The MR-base platform supports systematic causal inference across the human phenome

eLife

Volumn 7, Issue , 2018, Pages

  Hemani, Gibran ^a   Zheng, Jie ^a   Elsworth, Benjamin ^a   Wade, Kaitlin H ^a   Haberland, Valeriia ^a   Baird, Denis ^a   Laurin, Charles ^a   Burgess, Stephen ^b   Bowden, Jack ^a   Langdon, Ryan ^a   Tan, Vanessa Y ^a   Yarmolinsky, James ^a   Shihab, Hashem A ^a   Timpson, Nicholas J ^a   Evans, David M ^a,c   Relton, Caroline ^a   Martin, Richard M ^a   Davey Smith, George ^a   Gaunt, Tom R ^a   Haycock, Philip C ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CONTROLLED STUDY; GENE MUTATION; GENOME-WIDE ASSOCIATION STUDY; HUMAN; MENDELIAN RANDOMIZATION ANALYSIS; PHENOTYPE; PLEIOTROPY; RANDOMIZED CONTROLLED TRIAL; SENSITIVITY ANALYSIS; SINGLE NUCLEOTIDE POLYMORPHISM; SOFTWARE; STATISTICAL SIGNIFICANCE; BIOLOGICAL MODEL; CORONARY ARTERY DISEASE; GENETIC DATABASE; GENETICS; METABOLISM;

LOW DENSITY LIPOPROTEIN CHOLESTEROL;

CHOLESTEROL, LDL; CORONARY DISEASE; DATABASES, GENETIC; GENETIC PLEIOTROPY; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MENDELIAN RANDOMIZATION ANALYSIS; MODELS, GENETIC; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 85051679391     PISSN: None     EISSN: 2050084X     Source Type: Journal    
DOI: 10.7554/eLife.34408     Document Type: Article

References (82)

1. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR. 2015. A global reference for human genetic variation. Nature 526:68–74. DOI: https://doi.org/10.1038/nature15393, PMID: 26432245
2. Angrist JD, Krueger AB. 1992. Estimating the Payoff to Schooling Using the Vietnam-Era Draft Lottery. www. nber.org/papers/w4067[Accessed February 1, 2018].
3. Angrist JD, Krueger AB. 1995. Split-sample instrumental variables estimates of the return to schooling. Journal of Business & Economic Statistics 13:225–235.
4. Beck T, Hastings RK, Gollapudi S, Free RC, Brookes AJ. 2014. GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies. European Journal of Human Genetics 22: 949–952. DOI: https://doi.org/10.1038/ejhg.2013.274, PMID: 24301061
5. Benner C, Spencer CC, Havulinna AS, Salomaa V, Ripatti S, Pirinen M. 2016. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32:1493–1501. DOI: https://doi.org/10.1093/bioinformatics/btw018, PMID: 26773131
6. Bowden J, Davey Smith G, Burgess S. 2015. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology 44:512–525. DOI: https://doi.org/10.1093/ije/dyv080, PMID: 26050253
7. Bowden J, Davey Smith G, Haycock PC, Burgess S. 2016a. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genetic Epidemiology 40:304–314. DOI: https://doi.org/10.1002/gepi.21965, PMID: 27061298
8. Bowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan N, Thompson J. 2017a. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Statistics in Medicine 36: 1783–1802. DOI: https://doi.org/10.1002/sim.7221, PMID: 28114746
9. Bowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. 2016b. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. International Journal of Epidemiology 45:1961–1974. DOI: https://doi.org/10.1093/ije/dyw220, PMID: 27616674
10. Bowden J, Dudbridge F. 2009. Unbiased estimation of odds ratios: combining genomewide association scans with replication studies. Genetic Epidemiology 33:406–418. DOI: https://doi.org/10.1002/gepi.20394, PMID: 1 9140132
11. Bowden J, Spiller W, Del-Greco F, Sheehan N, Thompson J, Minelli C, Davey Smith G. 2017b. Improving the visualisation, interpretation and analysis of two-sample summary data mendelian randomization via the radial plot and radial regression. BioRxiv. DOI: https://doi.org/10.1101/200378
12. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, Duncan L, Perry JR, Patterson N, Robinson EB, Daly MJ, Price AL, Neale BM, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3. 2015. An atlas of genetic correlations across human diseases and traits. Nature Genetics 47:1236–1241. DOI: https://doi.org/10.1038/ng. 3406, PMID: 26414676
13. Burgess S, Butterworth AS, Thompson JR. 2016. Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors. Journal of Clinical Epidemiology 69:208–216. DOI: https://doi.org/10.1016/j.jclinepi. 2015.08.001, PMID: 26291580
14. Burgess S, Freitag DF, Khan H, Gorman DN, Thompson SG. 2014. Using multivariable mendelian randomization to disentangle the causal effects of lipid fractions. PLoS ONE 9:e108891. DOI: https://doi.org/10.1371/journal. pone.0108891
15. Burgess S, Thompson SG, CRP CHD Genetics Collaboration. 2011. Avoiding bias from weak instruments in Mendelian randomization studies. International Journal of Epidemiology 40:755–764. DOI: https://doi.org/10. 1093/ije/dyr036, PMID: 21414999
16. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT. 2017. Genome-wide genetic data on ~500,000 UK Biobank participants. bioRxiv. DOI: https://doi.org/10.1101/166298
17. Churchhouse C, Neale B. 2017. Rapid GWAS of thousands of phenotypes for 337,000 samples in the UK Biobank. Neale Lab. http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-the-uk-biobank [Accessed 14, December 2017].
18. Davey Smith G, Ebrahim S. 2001. Epidemiology–is it time to call it a day? International Journal of Epidemiology 30:1–11. DOI: https://doi.org/10.1093/ije/30.1.1, PMID: 11171840
19. Davey Smith G, Ebrahim S. 2003. ’Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology 32:1–22. DOI: https://doi.org/10.1093/ije/dyg070, PMID: 12689998
20. Davey Smith G, Hemani G. 2014. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics 23:R89–R98. DOI: https://doi.org/10.1093/hmg/ddu328, PMID: 25064373
21. Deming Y, Xia J, Cai Y, Lord J, Del-Aguila JL, Fernandez MV, Carrell D, Black K, Budde J, Ma S, Saef B, Howells B, Bertelsen S, Bailey M, Ridge PG, Hefti F, Fillit H, Zimmerman EA, Celmins D, Brown AD, et al. 2016. Genetic studies of plasma analytes identify novel potential biomarkers for several complex traits. Scientific Reports 6: 18092. DOI: https://doi.org/10.1038/srep18092
22. Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, Wang D, Masys DR, Roden DM, Crawford DC. 2010. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26:1205–1210. DOI: https://doi.org/10.1093/bioinformatics/btq126, PMID: 20335276
23. Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, Wood AM, Lewington S, Sattar N, Packard CJ, Collins R, Thompson SG, Danesh J, Emerging Risk Factors Collaboration. 2009. Major lipids, apolipoproteins, and risk of vascular disease. JAMA 302:1993–2000. DOI: https://doi.org/10.1001/jama.2009.1619, PMID: 19903920
24. Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Donnelly LA, Ehret GB, Esko T, et al. 2013. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nature Genetics 45:1345–1352. DOI: https://doi.org/10.1038/ng.2795, PMID: 24097064
25. Dudbridge F. 2013. Power and predictive accuracy of polygenic risk scores. PLoS Genetics 9:e1003348. DOI: https://doi.org/10.1371/journal.pgen.1003348, PMID: 23555274
26. Durinck S, Spellman PT, Birney E, Huber W. 2009. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols 4:1184–1191. DOI: https://doi.org/10.1038/nprot. 2009.97, PMID: 19617889
27. Euesden J, Lewis CM, O’Reilly PF. 2015. PRSice: Polygenic Risk Score software. Bioinformatics 31:1466–1468. DOI: https://doi.org/10.1093/bioinformatics/btu848, PMID: 25550326
28. Gaunt TR, Shihab HA, Hemani G, Min JL, Woodward G, Lyttleton O, Zheng J, Duggirala A, McArdle WL, Ho K, Ring SM, Evans DM, Davey Smith G, Relton CL. 2016. Systematic identification of genetic influences on methylation across the human life course. Genome Biology 17:61. DOI: https://doi.org/10.1186/s13059-016-0926-z, PMID: 27036880
29. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, Plagnol V. 2014. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genetics 10: e1004383. DOI: https://doi.org/10.1371/journal.pgen.1004383, PMID: 24830394
30. GTEx Consortium. 2015. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348:648–660. DOI: https://doi.org/10.1126/science.1262110, PMID: 25 954001
31. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, Jansen R, de Geus EJ, Boomsma DI, Wright FA, Sullivan PF, Nikkola E, Alvarez M, Civelek M, Lusis AJ, Lehtimäki T, Raitoharju E, Kähönen M, Seppälä I, Raitakari OT, et al. 2016. Integrative approaches for large-scale transcriptome-wide association studies. Nature Genetics 48: 245–252. DOI: https://doi.org/10.1038/ng.3506, PMID: 26854917
32. Hannon E, Weedon M, Bray N, O’Donovan M, Mill J. 2017a. Pleiotropic Effects of Trait-Associated Genetic Variation on DNA Methylation: Utility for Refining GWAS Loci. The American Journal of Human Genetics 100: 954–959. DOI: https://doi.org/10.1016/j.ajhg.2017.04.013, PMID: 28528868
33. Hartwig FP, Borges MC, Horta BL, Bowden J, Davey Smith G. 2017. Inflammatory Biomarkers and Risk of Schizophrenia: A 2-Sample Mendelian Randomization Study. JAMA Psychiatry 74. DOI: https://doi.org/10. 1001/jamapsychiatry.2017.3191, PMID: 29094161
34. Hartwig FP, Davey Smith G, Bowden J. 2017b. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. International Journal of Epidemiology 46:1985–1998. DOI: https://doi. org/10.1093/ije/dyx102, PMID: 29040600
35. Hartwig FP, Davies NM, Hemani G, Davey Smith G. 2016. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. International Journal of Epidemiology 45:1717–1726. DOI: https://doi.org/10.1093/ije/dyx028, PMID: 28338968
36. Haycock PC, Burgess S, Nounu A, Zheng J, Okoli GN, Bowden J, Wade KH, Timpson NJ, Evans DM, Willeit P, Aviv A, Gaunt TR, Hemani G, Mangino M, Ellis HP, Kurian KM, Pooley KA, Eeles RA, Lee JE, Fang S, et al. 2017. Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study. JAMA Oncology 3:636–651. DOI: https://doi.org/10.1001/jamaoncol.2016.5945, PMID: 28241208
37. Haycock PC, Burgess S, Wade KH, Bowden J, Relton C, Davey Smith G. 2016. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. The American Journal of Clinical Nutrition 103:965–978. DOI: https://doi.org/10.3945/ajcn.115.118216, PMID: 26961927
38. Hemani G, Bowden J, Haycock PC, Zheng J, Davis O, Flach P, Gaunt TR, Smith GD. 2017a. Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome. bioRxiv. DOI: https://doi.org/10.1101/173682
39. Hemani G, Tilling K, Davey Smith G. 2017b. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genetics 13:e1007081. DOI: https://doi.org/10.1371/journal.pgen. 1007081, PMID: 29149188
40. Hemani G. 2018. Analysis for MR Base methods paper. GitHub. 56a955c. https://github.com/explodecomputer/mr-base-methods-paper
41. Holmes MV, Ala-Korpela M, Smith GD. 2017. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nature Reviews Cardiology 14:577–590. DOI: https://doi.org/10.1038/nrcardio.2017.78, PMID: 28569269
42. Inoshita M, Numata S, Tajima A, Kinoshita M, Umehara H, Nakataki M, Ikeda M, Maruyama S, Yamamori H, Kanazawa T, Shimodera S, Hashimoto R, Imoto I, Yoneda H, Iwata N, Ohmori T. 2018. Retraction: A significant causal association between C-reactive protein levels and schizophrenia. Scientific Reports 8:46947. DOI: https://doi.org/10.1038/srep46947, PMID: 29465083
43. Johnson T. 2012. Efficient calculation for Multi-SNP genetic risk scores. American Society of Human Genetics Annual Meeting, San Francisco. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.398.7674.
