Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption
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Abstract
Background: Mendelian randomization (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions. Methods: Here, a new method - the mode-based estimate (MBE) - is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk. Results: The MBE presented less bias and lower type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared with the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia. Conclusions: The MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in sensitivity analyses.
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Additional approaches to test the exclusion restriction include weighted median and mode.
“Additional approaches to test the exclusion restriction include weighted median56 and mode.57”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [57]
The MR-PRESSO method is a variation on the IVW method that first sequentially removes genetic variants from the analysis whose variant-specific causal estimate differs substantially from those of other variants.
“The MR-PRESSO method is a variation on the IVW method that first sequentially removes genetic variants from the analysis whose variant-specific causal estimate differs substantially from those of other variants 21 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [21]
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Cites
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,938 · see citing works
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · see citing works
Self-citationBowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · see citing works
Self-citationDensity Estimation for Statistics and Data Analysis
(1986) · Springer eBooks · doi:10.1007/978-1-4899-3324-9 · Cited by 8,478 · see citing works
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050 · see citing works
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882 · see citing works
Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, G., & Hemani, G. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu328 · Cited by 4,677 · see citing works
Willer (2013) · doi:10.1038/ng.2797 · Cited by 2,983 · see citing works
Burgess, S., Bowden, J., Fall, T., Ingelsson, E., & Thompson, S. G. (2017) · Epidemiology · doi:10.1097/ede.0000000000000559 · Cited by 2,017 · see citing works
Self-citationA framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization
Bowden, J., Del Greco M, F., Minelli, C., Davey Smith, G., Sheehan, N., & Thompson, J. (2017) · Statistics in Medicine · doi:10.1002/sim.7221 · Cited by 1,887 · see citing works
Self-citationEPIC- InterAct Consortium, Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G., & Thompson, S. G. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-015-0011-z · Cited by 1,803 · see citing works
Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome
Greco M, F. D., Minelli, C., Sheehan, N. A., & Thompson, J. R. (2015) · Statistics in Medicine · doi:10.1002/sim.6522 · Cited by 1,700 · see citing works
Large-scale association analysis identifies new risk loci for coronary artery disease
The CARDIoGRAMplusC4D Consortium, Deloukas, P., DIAGRAM Consortium, CARDIOGENICS Consortium, MuTHER Consortium, Wellcome Trust Case Control Consortium, Kanoni, S., Willenborg, C., Farrall, M., Assimes, T. L., et al. (2013) · Nature Genetics · doi:10.1038/ng.2480 · Cited by 1,614 · see citing works
Bowden, J., Fabiola Del Greco, M., Minelli, C., Smith, G. D., Sheehan, N. A., & Thompson, J. R. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw220 · Cited by 1,572 · see citing works
Self-citationCommon variants associated with plasma triglycerides and risk for coronary artery disease
Do, R., Willer, C. J., Schmidt, E. M., Sengupta, S., Gao, C., Peloso, G. M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., et al. (2013) · Nature Genetics · doi:10.1038/ng.2795 · Cited by 862 · see citing works
Hartwig, F. P., Davies, N. M., Hemani, G., & Davey Smith, G. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyx028 · Cited by 860 · see citing works
Self-citationHaycock, P., Burgess, S., Wade, K. H., Bowden, J., Relton, C. L., & Smith, G. D. (2016) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.118216 · Cited by 602 · see citing works
Self-citationMendelian randomization: where are we now and where are we going?
Burgess, S., Timpson, N. J., Ebrahim, S. B. J., & Smith, G. D. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv108 · Cited by 308 · see citing works
Kang, H., Zhang, A., Cai, T. T., & Small, D. S. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.994705 · Cited by 265 · see citing works
Commentary: The concept of 'Mendelian Randomization'
Thomas, D. C. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh048 · Cited by 217 · see citing works
Thomas, D. C., Lawlor, D. A., & Thompson, J. R. (2007) · Annals of Epidemiology · doi:10.1016/j.annepidem.2006.12.005 · Cited by 138 · see citing works
Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.
