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The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Guidelines for performing Mendelian randomization investigations

Open access
Burgess, Stephen, Davey Smith, George, Davies, Neil M., Dudbridge, Frank, Gill, Dipender, Glymour, M. Maria, Hartwig, Fernando P., Holmes, Michael V., Minelli, Cosetta, Relton, Caroline L., Theodoratou, Evropi·2019·Wellcome Open Research
DOI 10.12688/wellcomeopenres.15555.1
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Abstract

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.

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Research works printed but not linked: 59

  1. 2019, STROBE-MR: Guidelines for strengthening the reporting of Mendelian randomization studies., PeerJ., 10.7287/peerj.preprints.27857 · DOI
  2. V Didelez, 2007, Mendelian randomization as an instrumental variable approach to causal inference., Stat Methods Med Res., 16, 309-330, 10.1177/0962280206077743 · DOI · Paper page
  3. T VanderWeele, 2014, Methodological challenges in Mendelian randomization., Epidemiology., 25, 427-435, 10.1097/EDE.0000000000000081 · DOI · Paper page
  4. L Millard, 2015, MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization., Sci Rep., 5, 10.1038/srep16645 · DOI
  5. X Li, 2018, MR-PheWAS: exploring the causal effect of SUA level on multiple disease outcomes by using genetic instruments in UK Biobank., Ann Rheum Dis., 77, 1039-1047, 10.1136/annrheumdis-2017-212534 · DOI
  6. G Hemani, 2018, The MR-Base platform supports systematic causal inference across the human phenome., eLife., 7, 10.7554/eLife.34408 · DOI · Paper page
  7. S Burgess, 2013, Mendelian randomization analysis with multiple genetic variants using summarized data., Genet Epidemiol., 37, 658-665, 10.1002/gepi.21758 · DOI · Paper page
  8. J Rees, 2019, Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates., PLoS One., 14, e0222362, 10.1371/journal.pone.0222362 · DOI · Paper page
  9. Q Zhao, 2018, Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score., arXiv.
  10. S Burgess, 2019, A robust and efficient method for Mendelian randomization with hundreds of genetic variants: unravelling mechanisms linking HDL-cholesterol and coronary heart disease., bioRxiv., 10.1101/566851 · DOI
  11. E Slob, 2019, A comparison of robust Mendelian randomization methods using summary data., bioRxiv., 10.1101/577940 · doi:10.1101/577940
  12. S Burgess, 2016, Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods., Stat Med., 35, 1880-1906, 10.1002/sim.6835 · DOI · Paper page
  13. S Swanson, 2018, Causal null hypotheses of sustained treatment strategies: What can be tested with an instrumental variable?, Eur J Epidemiol., 33, 723-728, 10.1007/s10654-018-0396-6 · DOI
  14. J Bowden, 2017, A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization., Stat Med., 36, 1783-1802, 10.1002/sim.7221 · DOI · Paper page
  15. S Burgess, 2014, Instrumental variable analysis with a nonlinear exposure-outcome relationship., Epidemiology., 25, 877-885, 10.1097/EDE.0000000000000161 · DOI
  16. J Staley, 2017, Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization., Genet Epidemiol., 41, 341-352, 10.1002/gepi.22041 · DOI · Paper page
  17. B Pierce, 2013, Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators., Am J Epidemiol., 178, 1177-1184, 10.1093/aje/kwt084 · DOI · Paper page
  18. S Burgess, 2016, Bias due to participant overlap in two-sample Mendelian randomization., Genet Epidemiol., 40, 597-608, 10.1002/gepi.21998 · DOI · Paper page
  19. D Swerdlow, 2016, Selecting instruments for Mendelian randomization in the wake of genome-wide association studies., Int J Epidemiol., 45, 1600-1616, 10.1093/ije/dyw088 · DOI · Paper page
