Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits


Autoři: Andrew D. Bretherick aff001;  Oriol Canela-Xandri aff001;  Peter K. Joshi aff003;  David W. Clark aff003;  Konrad Rawlik aff002;  Thibaud S. Boutin aff001;  Yanni Zeng aff001;  Carmen Amador aff001;  Pau Navarro aff001;  Igor Rudan aff003;  Alan F. Wright aff001;  Harry Campbell aff003;  Veronique Vitart aff001;  Caroline Hayward aff001;  James F. Wilson aff001;  Albert Tenesa aff001;  Chris P. Ponting aff001;  J. Kenneth Baillie aff002;  Chris Haley aff001
Působiště autorů: MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom aff001;  The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom aff002;  Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom aff003;  Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China aff004;  Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China aff005;  Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China aff006
Vyšlo v časopise: Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits. PLoS Genet 16(7): e32767. doi:10.1371/journal.pgen.1008785
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008785

Souhrn

To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk.

Klíčová slova:

Alleles – Asthma – Coronary heart disease – Drug discovery – Genetics of disease – Genome-wide association studies – Instrumental variable analysis – Schizophrenia


Zdroje

1. Munos B. Lessons from 60 years of pharmaceutical innovation. Nat Rev Drug Discov. 2009;8: 959–968. doi: 10.1038/nrd2961 19949401

2. Arrowsmith J. Trial watch: Phase II failures: 2008–2010. Nat Rev Drug Discov. 2011;10: 328–329. doi: 10.1038/nrd3439 21532551

3. Baillie JK. Translational genomics. Targeting the host immune response to fight infection. Science. 2014;344: 807–808. doi: 10.1126/science.1255074 24855243

4. Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, et al. The support of human genetic evidence for approved drug indications. Nat Genet. 2015;47: 856–860. doi: 10.1038/ng.3314 26121088

5. Finan C, Gaulton A, Kruger FA, Lumbers RT, Shah T, Engmann J, et al. The druggable genome and support for target identification and validation in drug development. Sci Transl Med. 2017;9: eaag1166. doi: 10.1126/scitranslmed.aag1166 28356508

6. Fang H, ULTRA-DD Consortium, De Wolf H, Knezevic B, Burnham KL, Osgood J, et al. A genetics-led approach defines the drug target landscape of 30 immune-related traits. Nat Genet. 2019;51: 1082–1091. doi: 10.1038/s41588-019-0456-1 31253980

7. Behan FM, Iorio F, Picco G, Gonçalves E, Beaver CM, Migliardi G, et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature. 2019;568: 511–516. doi: 10.1038/s41586-019-1103-9 30971826

8. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017;45: D896–D901. doi: 10.1093/nar/gkw1133 27899670

9. Smith GD, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32: 1–22. doi: 10.1093/ije/dyg070 12689998

10. Burgess S, Scott RA, Timpson NJ, Smith GD, Thompson SG, EPIC-InterAct Consortium. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30: 543–552. doi: 10.1007/s10654-015-0011-z 25773750

11. Mirauta BA, Seaton DD, Bensaddek D, Brenes A, Bonder MJ, Kilpinen H, et al. Population-scale proteome variation in human induced pluripotent stem cells. bioRxiv. 2018 [cited 13 Nov 2018]. doi: 10.1101/439216

12. Folkersen L, Fauman E, Sabater-Lleal M, Strawbridge RJ, Frånberg M, Sennblad B, et al. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet. 2017;13: e1006706. doi: 10.1371/journal.pgen.1006706 28369058

13. Suhre K, Arnold M, Bhagwat AM, Cotton RJ, Engelke R, Raffler J, et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun. 2017;8: 14357. doi: 10.1038/ncomms14357 28240269

14. Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558: 73–79. doi: 10.1038/s41586-018-0175-2 29875488

15. Yao C, Chen G, Song C, Keefe J, Mendelson M, Huan T, et al. Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat Commun. 2018;9: 3268. doi: 10.1038/s41467-018-05512-x 30111768