44. Jones SE, Tyrrell J, Wood AR, Beaumont RN, Ruth KS, Tuke MA, Yaghootkar H, Hu Y, Teder-Laving M, Hayward C, Roenneberg T, Wilson JF, Del Greco F, Hicks AA, Shin C, Yun C-H, Lee SK, Metspalu A, Byrne EM, Gehrman PR, et al. 2016. Genome-Wide association analyses in 128,266 individuals identifies new morningness and sleep duration loci. PLOS Genetics 12:e1006125. DOI: https://doi.org/10.1371/journal.pgen.1006125
45. Kang H, Zhang A, Cai TT, Small DS. 2014. Instrumental Variables Estimation with Some Invalid Instruments and its Application to Mendelian. arXiv. https://arxiv.org/abs/1401.5755.
46. Kettunen J, Demirkan A, Würtz P, Draisma HH, Haller T, Rawal R, Vaarhorst A, Kangas AJ, Lyytikäinen LP, Pirinen M, Pool R, Sarin AP, Soininen P, Tukiainen T, Wang Q, Tiainen M, Tynkkynen T, Amin N, Zeller T, Beekman M, et al. 2016. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nature Communications 7:11122. DOI: https://doi.org/10.1038/ncomms11122, PMID: 27005778
47. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. 2008. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine 27:1133–1163. DOI: https://doi.org/10.1002/sim.3034, PMID: 17886233
48. Lawlor DA, Tilling K, Davey Smith G. 2016. Triangulation in aetiological epidemiology. International Journal of Epidemiology 45:1866–1886. DOI: https://doi.org/10.1093/ije/dyw314, PMID: 28108528
49. Li MJ, Liu Z, Wang P, Wong MP, Nelson MR, Kocher JP, Yeager M, Sham PC, Chanock SJ, Xia Z, Wang J. 2016. GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Research 44:D869–876. DOI: https://doi.org/10.1093/nar/gkv1317, PMID: 26615194
50. Millard LA, Davies NM, Timpson NJ, Tilling K, Flach PA, Davey Smith G. 2015. MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization. Scientific Reports 5:16645. DOI: https://doi.org/10.1038/srep16645, PMID: 26568383
51. Millard LAC, Davies NM, Gaunt TR, Davey Smith G, Tilling K. 2017. Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank. International Journal of Epidemiology. DOI: https://doi.org/10.1093/ije/dyx204, PMID: 29040602
52. Munafò MR, Davey Smith G. 2018. Robust research needs many lines of evidence. Nature 553:399–401. DOI: https://doi.org/10.1038/d41586-018-01023-3, PMID: 29368721
53. Newcombe PJ, Conti DV, Richardson S. 2016. JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects. Genetic Epidemiology 40:188–201. DOI: https://doi.org/10.1002/gepi.21953, PMID: 27027514
54. Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, Saleheen D, Kyriakou T, Nelson CP, Hopewell JC, Webb TR, Zeng L, Dehghan A, Alver M, Armasu SM, Auro K, Bjonnes A, Chasman DI, Chen S, Ford I, et al. 2015. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nature Genetics 47:1121–1130. DOI: https://doi.org/10.1038/ng.3396, PMID: 26343387
55. Pasaniuc B, Price AL. 2017. Dissecting the genetics of complex traits using summary association statistics. Nature Reviews Genetics 18:117–127. DOI: https://doi.org/10.1038/nrg.2016.142, PMID: 27840428
56. Pickrell JK, Berisa T, Liu JZ, Ségurel L, Tung JY, Hinds DA. 2016. Detection and interpretation of shared genetic influences on 42 human traits. Nature Genetics 48:709–717. DOI: https://doi.org/10.1038/ng.3570, PMID: 271 82965
57. Pierce BL, Burgess S. 2013. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. American Journal of Epidemiology 178:1177–1184. DOI: https://doi.org/10. 1093/aje/kwt084, PMID: 23863760