White, J., Sofat, R., Hemani, G., Shah, T., Engmann, J., Dale, C., Shah, S., Kruger, F. A., Giambartolomei, C., Swerdlow, D. I., et al. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00386-1 · Cited by 132 · see citing works
On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications
Bickel, D. R., & Fruhwirth, R. K. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2005.07.011 · Cited by 115 · see citing works
Some Direct Estimates of the Mode
Grenander, U. (1965) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177700277 · Cited by 81 · see citing works
Bickel, D. R. (2003) · Journal of Statistical Computation and Simulation · doi:10.1080/0094965031000097809 · Cited by 53 · see citing works
Severity of bias of a simple estimator of the causal odds ratio in Mendelian randomization studies
Harbord, R. M., Didelez, V., Palmer, T., Meng, S., Sterne, J. A. C., & Sheehan, N. A. (2012) · Statistics in Medicine · doi:10.1002/sim.5659 · Cited by 46 · see citing works
Confidence Intervals for Causal Effects with Invalid Instruments using Two-Stage Hard ThresholdingPreprint
Guo, Z., Kang, H., Cai, T., & Small, D. S. (2016) · arXiv (Cornell University) · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 28 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Verbanck, M., Chen, C.-Y., Neale, B., & Do, R. (2018) · Nature Genetics · doi:10.1038/s41588-018-0099-7 · Cited by 8,591 · see citing works
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959 · see citing works
Self-citationReading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians
Davies, N. M., Holmes, M. V., & Davey Smith, G. (2018) · BMJ · doi:10.1136/bmj.k601 · Cited by 4,404 · see citing works
Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229 · see citing works
Large-scale association analyses identify host factors influencing human gut microbiome composition
Kurilshikov, A., Medina-Gomez, C., Bacigalupe, R., Radjabzadeh, D., Wang, J., Demirkan, A., Le Roy, C. I., Raygoza Garay, J. A., Finnicum, C. T., Liu, X., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00763-1 · Cited by 1,786 · see citing works
Guidelines for performing Mendelian randomization investigations
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2019) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.1 · Cited by 1,852 · see citing works
Self-citationMeta‐analysis and Mendelian randomization: A review
Bowden, J., & Holmes, M. V. (2019) · Research Synthesis Methods · doi:10.1002/jrsm.1346 · Cited by 1,674 · see citing works
Self-citationSkrivankova, V. W., Richmond, R. C., Woolf, B. A. R., Davies, N. M., Swanson, S. A., VanderWeele, T. J., Timpson, N. J., Higgins, J. P. T., Dimou, N., Langenberg, C., et al. (2021) · BMJ · doi:10.1136/bmj.n2233 · Cited by 1,648 · see citing works
Evaluating the potential role of pleiotropy in Mendelian randomization studies
Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496 · see citing works
Self-citationGuidelines for performing Mendelian randomization investigations
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., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.2 · Cited by 1,273 · see citing works
Self-citationRecent Developments in Mendelian Randomization Studies
Zheng, J., Baird, D. A., Borges, M. C., Bowden, J., Hemani, G., Haycock, P., Evans, D. M., & Smith, G. D. (2017) · Current Epidemiology Reports · doi:10.1007/s40471-017-0128-6 · Cited by 1,132 · see citing works
Self-citationRichardson, T. G., Sanderson, E., Palmer, T. M., Ala-Korpela, M., Ference, B. A., Davey Smith, G., & Holmes, M. V. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003062 · Cited by 843 · see citing works
Zhao, Q., Wang, J., Hemani, G., Bowden, J., & Small, D. S. (2020) · The Annals of Statistics · doi:10.1214/19-aos1866 · Cited by 854 · see citing works
Self-citationMendelian Randomization: Concepts and Scope
Richmond, R. C., & Davey Smith, G. (2022) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a040501 · Cited by 841 · see citing works
Multivariable Mendelian Randomization and MediationPreprint
Sanderson, E. C. M. (2020) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a038984 · Cited by 829 · see citing works
Guidelines for performing Mendelian randomization investigations: update for summer 2023
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Kutalik, Z., Holmes, M. V., Minelli, C., et al. (2023) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.3 · Cited by 737 · see citing works