16. Zheng J, Haberland V, Baird D, Walker V, Haycock P, Gutteridge A, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. bioRxiv. 2019 [cited 7 Sep 2019]. doi: 10.1101/627398

17. Chong M, Sjaarda J, Pigeyre M, Mohammadi-Shemirani P, Lali R, Shoamanesh A, et al. Novel Drug Targets for Ischemic Stroke Identified Through Mendelian Randomization Analysis of the Blood Proteome. Circulation. 2019;140: 819–830. doi: 10.1161/CIRCULATIONAHA.119.040180 31208196

18. Mosley JD, Benson MD, Smith JG, Melander O, Ngo D, Shaffer CM, et al. Probing the Virtual Proteome to Identify Novel Disease Biomarkers. Circulation. 2018;138: 2469–2481. doi: 10.1161/CIRCULATIONAHA.118.036063 30571344

19. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature. 2017;550: 204–213. doi: 10.1038/nature24277 29022597

20. Canela-Xandri O, Rawlik K, Tenesa A. An atlas of genetic associations in UK Biobank. bioRxiv. 2017 [cited 25 Aug 2017]. doi: 10.1101/176834

21. Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, et al. PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics. 2016;32: 3207–3209. doi: 10.1093/bioinformatics/btw373 27318201

22. Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 2019;35: 4851–4853. doi: 10.1093/bioinformatics/btz469 31233103

23. 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: 481–487. doi: 10.1038/ng.3538 27019110

24. The CARDIoGRAMplusC4D Consortium. A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47: 1121–1130. doi: 10.1038/ng.3396 26343387

25. Scott RA, Scott LJ, Mägi R, Marullo L, Gaulton KJ, Kaakinen M, et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes. 2017;66: 2888–2902. doi: 10.2337/db16-1253 28566273

26. Nelson CP, Goel A, Butterworth AS, Kanoni S, Webb TR, Marouli E, et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat Genet. 2017;49: 1385–1391. doi: 10.1038/ng.3913 28714975

27. Liu JZ, van Sommeren S, Huang H, Ng SC, Alberts R, Takahashi A, et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet. 2015;47: 979–986. doi: 10.1038/ng.3359 26192919

28. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511: 421–427. doi: 10.1038/nature13595 25056061

29. Bronson PG, Chang D, Bhangale T, Seldin MF, Ortmann W, Ferreira RC, et al. Common variants at PVT1, ATG13-AMBRA1, AHI1 and CLEC16A are associated with selective IgA deficiency. Nat Genet. 2016;48: 1425–1429. doi: 10.1038/ng.3675 27723758

30. Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506: 376–381. doi: 10.1038/nature12873 24390342

31. van Rheenen W, Shatunov A, Dekker AM, McLaughlin RL, Diekstra FP, Pulit SL, et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat Genet. 2016;48: 1043–1048. doi: 10.1038/ng.3622 27455348

32. Hammerschlag AR, Stringer S, de Leeuw CA, Sniekers S, Taskesen E, Watanabe K, et al. Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Nat Genet. 2017;49: 1584–1592. doi: 10.1038/ng.3888 28604731

33. Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JRI, Krapohl E, et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat Genet. 2017;49: 1107–1112. doi: 10.1038/ng.3869 28530673

34. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533: 539–542. doi: 10.1038/nature17671 27225129

35. Hou L, Bergen SE, Akula N, Song J, Hultman CM, Landén M, et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum Mol Genet. 2016;25: 3383–3394. doi: 10.1093/hmg/ddw181 27329760

36. Beaumont RN, Warrington NM, Cavadino A, Tyrrell J, Nodzenski M, Horikoshi M, et al. Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics. Hum Mol Genet. 2018;27: 742–756. doi: 10.1093/hmg/ddx429 29309628

37. Phelan CM, Kuchenbaecker KB, Tyrer JP, Kar SP, Lawrenson K, Winham SJ, et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat Genet. 2017;49: 680–691. doi: 10.1038/ng.3826 28346442

38. van der Harst P, Verweij N. Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease. Circ Res. 2018;122: 433–443. doi: 10.1161/CIRCRESAHA.117.312086 29212778

