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Cited by 48Biomarkers in Disease MechanismsAdipokines, Inflammation, and Metabolic DiseasesComplement system in diseases

C-reactive protein upregulates the whole blood expression of CD59 - an integrative analysis

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Lepik, Kaido, Annilo, Tarmo, Kukuškina, Viktorija, eQTLGen Consortium, Kisand, Kai, Kutalik, Zoltán, Peterson, Pärt, Peterson, Hedi·2017·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1005766PMC PMC5609773
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Abstract

Elevated C-reactive protein (CRP) concentrations in the blood are associated with acute and chronic infections and inflammation.Nevertheless, the functional role of increased CRP in multiple bacterial and viral infections as well as in chronic inflammatory diseases remains unclear.Here, we studied the relationship between CRP and gene expression levels in the blood in 491 individuals from the Estonian Biobank cohort, to elucidate the role of CRP in these inflammatory mechanisms.As a result, we identified a set of 1,614 genes associated with changes in CRP levels with a high proportion of interferon-stimulated genes.Further, we performed likelihood-based causality model selection and Mendelian randomization analysis to discover causal links between CRP and the expression of CRP-associated genes.Strikingly, our computational analysis and cell culture stimulation assays revealed increased CRP levels to drive the expression of complement regulatory protein CD59, suggesting CRP to have a critical role in protecting blood cells from the adverse effects of the immune defence system.Our results show the benefit of integrative analysis approaches in hypothesis-free uncovering of causal relationships between traits. Author summaryChronic inflammation is associated with chronic diseases, morbidity and mortality while lower base inflammation levels are thought to be predictive of healthy aging.Thus, to pursue a long and healthy lifespan, it is essential to understand the inflammatory regulatory mechanisms.To that end, we studied the functional role of C-reactive protein (CRP)-an inflammatory biomarker that is used to measure cardiovascular risk in clinical practice.There is evidence for a strong genetic component of elevated CRP levels but it is still unclear if it has a direct impact on the processes that lead to inflammatory diseases.In

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  • To account for hidden batch effects on gene expression, the first two principal components of the gene expression data were included as covariates, as described in Lepik et al.

    “To account for hidden batch effects on the gene expression, the first two principal components of the gene expression data were also included as covariates, as described in detail in Lepik et al.29.”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as Lepik et al. [29]

  • In the pQTL-eQTL colocalisation analysis, significant pQTL loci were compared to eQTL Catalogue datasets, excluding those of Kasela et al. and Lepik et al. due to sample overlap.

    “In an pQTL–eQTL colocalisation analysis, we compared our significant pQTL loci to all eQTL Catalogue datasets18, excluding those of Kasela et al.57 and Lepik et al.29 due to sample overlap, containing gene expression, exon expression, transcript usage and txrevise event usage data, and GTEx (version 8)58 datasets containing gene expression data”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as Lepik et al. [29]