  20. S Burgess, 2017, Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants., Epidemiology., 28, 30-42, 10.1097/EDE.0000000000000559 · DOI · Paper page
  21. M Brion, 2013, Calculating statistical power in Mendelian randomization studies., Int J Epidemiol., 42, 1497-1501, 10.1093/ije/dyt179 · DOI · Paper page
  22. Q Zhao, 2019, Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization., Int J Epidemiol., 48, 1478-1492, 10.1093/ije/dyz142 · DOI · Paper page
  23. G Davey Smith, 2014, Mendelian randomization: genetic anchors for causal inference in epidemiological studies., Hum Mol Genet., 23, R89-R98, 10.1093/hmg/ddu328 · DOI · Paper page
  24. F Hartwig, 2016, Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique., Int J Epidemiol., 45, 1717-1726, 10.1093/ije/dyx028 · DOI · Paper page
  25. D Lawlor, 2008, Mendelian randomization: using genes as instruments for making causal inferences in epidemiology., Stat Med., 27, 1133-1163, 10.1002/sim.3034 · DOI · Paper page
  26. T Palmer, 2011, Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses., Am J Epidemiol., 173, 1392-1403, 10.1093/aje/kwr026 · DOI · Paper page
  27. A Inoue, 2010, Two-Sample Instrumental Variables Estimators., Rev Econ Stat., 92, 557-561, 10.1162/REST_a_00011 · DOI · Paper page
  28. J Thompson, 2016, Mendelian Randomization using Public Data from Genetic Consortia., Int J Biostat., 12, 10.1515/ijb-2015-0074 · DOI
  29. S Walter, 2015, Revisiting Mendelian randomization studies of the effect of body mass index on depression., Am J Med Genet B Neuropsychiatr Genet., 168B, 108-115, 10.1002/ajmg.b.32286 · DOI
  30. J Bowden, 2017, A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization., Stat Med., 36, 1783-1802, 10.1002/sim.7221 · DOI · Paper page
  31. S Burgess, 2015, Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects., Am J Epidemiol., 181, 251-260, 10.1093/aje/kwu283 · DOI · Paper page
  32. E Sanderson, 2019, An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings., Int J Epidemiol., 48, 713-727, 10.1093/ije/dyy262 · DOI · Paper page
  33. S Burgess, 2014, Using multivariable Mendelian randomization to disentangle the causal effects of lipid fractions., PLoS One., 9, e108891, 10.1371/journal.pone.0108891 · DOI
  34. S Larsson, 2019, Body mass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: a Mendelian randomization study., Euro Heart J., 10.1093/eurheartj/ehz388 · DOI · Paper page
  35. S Burgess, 2017, Dissecting Causal Pathways Using Mendelian Randomization with Summarized Genetic Data: Application to Age at Menarche and Risk of Breast Cancer., Genetics., 207, 481-487, 10.1534/genetics.117.300191 · DOI · Paper page
  36. J Rees, 2017, Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy., Stat Med., 36, 4705-4718, 10.1002/sim.7492 · DOI · Paper page
  37. J Bowden, 2016, Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic., Int J Epidemiol., 45, 1961-1974, 10.1093/ije/dyw220 · DOI · Paper page
  38. J Thompson, 2017, Mendelian randomization incorporating uncertainty about pleiotropy., Stat Med., 36, 4627-4645, 10.1002/sim.7442 · DOI
  39. S Burgess, 2018, Modal-based estimation via heterogeneity-penalized weighting: model averaging for consistent and efficient estimation in Mendelian randomization when a plurality of candidate instruments are valid., Int J Epidemiol., 47, 1242-1254, 10.1093/ije/dyy080 · DOI
  40. G Qi, 2019, A Comprehensive Evaluation of Methods for Mendelian Randomization Using Realistic Simulations of Genome-wide Association Studies., bioRxiv., 10.1101/702787 · DOI
  41. J Bowden, 2019, Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption., Int J Epidemiol., 48, 728-742, 10.1093/ije/dyy258 · DOI · Paper page