58. Pilling LC, Atkins JL, Bowman K, Jones SE, Tyrrell J, Beaumont RN, Ruth KS, Tuke MA, Yaghootkar H, Wood AR, Freathy RM, Murray A, Weedon MN, Xue L, Lunetta K, Murabito JM, Harries LW, Robine JM, Brayne C, Kuchel GA, et al. 2016. Human longevity is influenced by many genetic variants: evidence from 75,000 UK biobank participants. Aging 8:547–560. DOI: https://doi.org/10.18632/aging.100930, PMID: 27015805
59. Richardson TG, Zheng J, Davey Smith G, Timpson NJ, Gaunt TR, Relton CL, Hemani G. 2017. Mendelian randomization analysis identifies CpG sites as putative mediators for genetic influences on cardiovascular disease risk. The American Journal of Human Genetics 101:590–602. DOI: https://doi.org/10.1016/j.ajhg.2017. 09.003, PMID: 28985495
60. Roederer M, Quaye L, Mangino M, Beddall MH, Mahnke Y, Chattopadhyay P, Tosi I, Napolitano L, Terranova Barberio M, Menni C, Villanova F, Di Meglio P, Spector TD, Nestle FO. 2015. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161:387–403. DOI: https://doi.org/10.1016/j.cell.2015.02.046, PMID: 25772697
61. Sattar N, Preiss D, Murray HM, Welsh P, Buckley BM, de Craen AJ, Seshasai SR, McMurray JJ, Freeman DJ, Jukema JW, Macfarlane PW, Packard CJ, Stott DJ, Westendorp RG, Shepherd J, Davis BR, Pressel SL, Marchioli R, Marfisi RM, Maggioni AP, et al. 2010. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet 375:735–742. DOI: https://doi.org/10.1016/S0140-6736(09)61965-6, PMID: 20167359
62. Schmidt AF, Swerdlow DI, Holmes MV, Patel RS, Fairhurst-Hunter Z, Lyall DM, Hartwig FP, Horta BL, Hyppönen E, Power C, Moldovan M, van Iperen E, Hovingh GK, Demuth I, Norman K, Steinhagen-Thiessen E, Demuth J, Bertram L, Liu T, Coassin S, et al. 2017. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study. The Lancet Diabetes & Endocrinology 5:97–105. DOI: https://doi.org/10.1016/S2213-8587(16)30396-5, PMID: 27908689
63. Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, Walter K, Menni C, Chen L, Vasquez L, Valdes AM, Hyde CL, Wang V, Ziemek D, Roberts P, Xi L, et al. 2014. An atlas of genetic influences on human blood metabolites. Nature Genetics 46:543–550. DOI: https://doi.org/10.1038/ng.2982, PMID: 24816252
64. Silverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, Braunwald E, Sabatine MS. 2016. Association Between Lowering LDL-C and Cardiovascular Risk Reduction Among Different Therapeutic Interventions: A Systematic Review and Meta-analysis. JAMA 316:1289–1297. DOI: https://doi.org/10.1001/jama.2016.13985, PMID: 27673306
65. Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, Paul DS, Freitag D, Burgess S, Danesh J, Young R, Butterworth AS. 2016. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32:3207–3209. DOI: https://doi.org/10.1093/bioinformatics/btw373, PMID: 27318201
66. Sterne JA, Sutton AJ, Ioannidis JP, Terrin N, Jones DR, Lau J, Carpenter J, Rücker G, Harbord RM, Schmid CH, Tetzlaff J, Deeks JJ, Peters J, Macaskill P, Schwarzer G, Duval S, Altman DG, Moher D, Higgins JP. 2011. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ 343:d4002. DOI: https://doi.org/10.1136/bmj.d4002, PMID: 21784880
67. Swerdlow DI, Kuchenbaecker KB, Shah S, Sofat R, Holmes MV, White J, Mindell JS, Kivimaki M, Brunner EJ, Whittaker JC, Casas JP, Hingorani AD. 2016. Selecting instruments for Mendelian randomization in the wake of genome-wide association studies. International Journal of Epidemiology 45:1600–1616. DOI: https://doi.org/10.1093/ije/dyw088, PMID: 27342221