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  1. [1]Black S, Kushner I, Samols D. C-reactive Protein. J Biol Chem. 2004; 279(47): 48487– 90. Epub 2004/08/26. doi: 10.1074/jbc.R400025200. 15337754 · DOI · Paper page
  2. [2]Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA. 2001; 286(3): 327– 34.. 11466099
  3. [3]Ridker PM, Stampfer MJ, Rifai N. Novel risk factors for systemic atherosclerosis: a comparison of C-reactive protein, fibrinogen, homocysteine, lipoprotein(a), and standard cholesterol screening as predictors of peripheral arterial disease. JAMA. 2001; 285(19): 2481– 5.. 11368701
  4. [4]Albert CM, Ma J, Rifai N, Stampfer MJ, Ridker PM. Prospective study of C-reactive protein, homocysteine, and plasma lipid levels as predictors of sudden cardiac death. Circulation. 2002; 105(22): 2595– 9.. 12045163
  5. [6]Dehghan A, Dupuis J, Barbalic M, Bis JC, Eiriksdottir G, Lu C, et al Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation. 2011; 123(7): 731– 8. doi: 10.1161/CIRCULATIONAHA.110.948570; PubMed Central PMCID: PMCPMC3147232. 21300955 PMC3147232 · DOI
  6. [7]Balbin OA, Prensner JR, Sahu A, Yocum A, Shankar S, Malik R, et al Reconstructing targetable pathways in lung cancer by integrating diverse omics data. Nat Commun. 2013; 4: 2617 doi: 10.1038/ncomms3617; PubMed Central PMCID: PMCPMC4107456. 24135919 PMC4107456 · DOI
  7. [8]Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, et al Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet. 2013; 45(11): 1345– 52. Epub 2013/10/06. doi: 10.1038/ng.2795; PubMed Central PMCID: PMCPMC3904346. 24097064 PMC3904346 · DOI · Paper page
  8. [11]Li R, Tsaih SW, Shockley K, Stylianou IM, Wergedal J, Paigen B, et al Structural model analysis of multiple quantitative traits. PLoS Genet. 2006; 2(7): e114 Epub 2006/06/07. doi: 10.1371/journal.pgen.0020114; PubMed Central PMCID: PMCPMC1513264. 16848643 PMC1513264 · DOI
  9. [12]Liu B, de la Fuente A, Hoeschele I. Gene network inference via structural equation modeling in genetical genomics experiments. Genetics. 2008; 178(3): 1763– 76. Epub 2008/02/03. doi: 10.1534/genetics.107.080069; PubMed Central PMCID: PMCPMC2278111. 18245846 PMC2278111 · DOI
  10. [13]Neto EC, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, et al Modeling causality for pairs of phenotypes in system genetics. Genetics. 2013; 193(3): 1003– 13. Epub 2013/01/03. doi: 10.1534/genetics.112.147124; PubMed Central PMCID: PMCPMC3583988. 23288936 PMC3583988 · DOI
  11. [14]Chaibub Neto E, Ferrara CT, Attie AD, Yandell BS. Inferring causal phenotype networks from segregating populations. Genetics. 2008; 179(2): 1089– 100. Epub 2008/05/27. doi: 10.1534/genetics.107.085167; PubMed Central PMCID: PMCPMC2429862. 18505877 PMC2429862 · DOI
  12. [15]Millstein J, Zhang B, Zhu J, Schadt EE. Disentangling molecular relationships with a causal inference test. BMC Genet. 2009; 10: 23 Epub 2009/05/27. doi: 10.1186/1471-2156-10-23; PubMed Central PMCID: PMCPMC3224661. 19473544 PMC3224661 · DOI
  13. [16]Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, et al Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013; 31(2): 142– 7. Epub 2013/01/20. doi: 10.1038/nbt.2487; PubMed Central PMCID: PMCPMC3598632. 23334450 PMC3598632 · DOI
  14. [17]Aten JE, Fuller TF, Lusis AJ, Horvath S. Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Syst Biol. 2008; 2: 34 Epub 2008/04/15. doi: 10.1186/1752-0509-2-34; PubMed Central PMCID: PMCPMC2387136. 18412962 PMC2387136 · DOI · Paper page
  15. [18]Neto EC, Keller MP, Attie AD, Yandell BS. CAUSAL GRAPHICAL MODELS IN SYSTEMS GENETICS: A UNIFIED FRAMEWORK FOR JOINT INFERENCE OF CAUSAL NETWORK AND GENETIC ARCHITECTURE FOR CORRELATED PHENOTYPES. Ann Appl Stat. 2010; 4(1): 320– 39.; PubMed Central PMCID: PMCPMC3017382. 21218138 PMC3017382
  16. [19]Timpson NJ, Nordestgaard BG, Harbord RM, Zacho J, Frayling TM, Tybjærg-Hansen A, et al C-reactive protein levels and body mass index: elucidating direction of causation through reciprocal Mendelian randomization. Int J Obes (Lond). 2011; 35(2): 300– 8. Epub 2010/08/17. doi: 10.1038/ijo.2010.137; PubMed Central PMCID: PMCPMC4783860. 20714329 PMC4783860 · DOI · Paper page
  17. [20]Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013; 37(7): 658– 65. Epub 2013/09/20. doi: 10.1002/gepi.21758; PubMed Central PMCID: PMCPMC4377079. 24114802 PMC4377079 · DOI · Paper page
  18. [22]Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016; 48(5): 481– 7. Epub 2016/03/28. doi: 10.1038/ng.3538. 27019110 · DOI · Paper page
  19. [23]Kruglyak L, Storey JD. Cause and express. Nat Biotechnol. 2009; 27(6): 544– 5. doi: 10.1038/nbt0609-544. 19513058 · DOI
  20. [24]Leitsalu L, Haller T, Esko T, Tammesoo ML, Alavere H, Snieder H, et al Cohort Profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int J Epidemiol. 2015; 44(4): 1137– 47. Epub 2014/02/11. doi: 10.1093/ije/dyt268. 24518929 · DOI