Self-citationA robust and efficient method for Mendelian randomization with hundreds of genetic variants
Burgess, S., Foley, C. N., Allara, E., Staley, J. R., & Howson, J. M. M. (2020) · Nature Communications · doi:10.1038/s41467-019-14156-4 · Cited by 681 · see citing works
Mendelian randomization in cardiometabolic disease: challenges in evaluating causality
Holmes, M. V., Ala-Korpela, M., & Smith, G. D. (2017) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2017.78 · Cited by 679 · see citing works
A comparison of robust Mendelian randomization methods using summary data
Slob, E. A. W., & Burgess, S. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22295 · Cited by 636 · see citing works
Stroke genetics informs drug discovery and risk prediction across ancestries
Mishra, A., Malik, R., Hachiya, T., Jürgenson, T., Namba, S., Posner, D. C., Kamanu, F., Koido, M., Grand, Q. L., Shi, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05165-3 · Cited by 591 · see citing works
Sanderson, E. C. M., Spiller, W., & Bowden, J. (2021) · Statistics in Medicine · doi:10.1002/sim.9133 · Cited by 603 · see citing works
Self-citationMorrison, J., Knoblauch, N., Marcus, J. H., Stephens, M., & He, X. (2020) · Nature Genetics · doi:10.1038/s41588-020-0631-4 · Cited by 578 · see citing works
Qin, Y., Havulinna, A. S., Liu, Y., Jousilahti, P., Ritchie, S. C., Tokolyi, A., Sanders, J. G., Valsta, L., Brożyńska, M., Zhu, Q., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00991-z · Cited by 586 · see citing works
Wootton, R. E., Richmond, R. C., Stuijfzand, B. G., Lawn, R. B., Sallis, H. M., Taylor, G. M. J., Hemani, G., Jones, H. J., Zammit, S., Davey Smith, G., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291719002678 · Cited by 587 · see citing works
Li, P., Wang, H., Guo, L., Gou, X., Chen, G., Lin, D., Fan, D., Guo, X., & Liu, Z. (2022) · BMC Medicine · doi:10.1186/s12916-022-02657-x · Cited by 577 · see citing works
15 years of GWAS discovery: Realizing the promise
Abdellaoui, A., Yengo, L., Verweij, K. J. H., & Visscher, P. M. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.12.011 · Cited by 542 · see citing works
Pasman, J. A., Verweij, K. J. H., Gerring, Z. F., Stringer, S., Sanchez‐Roige, S., Treur, J. L., Abdellaoui, A., Nivard, M. G., Baselmans, B. M. L., Ong, J., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0206-1 · Cited by 496 · see citing works
Bias correction for inverse variance weighting Mendelian randomization
Mounier, N., & Kutalik, Z. (2023) · Genetic Epidemiology · doi:10.1002/gepi.22522 · Cited by 445 · see citing works
Using genetic data to strengthen causal inference in observational research
Pingault, J.-B., O’Reilly, P. F., Schoeler, T., Ploubidis, G. B., Rijsdijk, F., & Dudbridge, F. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0020-3 · Cited by 444 · see citing works
Brumpton, B., Sanderson, E., Heilbron, K., Hartwig, F. P., Harrison, S., Vie, G. Å., Cho, Y., Howe, L. D., Hughes, A., Boomsma, D. I., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17117-4 · Cited by 402 · see citing works
Self-citationGWAS identifies 14 loci for device-measured physical activity and sleep duration
Doherty, A., Smith-Byrne, K., Ferreira, T., Holmes, M. V., Holmes, C., Pulit, S. L., & Lindgren, C. M. (2018) · Nature Communications · doi:10.1038/s41467-018-07743-4 · Cited by 408 · see citing works
Mendelian randomization study of inflammatory bowel disease and bone mineral density
Wu, F., Huang, Y., Hu, J., & Shao, Z. (2020) · BMC Medicine · doi:10.1186/s12916-020-01778-5 · Cited by 404 · see citing works
Causal relationship between gut microbiota and cancers: a two-sample Mendelian randomisation study
Long, Y., Tang, L., Zhou, Y., Zhao, S., & Zhu, H. (2023) · BMC Medicine · doi:10.1186/s12916-023-02761-6 · Cited by 401 · see citing works
Distinguishing genetic correlation from causation across 52 diseases and complex traits
O’Connor, L. J., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0255-0 · Cited by 394 · see citing works
Liu, X., Tong, X., Zou, Y., Lin, X., Zhao, H., Tian, L., Jie, Z., Wang, Q., Zhang, Z., Lu, H., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00968-y · Cited by 377 · see citing works
The Mediterranean diet, plasma metabolome, and cardiovascular disease risk
Li, J., Guasch‐Ferré, M., Chung, W., Ruiz‐Canela, M., Toledo, E., Corella, D., Bhupathiraju, S. N., Tobias, D. K., Tabung, F. K., Hu, J., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa209 · Cited by 364 · see citing works
