Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology

Lawlor, Debbie A.; Harbord, Roger M.; Sterne, Jonathan A. C.; Timpson, Nic; Davey Smith, George

doi:10.1002/sim.3034

Cited by 4,618Genetic Associations and EpidemiologyAdvanced Causal Inference TechniquesStatistical Methods in Epidemiology

Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology

Lawlor, Debbie A., Harbord, Roger M., Sterne, Jonathan A. C., Timpson, Nic, Davey Smith, George·2008·Statistics in Medicine

DOI 10.1002/sim.3034

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Abstract

Observational epidemiological studies suffer from many potential biases, from confounding and from reverse causation, and this limits their ability to robustly identify causal associations. Several high‐profile situations exist in which randomized controlled trials of precisely the same intervention that has been examined in observational studies have produced markedly different findings. In other observational sciences, the use of instrumental variable (IV) approaches has been one approach to strengthening causal inferences in non‐experimental situations. The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies. The method has been referred to as ‘Mendelian randomization’, and can be considered as analogous to randomized controlled trials. This paper outlines Mendelian randomization, draws parallels with IV methods, provides examples of implementation of the approach and discusses limitations of the approach and some methods for dealing with these. Copyright © 2007 John Wiley & Sons, Ltd.

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Violation of the exclusion restriction criteria could bias causal estimates in IV analyses when used to test effects of intermediates in RCTs or when used in observational data (including MR).
“Similarly, violation of the ‘exclusion restriction criteria’33–37 could bias causal estimates in IV analyses when used to test effects of intermediates in RCTs or when used in observational data (including MR)”
From Triangulation in aetiological epidemiology (2017) · cites this paper as [33]
MR studies may be confounded by population stratification.
“but may be confounded by population stratification.33,34”
From Triangulation in aetiological epidemiology (2017) · cites this paper as [33]
Vertical pleiotropy can shed light on differences in treatments (e.g. PCSK9 and HMGCR) on intermediaries (e.g. blood metabolic markers), which can then be used to explore whether such differences are clinically relevant.
“Can shed light on differences in treatments (e.g. PCSK9 and HMGCR) on intermediaries (e.g. blood metabolic markers 106 ), which can then be used to explore whether such differences are clinically”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [106]
Mendelian randomization generally assesses cumulative effects of a risk factor over a long period of the life course without requiring repeat risk factor assessment and with little chance of regression dilution bias or reverse causation.
“Whereas one of the noted advantages of MR is that it generally assesses the cumulative effects of a risk factor over a long period of the life course (potentially from conception) without requiring repeat risk factor assessment and with little chance of regression dilution bias or reverse causation (confounding by prevalent disease), 7”
From Commentary: Two-sample Mendelian randomization: opportunities and challenges (2016) · cites this paper as [7]
Concerns about independence of genetic variants from confounders are greater for traits that are more socially-patterned, as this increases susceptibility to associations arising from population structure, assortative mating, and dynastic effects.
“Concerns about independence are greater for traits that are more socially-patterned, as this increases susceptibility to associations arising from population structure, assortative mating, and dynastic effects 48 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [48]