  21. [26]Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014; 15(2): R29 Epub 2014/02/03. doi: 10.1186/gb-2014-15-2-r29; PubMed Central PMCID: PMCPMC4053721. 24485249 PMC4053721 · DOI · Paper page
  22. [27]Seo M, Kim K, Yoon J, Jeong JY, Lee HJ, Cho S, et al RNA-seq analysis for detecting quantitative trait-associated genes. Sci Rep. 2016; 6: 24375 Epub 2016/04/13. doi: 10.1038/srep24375; PubMed Central PMCID: PMCPMC4829873. 27071914 PMC4829873 · DOI
  23. [28]Rusinova I, Forster S, Yu S, Kannan A, Masse M, Cumming H, et al Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res. 2013; 41(Database issue): D1040– 6. Epub 2012/11/29. doi: 10.1093/nar/gks1215; PubMed Central PMCID: PMCPMC3531205. 23203888 PMC3531205 · DOI · Paper page
  24. [29]O'Sullivan J, Bitu CC, Daly SB, Urquhart JE, Barron MJ, Bhaskar SS, et al Whole-Exome sequencing identifies FAM20A mutations as a cause of amelogenesis imperfecta and gingival hyperplasia syndrome. Am J Hum Genet. 2011; 88(5): 616– 20. Epub 2011/05/05. doi: 10.1016/j.ajhg.2011.04.005; PubMed Central PMCID: PMCPMC3146735. 21549343 PMC3146735 · DOI
  25. [30]Jaureguiberry G, De la Dure-Molla M, Parry D, Quentric M, Himmerkus N, Koike T, et al Nephrocalcinosis (enamel renal syndrome) caused by autosomal recessive FAM20A mutations. Nephron Physiol. 2012; 122(1–2): 1– 6. Epub 2013/02/23. doi: 10.1159/000349989; PubMed Central PMCID: PMCPMC3782194. 23434854 PMC3782194 · DOI
  26. [31]Reimand J, Arak T, Adler P, Kolberg L, Reisberg S, Peterson H, et al g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016; 44(W1): W83– 9. Epub 2016/04/20. doi: 10.1093/nar/gkw199; PubMed Central PMCID: PMCPMC4987867. 27098042 PMC4987867 · DOI · Paper page
  27. [32]Consortium G. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015; 348(6235): 648– 60. Epub 2015/05/07. doi: 10.1126/science.1262110; PubMed Central PMCID: PMCPMC4547484. 25954001 PMC4547484 · DOI · Paper page
  28. [33]Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013; 45(10): 1238– 43. Epub 2013/09/08. doi: 10.1038/ng.2756; PubMed Central PMCID: PMCPMC3991562. 24013639 PMC3991562 · DOI · Paper page
  29. [37]Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2015 Epub 2015/08/17. doi: 10.1177/0962280215597579. 26282889 PMC5642006 · DOI
  30. [38]Pasaniuc B, Zaitlen N, Shi H, Bhatia G, Gusev A, Pickrell J, et al Fast and accurate imputation of summary statistics enhances evidence of functional enrichment. Bioinformatics. 2014; 30(20): 2906– 14. Epub 2014/07/01. doi: 10.1093/bioinformatics/btu416; PubMed Central PMCID: PMCPMC4184260. 24990607 PMC4184260 · DOI
  31. [39]Lemaitre RN, Tanaka T, Tang W, Manichaikul A, Foy M, Kabagambe EK, et al Genetic loci associated with plasma phospholipid n-3 fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. PLoS Genet. 2011; 7(7): e1002193 Epub 2011/07/28. doi: 10.1371/journal.pgen.1002193; PubMed Central PMCID: PMCPMC3145614. 21829377 PMC3145614 · DOI
  32. [40]Mozaffarian D, Kabagambe EK, Johnson CO, Lemaitre RN, Manichaikul A, Sun Q, et al Genetic loci associated with circulating phospholipid trans fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. Am J Clin Nutr. 2015; 101(2): 398– 406. Epub 2014/12/10. doi: 10.3945/ajcn.114.094557; PubMed Central PMCID: PMCPMC4307209. 25646338 PMC4307209 · DOI
  33. [41]Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014; 42(Database issue): D1001– 6. Epub 2013/12/06. doi: 10.1093/nar/gkt1229; PubMed Central PMCID: PMCPMC3965119. 24316577 PMC3965119 · DOI · Paper page
  34. [42]Chang MK, Binder CJ, Torzewski M, Witztum JL. C-reactive protein binds to both oxidized LDL and apoptotic cells through recognition of a common ligand: Phosphorylcholine of oxidized phospholipids. Proc Natl Acad Sci U S A. 2002; 99(20): 13043– 8. Epub 2002/09/20. doi: 10.1073/pnas.192399699; PubMed Central PMCID: PMCPMC130583. 12244213 PMC130583 · DOI
  35. [43]Tugirimana P, Speeckaert MM, Fiers T, De Buyzere ML, Kint J, Benoit D, et al Agglutination of intravenously administered phosphatidylcholine-containing lipid emulsions with serum C-reactive protein. Nutr Clin Pract. 2013; 28(2): 253– 9. Epub 2013/03/01. doi: 10.1177/0884533612474040. 23454723 · DOI
  36. [44]Martinelli N, Girelli D, Malerba G, Guarini P, Illig T, Trabetti E, et al FADS genotypes and desaturase activity estimated by the ratio of arachidonic acid to linoleic acid are associated with inflammation and coronary artery disease. Am J Clin Nutr. 2008; 88(4): 941– 9.. 18842780
  37. [45]Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, et al Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet. 2009; 41(1): 47– 55. Epub 2008/12/07. doi: 10.1038/ng.269; PubMed Central PMCID: PMCPMC2687074. 19060911 PMC2687074 · DOI
  38. [46]Fumagalli M, Moltke I, Grarup N, Racimo F, Bjerregaard P, Jørgensen ME, et al Greenlandic Inuit show genetic signatures of diet and climate adaptation. Science. 2015; 349(6254): 1343– 7. doi: 10.1126/science.aab2319. 26383953 · DOI