39. van den Berg SM, de Moor MHM, Verweij KJH, Krueger RF, Luciano M, Vasquez AA, et al. Meta-analysis of genome-wide association studies for extraversion: findings from the Genetics of Personality Consortium. Behav Genet. 2016;46: 170–182. doi: 10.1007/s10519-015-9735-5 26362575

40. Genetics of Personality Consortium. Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with major depressive disorder. JAMA Psychiatry. 2015;72: 642–650. doi: 10.1001/jamapsychiatry.2015.0554 25993607

41. The EArly Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium. Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. Nat Genet. 2015;47: 1449–1456. doi: 10.1038/ng.3424 26482879

42. Ferreira MA, Vonk JM, Baurecht H, Marenholz I, Tian C, Hoffman JD, et al. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat Genet. 2017;49: 1752–1757. doi: 10.1038/ng.3985 29083406

43. Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell. 2016;167: 1415–1429.e19. doi: 10.1016/j.cell.2016.10.042 27863252

44. Hormozdiari F, van de Bunt M, Segrè AV, Li X, Joo JWJ, Bilow M, et al. Colocalization of GWAS and eQTL signals detects target genes. Am J Hum Genet. 2016;99: 1245–1260. doi: 10.1016/j.ajhg.2016.10.003 27866706

45. Gordon ED, Palandra J, Wesolowska-Andersen A, Ringel L, Rios CL, Lachowicz-Scroggins ME, et al. IL1RL1 asthma risk variants regulate airway type 2 inflammation. JCI Insight. 2016;1: e87871. doi: 10.1172/jci.insight.87871 27699235

46. Gudbjartsson DF, Bjornsdottir US, Halapi E, Helgadottir A, Sulem P, Jonsdottir GM, et al. Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction. Nat Genet. 2009;41: 342–347. doi: 10.1038/ng.323 19198610

47. Busse WW, Israel E, Nelson HS, Baker JW, Charous BL, Young DY, et al. Daclizumab improves asthma control in patients with moderate to severe persistent asthma: a randomized, controlled trial. Am J Respir Crit Care Med. 2008;178: 1002–1008. doi: 10.1164/rccm.200708-1200OC 18787222

48. Massoud AH, Charbonnier L-M, Lopez D, Pellegrini M, Phipatanakul W, Chatila TA. An asthma-associated IL4R variant exacerbates airway inflammation by promoting conversion of regulatory T cells to TH17-like cells. Nat Med. 2016;22: 1013–1022. doi: 10.1038/nm.4147 27479084

49. Navarini AA, French LE, Hofbauer GFL. Interrupting IL-6–receptor signaling improves atopic dermatitis but associates with bacterial superinfection. J Allergy Clin Immunol. 2011;128: 1128–1130. doi: 10.1016/j.jaci.2011.09.009 21962991

50. Ullah MA, Sukkar M, Ferreira M, Phipps S. 53: IL-6R blockade: A new personalised treatment for asthma? Cytokine. 2014;70: 40. doi: 10.1016/j.cyto.2014.07.060

51. Esparza-Gordillo J, Schaarschmidt H, Liang L, Cookson W, Bauerfeind A, Lee-Kirsch M-A, et al. A functional IL-6 receptor (IL6R) variant is a risk factor for persistent atopic dermatitis. J Allergy Clin Immunol. 2013;132: 371–377. doi: 10.1016/j.jaci.2013.01.057 23582566

52. Ferreira MAR, Matheson MC, Duffy DL, Marks GB, Hui J, Le Souëf P, et al. Identification of IL6R and chromosome 11q13.5 as risk loci for asthma. Lancet. 2011;378: 1006–1014. doi: 10.1016/S0140-6736(11)60874-X 21907864

53. Scott LJ. Tocilizumab: a review in rheumatoid arthritis. Drugs. 2017;77: 1865–1879. doi: 10.1007/s40265-017-0829-7 29094311

54. IL6R Genetics Consortium Emerging Risk Factors Collaboration. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet. 2012;379: 1205–1213. doi: 10.1016/S0140-6736(11)61931-4 22421339