Xue, H., Shen, X., & Pan, W. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.05.014 · Cited by 355 · see citing works
Ong, J., & MacGregor, S. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22207 · Cited by 350 · see citing works
The use of two-sample methods for Mendelian randomization analyses on single large datasets
Minelli, C., Fabiola Del Greco, M., van der Plaat, D. A., Bowden, J., Sheehan, N. A., & Thompson, J. R. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyab084 · Cited by 328 · see citing works
Self-citationPorcu, E., Rüeger, S., Lepik, K., eQTLGen Consortium, Agbessi, M., Ahsan, H., Alves, I., Andiappan, A., Arindrarto, W., Awadalla, P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10936-0 · Cited by 322 · see citing works
Statistical methods for Mendelian randomization in genome-wide association studies: A review
Boehm, F. J., & Zhou, X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.015 · Cited by 328 · see citing works
Lin, Z., Deng, Y., & Pan, W. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009922 · Cited by 321 · see citing works
Causal role of immune cells in schizophrenia: Mendelian randomization (MR) study
Wang, C., Zhu, D., Zhang, D., Zuo, X., Yao, L., Liu, T., Ge, X., He, C., Zhou, Y., & Shen, Z. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-05081-4 · Cited by 317 · see citing works
Burgess, S., Foley, C. N., & Zuber, V. (2018) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083117-021731 · Cited by 287 · see citing works
Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging
McCartney, D. L., Min, J. L., Richmond, R. C., Lu, A. T.-H., Sobczyk, M., Davies, G., Broer, L., Guo, X., Jeong, A., Jung, J., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02398-9 · Cited by 304 · see citing works
Tadros, R., Francis, C., Xu, X. Y., Vermeer, A. M. C., Harper, A. R., Huurman, R., Bisabu, K. K., Walsh, R., Hoorntje, E. T., Rijdt, W. P. T., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00762-2 · Cited by 283 · see citing works
Advancing the use of genome-wide association studies for drug repurposing
Reay, W. R., & Cairns, M. J. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00387-z · Cited by 277 · see citing works
Bowden, J., Hemani, G., & Davey Smith, G. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy185 · Cited by 251 · see citing works
Self-citationBudu‐Aggrey, A., Brumpton, B. M., Tyrrell, J., Watkins, S., Modalsli, E. H., Celis-Morales, C. A., Ferguson, L. D., Vie, G. Å., Palmer, T., Fritsche, L. G., et al. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002739 · Cited by 266 · see citing works
Hughes, D. A., Bacigalupe, R., Wang, J., Rühlemann, M. C., Tito, R. Y., Falony, G., Joossens, M., Vieira‐Silva, S., Henckaerts, L., Rymenans, L., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0743-8 · Cited by 242 · see citing works
Zuber, V., Colijn, J. M., Klaver, C. C. W., & Burgess, S. (2020) · Nature Communications · doi:10.1038/s41467-019-13870-3 · Cited by 237 · see citing works
Richardson, T. G., Harrison, S., Hemani, G., & Davey Smith, G. (2019) · eLife · doi:10.7554/elife.43657 · Cited by 198 · see citing works
Wang, Z., Emmerich, A., Pillon, N. J., Moore, T. M., Hemerich, D., Cornelis, M. C., Mazzaferro, E., Broos, S., Ahluwalia, T. S., Bartz, T. M., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01165-1 · Cited by 230 · see citing works
Self-citationZhuang, Z., Yang, R., Wang, W., Qi, L., & Huang, T. (2020) · Journal of Neuroinflammation · doi:10.1186/s12974-020-01961-8 · Cited by 220 · see citing works
Education and coronary heart disease: mendelian randomisation study
Tillmann, T., Vaucher, J., Okbay, A., Pikhart, H., Peasey, A., Kubinova, R., Pajak, A., Tamosiunas, A., Malyutina, S., Hartwig, F. P., et al. (2017) · BMJ · doi:10.1136/bmj.j3542 · Cited by 225 · see citing works
Self-citationQi, G., & Chatterjee, N. (2019) · Nature Communications · doi:10.1038/s41467-019-09432-2 · Cited by 207 · see citing works
Genetic analysis of over half a million people characterises C-reactive protein loci
Said, S., Pazoki, R., Karhunen, V., Võsa, U., Ligthart, S., Bodinier, B., Koskeridis, F., Welsh, P. I., Alizadeh, B. Z., Chasman, D. I., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29650-5 · Cited by 209 · see citing works
Jia, J., Dou, P., Gao, M., Kong, X., Li, C., Liu, Z., & Huang, T. (2019) · Diabetes · doi:10.2337/db19-0153 · Cited by 193 · see citing works
Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?