  39. [47]Ference BA, Ginsberg HN, Graham I, Ray KK, Packard CJ, Bruckert E, et al Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2017 Epub 2017/04/24. doi: 10.1093/eurheartj/ehx144. 28444290 PMC5837225 · DOI · Paper page
  40. [48]Davies A, Simmons DL, Hale G, Harrison RA, Tighe H, Lachmann PJ, et al CD59, an LY-6-like protein expressed in human lymphoid cells, regulates the action of the complement membrane attack complex on homologous cells. J Exp Med. 1989; 170(3): 637– 54.; PubMed Central PMCID: PMCPMC2189447. 2475570 PMC2189447
  41. [49]Wu G, Hu W, Shahsafaei A, Song W, Dobarro M, Sukhova GK, et al Complement regulator CD59 protects against atherosclerosis by restricting the formation of complement membrane attack complex. Circ Res. 2009; 104(4): 550– 8. Epub 2009/01/08. doi: 10.1161/CIRCRESAHA.108.191361; PubMed Central PMCID: PMCPMC4267695. 19131645 PMC4267695 · DOI
  42. [50]Li SH, Szmitko PE, Weisel RD, Wang CH, Fedak PW, Li RK, et al C-reactive protein upregulates complement-inhibitory factors in endothelial cells. Circulation. 2004; 109(7): 833– 6. Epub 2004/02/16. doi: 10.1161/01.CIR.0000117087.27524.0E. 14967730 · DOI
  43. [51]van den Berg CW, Taylor KE. Letter regarding article by Li et al, "C-reactive protein upregulates complement-inhibitory factors in endothelial cells.". Circulation. 2004; 110(25): e542 doi: 10.1161/01.CIR.0000150402.43562.C4. 15611377 · DOI
  44. [52]Schneider WM, Chevillotte MD, Rice CM. Interferon-stimulated genes: a complex web of host defenses. Annu Rev Immunol. 2014; 32: 513– 45. Epub 2014/02/06. doi: 10.1146/annurev-immunol-032713-120231; PubMed Central PMCID: PMCPMC4313732. 24555472 PMC4313732 · DOI · Paper page
  45. [53]Meri S, Waldmann H, Lachmann PJ. Distribution of protectin (CD59), a complement membrane attack inhibitor, in normal human tissues. Lab Invest. 1991; 65(5): 532– 7.. 1721667
  46. [54]Nevo Y, Ben-Zeev B, Tabib A, Straussberg R, Anikster Y, Shorer Z, et al CD59 deficiency is associated with chronic hemolysis and childhood relapsing immune-mediated polyneuropathy. Blood. 2013; 121(1): 129– 35. Epub 2012/11/13. doi: 10.1182/blood-2012-07-441857. 23149847 · DOI
  47. [56]Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013; 42(5): 1497– 501. doi: 10.1093/ije/dyt179; PubMed Central PMCID: PMCPMC3807619. 24159078 PMC3807619 · DOI · Paper page
  48. [57]Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, et al Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016; 48(3): 245– 52. Epub 2016/02/08. doi: 10.1038/ng.3506; PubMed Central PMCID: PMCPMC4767558. 26854917 PMC4767558 · DOI · Paper page
  49. [58]Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30(15): 2114– 20. Epub 2014/04/01. doi: 10.1093/bioinformatics/btu170; PubMed Central PMCID: PMCPMC4103590. 24695404 PMC4103590 · DOI · Paper page
  50. [60]Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29(1): 15– 21. Epub 2012/10/25. doi: 10.1093/bioinformatics/bts635; PubMed Central PMCID: PMCPMC3530905. 23104886 PMC3530905 · DOI · Paper page
  51. [61]Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015; 4: 7 Epub 2015/02/25. doi: 10.1186/s13742-015-0047-8; PubMed Central PMCID: PMCPMC4342193. 25722852 PMC4342193 · DOI · Paper page
  52. [62]Westra HJ, Jansen RC, Fehrmann RS, te Meerman GJ, van Heel D, Wijmenga C, et al MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects. Bioinformatics. 2011; 27(15): 2104– 11. Epub 2011/06/07. doi: 10.1093/bioinformatics/btr323. 21653519 · DOI
  53. [63]Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43(7): e47 Epub 2015/01/20. doi: 10.1093/nar/gkv007; PubMed Central PMCID: PMCPMC4402510. 25605792 PMC4402510 · DOI · Paper page
  54. [64]Novembre J, Johnson T, Bryc K, Kutalik Z, Boyko AR, Auton A, et al Genes mirror geography within Europe. Nature. 2008; 456(7218): 98– 101. Epub 2008/08/31. doi: 10.1038/nature07331; PubMed Central PMCID: PMCPMC2735096. 18758442 PMC2735096 · DOI · Paper page
  55. [65]Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010; 11(3): R25 Epub 2010/03/02. doi: 10.1186/gb-2010-11-3-r25; PubMed Central PMCID: PMCPMC2864565. 20196867 PMC2864565 · DOI · Paper page
  56. [66]Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1): 139– 40. Epub 2009/11/11. doi: 10.1093/bioinformatics/btp616; PubMed Central PMCID: PMCPMC2796818. 19910308 PMC2796818 · DOI · Paper page
  57. [68]Stegle O, Parts L, Durbin R, Winn J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput Biol. 2010; 6(5): e1000770 Epub 2010/05/06. doi: 10.1371/journal.pcbi.1000770; PubMed Central PMCID: PMCPMC2865505. 20463871 PMC2865505 · DOI · Paper page
  58. [69]Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics. 2016; 32(10): 1479– 85. Epub 2015/12/26. doi: 10.1093/bioinformatics/btv722; PubMed Central PMCID: PMCPMC4866519. 26708335 PMC4866519 · DOI