68. Swerdlow DI, Preiss D, Kuchenbaecker KB, Holmes MV, Engmann JE, Shah T, Sofat R, Stender S, Johnson PC, Scott RA, Leusink M, Verweij N, Sharp SJ, Guo Y, Giambartolomei C, Chung C, Peasey A, Amuzu A, Li K, Palmen J, et al. 2015. HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials. Lancet 385:351–361. DOI: https://doi.org/10.1016/S0140-6736(14) 61183-1, PMID: 25262344
69. VanderWeele TJ, Tchetgen Tchetgen EJ, Cornelis M, Kraft P. 2014. Methodological challenges in mendelian randomization. Epidemiology 25:427–435. DOI: https://doi.org/10.1097/EDE.0000000000000081, PMID: 24681576
70. Verbanck M, Chen CY, Neale B, Do R. 2018. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics: 693–698. DOI: https://doi.org/10.1038/s41588-018-0099-7, PMID: 29686387
71. Visscher PM, Brown MA, McCarthy MI, Yang J. 2012. Five years of GWAS discovery. The American Journal of Human Genetics 90:7–24. DOI: https://doi.org/10.1016/j.ajhg.2011.11.029, PMID: 22243964
72. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 2017. 10 Years of GWAS Discovery: Biology, Function, and Translation. The American Journal of Human Genetics 101:5–22. DOI: https://doi.org/10.1016/j.ajhg.2017.06.005, PMID: 28686856
73. 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 Research 42:D1001–D1006. DOI: https://doi.org/10.1093/nar/gkt1229, PMID: 24316577
74. White J, Swerdlow DI, Preiss D, Fairhurst-Hunter Z, Keating BJ, Asselbergs FW, Sattar N, Humphries SE, Hingorani AD, Holmes MV. 2016. Association of Lipid Fractions With Risks for Coronary Artery Disease and Diabetes. JAMA Cardiology 1:692–699. DOI: https://doi.org/10.1001/jamacardio.2016.1884, PMID: 27487401
75. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, et al. 2013. Discovery and refinement of loci associated with lipid levels. Nature Genetics 45:1274–1283. DOI: https://doi.org/10.1038/ng.2797, PMID: 24097068
76. Wood AR, Tyrrell J, Beaumont R, Jones SE, Tuke MA, Ruth KS, Yaghootkar H, Freathy RM, Murray A, Frayling TM, Weedon MN, GIANT consortium. 2016. Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively. Diabetologia 59:1214–1221. DOI: https://doi.org/10.1007/s00125-016-3908-5, PMID: 26961502
77. Yavorska OO, Burgess S. 2017. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. International Journal of Epidemiology 46:1734–1739. DOI: https://doi.org/10.1093/ije/dyx034, PMID: 28398548
78. Zhao Q, Wang J, Hemani G, Bowden J, Small DS. 2018. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. arXiv. https://arxiv.org/abs/1801.09652.
79. Zheng J, Baird D, Borges MC, Bowden J, Hemani G, Haycock P, Evans DM, Smith GD. 2017a. Recent developments in mendelian randomization studies. Current Epidemiology Reports 4:330–345. DOI: https://doi. org/10.1007/s40471-017-0128-6, PMID: 29226067
80. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, Hemani G, Tansey K, Laurin C, Pourcain BS, Warrington NM, Finucane HK, Price AL, Bulik-Sullivan BK, Anttila V, Paternoster L, Gaunt TR, Evans DM, Neale BM, Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium. 2017b. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33:272–279. DOI: https://doi.org/10.1093/bioinformatics/btw613, PMID: 27663502
81. 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. Nature genetics 48:481–487. DOI: https://doi.org/10.1038/ng.3538, PMID: 27019110
82. Zollner S, Pritchard JK. 2007. Overcoming the winner’s curse: estimating penetrance parameters from case-control data. The American Journal of Human Genetics 80:605–615. DOI: https://doi.org/10.1086/512821, PMID: 17357068