55. Interleukin-6 Receptor Mendelian Randomisation Analysis (IL6R MR) Consortium. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet. 2012;379: 1214–1224. doi: 10.1016/S0140-6736(12)60110-X 22421340

56. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26: 2336–2337. doi: 10.1093/bioinformatics/btq419 20634204

57. Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Blal HA, et al. A subcellular map of the human proteome. Science. 2017;356: eaal3321. doi: 10.1126/science.aal3321 28495876

58. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Tissue-based map of the human proteome. Science. 2015;347: 1260419. doi: 10.1126/science.1260419 25613900

59. Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, et al. A pathology atlas of the human cancer transcriptome. Science. 2017;357: eaan2507. doi: 10.1126/science.aan2507 28818916

60. Ohnishi H, Kaneko Y, Okazawa H, Miyashita M, Sato R, Hayashi A, et al. Differential localization of Src homology 2 domain-dontaining protein tyrosine phosphatase substrate-1 and CD47 and Its molecular mechanisms in cultured hippocampal neurons. J Neurosci. 2005;25: 2702–2711. doi: 10.1523/JNEUROSCI.5173-04.2005 15758180

61. Toth AB, Terauchi A, Zhang LY, Johnson-Venkatesh EM, Larsen DJ, Sutton MA, et al. Synapse maturation by activity-dependent ectodomain shedding of SIRPα. Nat Neurosci. 2013;16: 1417–1425. doi: 10.1038/nn.3516 24036914

62. Ma L, Kulesskaya N, Võikar V, Tian L. Differential expression of brain immune genes and schizophrenia-related behavior in C57BL/6N and DBA/2J female mice. Psychiatry Res. 2015;226: 211–216. doi: 10.1016/j.psychres.2015.01.001 25661533

63. Koshimizu H, Takao K, Matozaki T, Ohnishi H, Miyakawa T. Comprehensive behavioral analysis of cluster of differentiation 47 knockout mice. PLoS ONE. 2014;9: e89584. doi: 10.1371/journal.pone.0089584 24586890

64. Ohnishi H, Murata T, Kusakari S, Hayashi Y, Takao K, Maruyama T, et al. Stress-evoked tyrosine phosphorylation of signal regulatory protein α regulates behavioral immobility in the forced swim test. J Neurosci. 2010;30: 10472–10483. doi: 10.1523/JNEUROSCI.0257-10.2010 20685990

65. Chang HP, Lindberg FP, Wang HL, Huang AM, Lee EHY. Impaired memory retention and decreased long-term potentiation in integrin-associated protein-deficient mice. Learn Mem. 1999;6: 448–457. doi: 10.1101/lm.6.5.448 10541465

66. Huang AM, Wang HL, Tang YP, Lee EHY. Expression of integrin-associated protein gene associated with memory formation in rats. J Neurosci. 1998;18: 4305–4313. doi: 10.1523/JNEUROSCI.18-11-04305.1998 9592107

67. Brown GC, Neher JJ. Microglial phagocytosis of live neurons. Nat Rev Neurosci. 2014;15: 209–216. doi: 10.1038/nrn3710 24646669

68. Martins-de-Souza D, Gattaz WF, Schmitt A, Rewerts C, Maccarrone G, Dias-Neto E, et al. Prefrontal cortex shotgun proteome analysis reveals altered calcium homeostasis and immune system imbalance in schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2009;259: 151–163. doi: 10.1007/s00406-008-0847-2 19165527

69. Klarin D, Zhu QM, Emdin CA, Chaffin M, Horner S, McMillan BJ, et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat Genet. 2017;49: 1392–1397. doi: 10.1038/ng.3914 28714974

70. Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, et al. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002;32: 650–654. doi: 10.1038/ng1047 12426569

71. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, et al. An integrated map of structural variation in 2,504 human genomes. Nature. 2015;526: 75–81. doi: 10.1038/nature15394 26432246

72. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015;526: 68–74. doi: 10.1038/nature15393 26432245