Yarmolinsky, J., Wade, K. H., Richmond, R. C., Langdon, R., Bull, C. J., Tilling, K. M., Relton, C. L., Lewis, S. J., Smith, G. D., & Martin, R. M. (2018) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-17-1177 · Cited by 186 · see citing works
Yin, K., Huang, J.-X., Wang, P., Yang, X.-K., Tao, S.-S., Li, H.-M., Ni, J., & Pan, H.-F. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.808832 · Cited by 140 · see citing works
Wang, Q., Holmes, M. V., Smith, G. D., & Ala‐Korpela, M. (2017) · Diabetes Care · doi:10.2337/dc17-1642 · Cited by 184 · see citing works
Cao, Z., Wu, Y., Li, Q., Li, Y., & Wu, J. (2022) · Annals of Medicine · doi:10.1080/07853890.2022.2085883 · Cited by 183 · see citing works
Zhou, W., Liu, G., Hung, R. J., Haycock, P. C., Aldrich, M. C., Andrew, A. S., Arnold, S. M., Bickeböller, H., Bojesen, S. E., Brennan, P., et al. (2021) · International Journal of Cancer · doi:10.1002/ijc.33292 · Cited by 172 · see citing works
Xu, J., Zhang, S., Tian, Y., Si, H., Zeng, Y., Wu, Y., Liu, Y., Li, M., Sun, K., Wu, L., et al. (2022) · Nutrients · doi:10.3390/nu14183683 · Cited by 171 · see citing works
Malik, R., Georgakis, M. K., Neitzel, J., Rannikmäe, K., Ewers, M., Seshadri, S. S., Sudlow, C. L. M., & Dichgans, M. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12320 · Cited by 170 · see citing works
Understanding the assumptions underlying Mendelian randomization
de Leeuw, C., Savage, J. E., Bucur, I. G., Heskes, T., & Posthuma, D. (2022) · European Journal of Human Genetics · doi:10.1038/s41431-022-01038-5 · Cited by 169 · see citing works
Role of obesity in smoking behaviour: Mendelian randomisation study in UK Biobank
Carreras‐Torres, R., Johansson, M., Haycock, P., Relton, C. L., Smith, G. D., Brennan, P., & Martin, R. M. (2018) · BMJ · doi:10.1136/bmj.k1767 · Cited by 169 · see citing works
McCormick, N., O’Connor, M. J., Yokose, C., Merriman, T. R., Mount, D. B., Leong, A., & Choi, H. K. (2021) · Arthritis & Rheumatology · doi:10.1002/art.41779 · Cited by 173 · see citing works
Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study
Nazarzadeh, M., Pinho-Gomes, A.-C., Bidel, Z., Dehghan, A., Canoy, D., Hassaine, A., Ayala Solares, J. R., Salimi-Khorshidi, G., Smith, G. D., Otto, C. M., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa070 · Cited by 163 · see citing works
Adiposity, metabolites, and colorectal cancer risk: Mendelian randomization study
Bull, C. J., Bell, J. A., Murphy, N., Sanderson, E. C. M., Smith, G. D., Timpson, N. J., Banbury, B. L., Albanes, D., Berndt, S. I., Bezieau, S., et al. (2020) · BMC Medicine · doi:10.1186/s12916-020-01855-9 · Cited by 167 · see citing works
Leong, A., Cole, J. B., Brenner, L. N., Meigs, J. B., Florez, J. C., & Mercader, J. M. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003553 · Cited by 165 · see citing works
Within family Mendelian randomization studies
Davies, N. M., Howe, L. J., Brumpton, B., Havdahl, A., Evans, D. M., & Davey Smith, G. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz204 · Cited by 158 · see citing works
Richmond, R. C., Anderson, E. L., Dashti, H. S., Jones, S. E., Lane, J. M., Strand, L. B., Brumpton, B. M., Rutter, M. K., Wood, A. R., Straíf, K., et al. (2019) · BMJ · doi:10.1136/bmj.l2327 · Cited by 152 · see citing works
Mendelian Randomization Study of Obesity and Cerebrovascular Disease
Marini, S., Merino, J., Montgomery, B. E., Malik, R., Sudlow, C. L. M., Dichgans, M., Florez, J. C., Rosand, J. M., Gill, D., & Anderson, C. D. (2020) · Annals of Neurology · doi:10.1002/ana.25686 · Cited by 152 · see citing works