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Casas, J. P., Shah, T., Cooper, J. A., Hawe, E., McMahon, A. D., Gaffney, D. F., Packard, C. J., O’Reilly, D. S. J., Juhan-Vague, I., Yudkin, J. S., et al. (2006) · International Journal of Epidemiology · doi:10.1093/ije/dyl041 · Cited by 172 · see citing works
Robust, Accurate Confidence Intervals with a Weak Instrument: Quarter of Birth and Education
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Genetic Predisposition for Adult Lactose Intolerance and Relation to Diet, Bone Density, and Bone Fractures
Obermayer-Pietsch, B. M., Bonelli, C. M., Walter, D., Kühn, R., Fahrleitner‐Pammer, A., Berghold, A., Goessler, W., Stepan, V. M., Dobnig, H., Leb, G., et al. (2004) · Journal of Bone and Mineral Research · doi:10.1359/jbmr.0301207 · Cited by 166 · see citing works
To be or not to be active: the stochastic nature of enhancer action
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A Simple and Improved Correction for Population Stratification in Case-Control Studies
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Fibrinogen and coronary heart disease: test of causality by ‘Mendelian randomization’
Keavney, B. D., Danesh, J., Parish, S., Palmer, A., Clark, S., Youngman, L. D., Délepine, M., Lathrop, M. G., Peto, R., & Collins, R. (2006) · International Journal of Epidemiology · doi:10.1093/ije/dyl114 · Cited by 142 · see citing works
Meta-analyses of Observational and Genetic Association Studies of Folate Intakes or Levels and Breast Cancer Risk
Lewis, S. J., Harbord, R. M., Harris, R., & Smith, G. D. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj440 · Cited by 141 · see citing works
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A novel common single nucleotide polymorphism in the promoter region of the C-reactive protein gene associated with the plasma concentration of C-reactive protein
Kovacs, A., Green, F., Hansson, L., et al. (2004) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2004.08.018 · Cited by 140 · see citing works
Re: Estimation of Bias in Nongenetic Observational Studies Using “Mendelian Triangulation” by Bautista et al.
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
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Canalization: A molecular genetic perspective
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Relation of polymorphism within the C-reactive protein gene and plasma CRP levels
Suk, H. J., Ridker, P. M., Cook, N. R., & Zee, R. Y. L. (2004) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2004.07.033 · Cited by 135 · see citing works
Family-Based Association Studies
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Estimation of treatment effects in randomised trials with non-compliance and a dichotomous outcome using structural mean models
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Bias in relative odds estimation owing to imprecise measurement of correlated exposures
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A meta‐analysis of the MTHFR C677T polymorphism and schizophrenia risk
Lewis, S. J., Zammit, S., Gunnell, D., & Smith, G. D. (2005) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.30170 · Cited by 100 · see citing works
Meta-analysis of vitamin D receptor polymorphisms and pulmonary tuberculosis risk.
Lewis, S. J., Baker, I., & Smith, G. D. (2005) · PubMed · Cited by 99 · see citing works
Tests and Confidence Sets with Correct Size when Instruments are Potentially Weak
Mikusheva, A., & Poi, B. P. (2006) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0600600303 · Cited by 99 · see citing works
Econometrics: A Modern Introduction
Murray, M. P. (2005) · Cited by 97 · see citing works
A Statistical Approach to Genetic Epidemiology: Concepts and Applications
Ziegler, A., & König, I. R. (2006) · Cited by 96 · see citing works
Does Elevated Plasma Fibrinogen Increase the Risk of Coronary Heart Disease?
Smith, G. D., Harbord, R. M., Milton, J., Ebrahim, S. B. J., & Sterne, J. A. C. (2005) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/01.atv.0000183937.65887.9c · Cited by 94 · see citing works
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C-reactive protein and cardiovascular disease: Weighing the evidence
Lowe, G. D. O., & Pepys, M. B. (2006) · Current Atherosclerosis Reports · doi:10.1007/s11883-006-0040-x · Cited by 87 · see citing works
Meta-analysis of genetic studies using Mendelian randomization—a multivariate approach
Thompson, J. R., Minelli, C., Abrams, K. R., Tobin, M. D., & Riley, R. D. (2005) · Statistics in Medicine · doi:10.1002/sim.2100 · Cited by 85 · see citing works
An Integrated Approach to the Meta-Analysis of Genetic Association Studies using Mendelian Randomization
Minelli, C. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh228 · Cited by 78 · see citing works
Genetic Variation and Willingness to Participate in Epidemiologic Research: Data from Three Studies
Bhatti, P., Sigurdson, A. J., WANG, S. S., Chen, J., Rothman, N., Hartge, P. A., Bergen, A. W., & Landi, M. T. (2005) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-05-0463 · Cited by 76 · see citing works
Avoiding milk is associated with a reduced risk of insulin resistance and the metabolic syndrome: findings from the British Women's Heart and Health Study
Lawlor, D. A., Ebrahim, S. B. J., Timpson, N. J., & Smith, G. D. (2005) · Diabetic Medicine · doi:10.1111/j.1464-5491.2005.01537.x · Cited by 74 · see citing works
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An Interleukin-18 Polymorphism Is Associated With Reduced Serum Concentrations and Better Physical Functioning in Older People