73. Ferreira RC, Freitag DF, Cutler AJ, Howson JMM, Rainbow DB, Smyth DJ, et al. Functional IL6R 358Ala allele impairs classical IL-6 receptor signaling and influences risk of diverse inflammatory diseases. PLoS Genet. 2013;9: e1003444. doi: 10.1371/journal.pgen.1003444 23593036

74. Wenzel S, Castro M, Corren J, Maspero J, Wang L, Zhang B, et al. Dupilumab efficacy and safety in adults with uncontrolled persistent asthma despite use of medium-to-high-dose inhaled corticosteroids plus a long-acting β2 agonist: a randomised double-blind placebo-controlled pivotal phase 2b dose-ranging trial. Lancet. 2016;388: 31–44. doi: 10.1016/S0140-6736(16)30307-5 27130691

75. Wenzel S, Ford L, Pearlman D, Spector S, Sher L, Skobieranda F, et al. Dupilumab in persistent asthma with elevated eosinophil levels. N Engl J Med. 2013;368: 2455–2466. doi: 10.1056/NEJMoa1304048 23688323

76. McQuillan R, Leutenegger A-L, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L, et al. Runs of homozygosity in European populations. Am J Hum Genet. 2008;83: 359–372. doi: 10.1016/j.ajhg.2008.08.007 18760389

77. Campbell H, Carothers AD, Rudan I, Hayward C, Biloglav Z, Barac L, et al. Effects of genome-wide heterozygosity on a range of biomedically relevant human quantitative traits. Hum Mol Genet. 2007;16: 233–241. doi: 10.1093/hmg/ddl473 17220173

78. Rudan I, Marusić A, Janković S, Rotim K, Boban M, Lauc G, et al. “10001 Dalmatians:” Croatia launches its national biobank. Croat Med J. 2009;50: 4–6. doi: 10.3325/cmj.2009.50.4 19260138

79. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R library for genome-wide association analysis. Bioinformatics. 2007;23: 1294–1296. doi: 10.1093/bioinformatics/btm108 17384015

80. Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4: s13742-015-0047–8. doi: 10.1186/s13742-015-0047-8 25722852

81. Purcell S. PLINK: v1.90. 2017.

82. O’Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S, Cocca M, et al. A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 2014;10: e1004234. doi: 10.1371/journal.pgen.1004234 24743097

83. The Haplotype Reference Consortium. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48: 1279–1283. doi: 10.1038/ng.3643 27548312

84. Assarsson E, Lundberg M, Holmquist G, Björkesten J, Thorsen SB, Ekman D, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PloS ONE. 2014;9: e95192. doi: 10.1371/journal.pone.0095192 24755770

85. Haller T, Kals M, Esko T, Mägi R, Fischer K. RegScan: a GWAS tool for quick estimation of allele effects on continuous traits and their combinations. Brief Bioinform. 2015;16: 39–44. doi: 10.1093/bib/bbt066 24008273

86. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018;46: D754–D761. doi: 10.1093/nar/gkx1098 29155950

87. Lynch M, Walsh B. Genetics and Analysis of Quantitative Traits. 1998 edition. Sunderland, Mass: Sinauer; 1998.

88. Canela-Xandri O, Rawlik K, Tenesa A. An atlas of genetic associations in UK Biobank. Nat Genet. 2018;50: 1593–1599. doi: 10.1038/s41588-018-0248-z 30349118

89. Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, et al. ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic Acids Res. 2015;43: W612–W620. doi: 10.1093/nar/gkv352 25883136


Článek vyšel v časopise

PLOS Genetics


2020 Číslo 7
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Současné pohledy na riziko v parodontologii
nový kurz
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Svět praktické medicíny 3/2024 (znalostní test z časopisu)

Kardiologické projevy hypereozinofilií
Autoři: prof. MUDr. Petr Němec, Ph.D.

Střevní příprava před kolonoskopií
Autoři: MUDr. Klára Kmochová, Ph.D.

Aktuální možnosti diagnostiky a léčby litiáz
Autoři: MUDr. Tomáš Ürge, PhD.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se