  42. J Bowden, 2018, Invited Commentary: Detecting Individual and Global Horizontal Pleiotropy in Mendelian Randomization-A Job for the Humble Heterogeneity Statistic?, Am J Epidemiol., 187, 2681-2685, 10.1093/aje/kwy185 · DOI · Paper page
  43. L Corbin, 2016, BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization., Diabetes., 65, 3002-3007, 10.2337/db16-0418 · DOI · Paper page
  44. J Smith, 2014, Association of low-density lipoprotein cholesterol-related genetic variants with aortic valve calcium and incident aortic stenosis., JAMA., 312, 1764-1771, 10.1001/jama.2014.13959 · DOI · Paper page
  45. Y Cho, 2018, MR-TRYX: Exploiting horizontal pleiotropy to infer novel causal pathways., bioRxiv., 10.1101/476085 · DOI
  46. C Minelli, 2018, Age at puberty and risk of asthma: A Mendelian randomisation study., PLoS Med., 15, e1002634, 10.1371/journal.pmed.1002634 · DOI
  47. F Hormozdiari, 2014, Identifying causal variants at loci with multiple signals of association., Genetics., 198, 497-508, 10.1534/genetics.114.167908 · DOI · Paper page
  48. N Solovieff, 2013, Pleiotropy in complex traits: challenges and strategies., Nat Rev Genet., 14, 483, 10.1038/nrg3461 · DOI · Paper page
  49. I Millwood, 2019, Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China., Lancet., 393, 1831-1842, 10.1016/S0140-6736(18)31772-0 · DOI · Paper page
  50. H van Kippersluis, 2018, Pleiotropy-robust Mendelian randomization., Int J Epidemiol., 47, 1279-1288, 10.1093/ije/dyx002 · DOI · Paper page
  51. W Spiller, 2019, Detecting and correcting for bias in Mendelian randomization analyses using Gene-by-Environment interactions., Int J Epidemiol., 48, 702-712, 10.1093/ije/dyy204 · DOI · Paper page
  52. M Munafò, 2018, Collider scope: when selection bias can substantially influence observed associations., Int J Epidemiol., 47, 226-235, 10.1093/ije/dyx206 · DOI · Paper page
  53. A Gkatzionis, 2019, Contextualizing selection bias in Mendelian randomization: how bad is it likely to be?, Int J Epidemiol., 48, 691-701, 10.1093/ije/dyy202 · DOI · Paper page
  54. J White, 2016, Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis., Lancet Diabetes Endocrinol., 4, 327-336, 10.1016/S2213-8587(15)00386-1 · DOI · Paper page
  55. J Bowden, 2018, Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression., Int J Epidemiol., 47, 1264-1278, 10.1093/ije/dyy101 · DOI · Paper page
  56. P Haycock, 2016, Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies., Am J Clin Nutr., 103, 965-978, 10.3945/ajcn.115.118216 · DOI · Paper page
  57. M Munafò, 2018, Robust research needs many lines of evidence., Nature., 553, 399-401, 10.1038/d41586-018-01023-3 · DOI · Paper page
  58. M Greco, 2015, Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome., Stat Med., 34, 2926-2940, 10.1002/sim.6522 · DOI · Paper page
  59. M Holmes, 2017, Mendelian randomization in cardiometabolic disease: challenges in evaluating causality., Nat Rev Cardiol., 14, 577-590, 10.1038/nrcardio.2017.78 · DOI · Paper page

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Metadata

Authors
Burgess, S., Davey Smith, G., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., & Theodoratou, E.
Venue
Wellcome Open Research
Publisher
F1000 Research Ltd
Published
2019-11-26
Volume
4
Pages
186
ISSN
2398-502X (ISSN-L)
DOI
10.12688/wellcomeopenres.15555.1
OpenAlex
W4253754191
Cited by
1,834
References
41
FWCI
41.99
Type
journal article
Language
en
Update notices
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (tdm)
Funders
  • Economic and Social Research Council (ES/N000757/1)
  • Cancer Research UK (C31250/A22804)
  • Wellcome Trust (204623)
  • Medical Research Council (MC_UU_00011/5, MC_UU_12013/9, MC_UU_00011/1, MC_UU_12013/1)
  • British Heart Foundation (FS/18/23/33512)

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