Broadbent, J. R., Foley, C. N., Grant, A. J., Mason, A. M., Staley, J. R., & Burgess, S. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.16374.2 · Cited by 145 · see citing works
Maina, J. G., Balkhiyarova, Z., Nouwen, A., Pupko, I., Ulrich, A., Boissel, M., Bonnefond, A., Froguel, P., Khamis, A., Prokopenko, I., et al. (2023) · Diabetes Care · doi:10.2337/dc22-2373 · Cited by 151 · see citing works
van de Vegte, Y. J., Said, M. A., Rienstra, M., van der Harst, P., & Verweij, N. (2020) · Nature Communications · doi:10.1038/s41467-020-15553-w · Cited by 144 · see citing works
Genetic and functional insights into the fractal structure of the heart
Meyer, H. V., Dawes, T. J. W., Serrani, M., Bai, W., Tokarczuk, P. F., Cai, J., de Marvao, A., Henry, A., Lumbers, R. T., Gierten, J., et al. (2020) · Nature · doi:10.1038/s41586-020-2635-8 · Cited by 137 · see citing works
Large-scale association analyses identify host factors influencing human gut microbiome compositionPreprint
Kurilshikov, A., Medina‐Gómez, C., Bacigalupe, R., Radjabzadeh, D., Wang, J., Demirkan, A., Roy, C. I. L., Garay, J. A. R., Finnicum, C. T., Liu, X., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.26.173724 · Cited by 143 · see citing works
Karhunen, V., Bakker, M. K., Ruigrok, Y. M., Gill, D., & Larsson, S. C. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.121.022277 · Cited by 143 · see citing works
Yao, S., Zhang, M., Dong, S., Wang, J.-H., Zhang, K., Guo, J., Guo, Y., & Yang, T. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01412-9 · Cited by 143 · see citing works
Yu, Z., Coresh, J., Qi, G., Grams, M., Boerwinkle, E., Snieder, H., Teumer, A., Pattaro, C., Köttgen, A., Chatterjee, N., et al. (2020) · Kidney International · doi:10.1016/j.kint.2020.04.044 · Cited by 135 · see citing works
Diabetes Mellitus, Glycemic Traits, and Cerebrovascular Disease
Georgakis, M. K., Harshfield, E. L., Malik, R., Franceschini, N., Langenberg, C., Wareham, N. J., Markus, H. S., & Dichgans, M. (2021) · Neurology · doi:10.1212/wnl.0000000000011555 · Cited by 143 · see citing works
Guo, J., Yu, K., Dong, S., Yao, S., Rong, Y., Wu, H., Zhang, K., Jiang, F., Chen, Y.-X., Guo, Y., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01174-7 · Cited by 143 · see citing works
Porcu, E., Sadler, M. C., Lepik, K., Auwerx, C., Wood, A. R., Weihs, A., Sleiman, M. S. B., Ribeiro, D. M., Bandinelli, S., Tanaka, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25805-y · Cited by 139 · see citing works
Wootton, R. E., Lawn, R. B., Millard, L. A. C., Davies, N. M., Taylor, A. E., Munafò, M. R., Timpson, N. J., Davis, O. S. P., Davey Smith, G., & Haworth, C. M. A. (2018) · BMJ · doi:10.1136/bmj.k3788 · Cited by 132 · see citing works
Davies, N. M., Hill, W. D., Anderson, E. L., Sanderson, E., Deary, I. J., & Davey Smith, G. (2019) · eLife · doi:10.7554/elife.43990 · Cited by 135 · see citing works
Cho, Y., Haycock, P., Sanderson, E. C. M., Gaunt, T. R., Zheng, J., Morris, A. P., Smith, G. D., & Hemani, G. (2020) · Nature Communications · doi:10.1038/s41467-020-14452-4 · Cited by 134 · see citing works
Gormley, M., Dudding, T., Sanderson, E. C. M., Martin, R. M., Thomas, S. J., Tyrrell, J., Ness, A., Brennan, P., Munafo, M. R., Pring, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19822-6 · Cited by 133 · see citing works
Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization
Ye, T., Shao, J., & Kang, H. (2021) · The Annals of Statistics · doi:10.1214/20-aos2027 · Cited by 131 · see citing works
Rosoff, D. B., Clarke, T., Adams, M. J., McIntosh, A. M., Smith, G. D., Jung, J., & Lohoff, F. W. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0535-9 · Cited by 126 · see citing works
