Foundation (GB)
  • University Medical Center Groningen (NL)
  • Assistance Publique – Hôpitaux de Paris (FR)
  • University of Eastern Finland (FI)
  • University of Perugia (IT)
  • Uppsala University (SE)
  • Centre for Human Genetics (GB)
  • University of Liverpool (GB)
Corresponding author
Mälarstig, Anders
ORCID
Venue
PLOS Genetics
Publisher
Public Library of Science (PLoS)
Published
2017-04-03
Volume
13
Issue
4
Pages
e1006706
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1006706
PMC
PMC5393901
OpenAlex
W2605261164
Cited by
343
References
4
Citations by year
2026: 36 · 2025: 57 · 2024: 92 · 2023: 38 · 2022: 31 · 2021: 32 · 2020: 24 · 2019: 16 · 2018: 13 · 2017: 4
FWCI
10.28
Type
journal article
Language
en
Update notices
new_version (2017-04-17)
Open access
Licence
Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
  • Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, cc-by)
  • UEF eRepo (University of Eastern Finland) (submittedVersion, cc-by)
  • KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • UEF eRepo (University of Eastern Finland) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Oxford University Research Archive (ORA) (University of Oxford) (submittedVersion, cc-by)
  • Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders
  • European Hematology Association
  • Wellcome Trust (WT098017)
  • Innovationsfonden
  • National Institute for Health and Care Research (PG08/008)
  • British Heart Foundation (PG 08/008, RG/08/008/25291)
  • University College London
  • National Heart, Lung, and Blood Institute (R01HL135313)
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 › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
plasma protein biomarkers, cardiovascular disease, genome-wide association study, expression quantitative trait loci, stem cell factor, MMP-9, coronary artery disease, causal mechanisms, post-translational regulation, drug target identification
MeSH terms
Blood Proteins, Cardiovascular Diseases, Coronary Artery Disease, Female, Humans, Male, Biomarkers, Genetic Predisposition to Disease, Quantitative Trait Loci, 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