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Authors
Lepik, K., Annilo, T., Kukuškina, V., eQTLGen Consortium, Kisand, K., Kutalik, Z., Peterson, P., & Peterson, H.
Affiliations
  • SIB Swiss Institute of Bioinformatics (CH)
  • University of Tartu (EE)
  • Quretec (Estonia) (EE)
Corresponding author
Lepik, Kaido
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2017-09-18
Volume
13
Issue
9
Pages
e1005766
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1005766
PMC
PMC5609773
OpenAlex
W2755966781
Cited by
48
References
9
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2026: 7 · 2025: 8 · 2024: 5 · 2023: 9 · 2022: 4 · 2021: 10 · 2020: 5
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0.60
Type
journal article
Language
en
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new_version (2017-09-22)
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Funders
  • National Science Foundation
  • European Commission (2014‐2020, 2020 ‘, 2014‐2020.4.01.15‐0012, 15-0012, H2020, 692145)
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (51RTP0_151019, 169929)
  • Eesti Teadusagentuur (IUT20-60, 2014-2020, 2014-2020.4.01.15-0012, SP1GVARENG, IUT2-2, IUT34-4)
  • Tartu Ülikool (SP1GVARENG, 2014-2020.4.01.15-0012)
  • SystemsX.ch (51RTP0 151019)
  • Horizon 2020 Framework Programme (692145)
  • European Regional Development Fund (2014-2020’, H2020, 2014-2020.4.01.15-0012, (Project No. 2014-2020.4.01.15-0012), 15-0012, No. 2014-2020.4.01.15-0012, IUT2-2)
Topics
  • Life Sciences › Immunology and Microbiology › Immunology › Biomarkers in Disease Mechanisms
  • Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
  • Life Sciences › Immunology and Microbiology › Immunology › Complement system in diseases
Keywords
C-reactive protein, CD59, complement regulatory proteins, whole blood gene expression, interferon-stimulated genes, chronic inflammation, causal inference, Mendelian randomization, Estonian Biobank, healthy aging, inflammatory markers
MeSH terms
Adult, C-Reactive Protein, Estonia, Humans, Inflammation, Cohort Studies, Up-Regulation, CD59 Antigens

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 ›

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