The relationship between circulating lipids and breast cancer risk: A Mendelian randomization study
Johnson, K. E., Siewert, K. M., Klarin, D. M., Damrauer, S. M., Program, T. V. M. V., Chang, K., Tsao, P. S., Assimes, T. L., Maxwell, K. N., & Voight, B. F. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003302 · Cited by 128 · see citing works
Hu, X., Zhao, J., Lin, Z., Wang, Y., Peng, H., Zhao, H., Wan, X., & Yang, C. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106858119 · Cited by 128 · see citing works
Causal effects of blood lipids on amyotrophic lateral sclerosis: a Mendelian randomization study
Zeng, P., & Zhou, X. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddy384 · Cited by 130 · see citing works
Modifiable pathways for colorectal cancer: a mendelian randomisation analysis
Cornish, A. J., Law, P., Timofeeva, M. N., Palin, K., Farrington, S. M., Palles, C., Jenkins, M. A., Casey, G., Brenner, H., Chang-Claude, J. C., et al. (2019) · The Lancet. Gastroenterology & hepatology · doi:10.1016/s2468-1253(19)30294-8 · Cited by 126 · see citing works
Association of inflammatory markers with cerebral small vessel disease in community-based population
Jiang, L., Cai, X., Yao, D., Jing, J., Mei, L., Yang, Y., Li, S., Jin, A., Meng, X., Li, H., et al. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02468-0 · Cited by 130 · see citing works
Yarmolinsky, J., Bull, C. J., Vincent, E. E., Robinson, J., Walther, A., Smith, G. D., Lewis, S. J., Relton, C. L., & Martin, R. M. (2020) · JAMA · doi:10.1001/jama.2020.0150 · Cited by 124 · see citing works
Zheng, J., Zhang, Y., Rasheed, H., Walker, V. M., Sugawara, Y., Li, J., Leng, Y., Elsworth, B., Wootton, R. E., Fang, S., et al. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyab203 · Cited by 124 · see citing works
Cardiometabolic Traits, Sepsis, and Severe COVID-19
Ponsford, M., Gkatzionis, A., Walker, V. M., Grant, A. J., Wootton, R. E., Moore, L. S. P., Fatumo, S., Mason, A. M., Zuber, V., Willer, C. J., et al. (2020) · Circulation · doi:10.1161/circulationaha.120.050753 · Cited by 123 · see citing works
Yang, M., Wan, X., Zheng, H., Xu, K., Xie, J., Yu, H., Wang, J., & Xu, P. (2023) · Nutrients · doi:10.3390/nu15041057 · Cited by 123 · see citing works
- Authors
- Hartwig, F. P., Davey Smith, G., & Bowden, J.
- Affiliations
- Universidade Federal de Pelotas (BR)
- MRC Integrative Epidemiology Unit and
- University of Bristol (GB)
- Corresponding author
- Hartwig, Fernando Pires
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-07-12
- Volume
- 46
- Issue
- 6
- Pages
- 1985-1998
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC5837715
- OpenAlex
- W2613603864
- Cited by
- 3,485
- References
- 28
- Citations by year
- 2026: 387 · 2025: 798 · 2024: 1094 · 2023: 475 · 2022: 231 · 2021: 226 · 2020: 119 · 2019: 88 · 2018: 53 · 2017: 12 · 2014: 1
- FWCI
- 49.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion, cc-by)
- Funders
- University of Bristol (MC_UU_12013/9)
- Medical Research Council (MC_UU_12013, MR/N501906/1, MC_UU_12013/9)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- Mendelian randomization, summary data, invalid instruments, causal effect estimation, MR-Egger, weighted median, inverse variance weighting, plasma lipids, uric acid, coronary artery disease, sensitivity analysis
- MeSH terms
- Humans, Models, Statistical, Causality, Sample Size, Genome-Wide Association Study, Mendelian Randomization Analysis, Genetic Pleiotropy
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