Frayling, T. M., Rafiq, S., Murray, A., Hurst, A. J., Weedon, M. N., Henley, W., Bandinelli, S., Corsi, A., Ferrucci, L., Guralnik, J. M., et al. (2007) · The Journals of Gerontology Series A · doi:10.1093/gerona/62.1.73 · Cited by 72 · see citing works
Associations of fibrinogen and C-reactive protein with prevalent and incident coronary heart disease are attenuated by adjustment for confounding factors
Smith, G. D., Rumley, A., Lowe, G. D. O., Ebrahim, S. B. J., & Lawlor, D. A. (2005) · Thrombosis and Haemostasis · doi:10.1160/th04-12-0805 · Cited by 72 · see citing works
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Econometric approaches to epidemiologic data: Relating endogeneity and unobserved heterogeneity to confounding
Zohoori, N., & Savitz, D. A. (1997) · Annals of Epidemiology · doi:10.1016/s1047-2797(97)00023-9 · Cited by 72 · see citing works
Systemic lupus erythematosus in a multiethnic US Cohort (LUMINA). XXX: association between C-reactive protein (CRP) gene polymorphisms and vascular events
Szalai, A. J., Alarcón, G. S., Alén, J. C., Toloza, S., McCrory, M. A., Edberg, J. C., McGwin, G., Bastian, H. M., Fessler, B. J., Vilá, L. M., et al. (2005) · Lara D. Veeken · doi:10.1093/rheumatology/keh613 · Cited by 71 · see citing works
Estimation of Bias in Nongenetic Observational Studies Using “Mendelian Triangulation”
Bautista, L. E., Smeeth, L. L., Hingorani, A. D., & Casas, J. P. (2006) · Annals of Epidemiology · doi:10.1016/j.annepidem.2006.02.001 · Cited by 67 · see citing works
Lipid-related genes and myocardial infarction in 4685 cases and 3460 controls: discrepancies between genotype, blood lipid concentrations, and coronary disease risk
Keavney, B. D. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh275 · Cited by 62 · see citing works
Plasma fibrinogen and fibrinogen genotypes in 4685 cases of myocardial infarction and in 6002 controls: Test of causality by "Mendelian randomisation"
Youngman, L. D., Keavney, B. D., Palmer, A. J., Parish, S., Clark, S. L., Danesh, J., Délepine, M., Lathrop, M. G., Peto, R., & Collins, R. (2000) · Circulation · Cited by 59 · see citing works
Self-Reported Alcohol-Associated Symptoms and Drinking Behavior in Three ALDH2 Genotypes Among Japanese University Students
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Protein Z Gene Polymorphisms, Protein Z Concentrations, and Ischemic Stroke
Staton, J. M., Sayer, M. S., Hankey, G. J., et al. (2005) · Stroke · doi:10.1161/01.str.0000166058.49577.ca · Cited by 53 · see citing works
Fibrinogen, C-reactive protein and coronary heart disease: does Mendelian randomization suggest the associations are non-causal?
Smith, G. D., Harbord, R. M., & Ebrahim, S. B. J. (2004) · QJM · doi:10.1093/qjmed/hch025 · Cited by 53 · see citing works
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Recent trends in cancer mortality in the UK
Doll, R., & Boreham, J. (2005) · British Journal of Cancer · doi:10.1038/sj.bjc.6602450 · Cited by 49 · see citing works
C-Reactive Protein and Cardiovascular Disease Risk: Still an Unknown Quantity?
Smith, G. D., Timpson, N. J., & Lawlor, D. A. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-145-1-200607040-00130 · Cited by 46 · see citing works
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Mendelian Randomization Suggests No Causal Association Between C-reactive Protein and Carotid Intima-media Thickness in the Young Finns Study
Mika Kivimaki, B., Lawlor, D. A., Eklund, C., Smith, G. D., Hurme, M., Lehtimäki, T. J., Viikari, J. S. A., & Raitakari, O. T. (2007) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/01.atv.0000258869.48076.14 · Cited by 40 · see citing works
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Randomised by (your) god: robust inference from an observational study design
Smith, G. D. (2006) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2004.031880 · Cited by 39 · see citing works
Genetic Polymorphism in Cytochrome P450 7A1 and Risk of Colorectal Cancer: The Fukuoka Colorectal Cancer Study
Hagiwara, T., Kono, S., Yin, G., Toyomura, K., Nagano, J., Mizoue, T., Mibu, R., Tanaka, M., Kakeji, Y., Maehara, Y., et al. (2005) · Cancer Research · doi:10.1158/0008-5472.can-04-3872 · Cited by 37 · see citing works
On the identification of path analysis models with one hidden variable
Stanghellini, E., & Wermuth, N. (2005) · Biometrika · doi:10.1093/biomet/92.2.337 · Cited by 28 · see citing works
Lactose intolerance, calcium intake, and osteopenia
Mainguet, P., Faille, I., Destrebecq, L., et al. (1991) · The Lancet · doi:10.1016/0140-6736(91)92024-v · Cited by 23 · see citing works
Adjusting for population structure in admixed populations
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Commentary: The HRT story: vindication of old epidemiological theory
Vandenbroucke, J. P. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh121 · Cited by 22 · see citing works
Mendelian randomisation: Why epidemiology needs a formal language for causality
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Identification of Causal Effects Using Instrumental Variables: Comment
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Fibrinogen, social position, and "Mendelian randomisation".
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Weak Instruments: An Overview and New TechniquesPreprint
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Cardiovascular risk and hormone replacement therapy
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Letter by Timpson et al Regarding Article, “Contribution of Clinical Correlates and 13 C-Reactive Protein Gene Polymorphisms to Interindividual Variability in Serum C-Reactive Protein Level”
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Fibrinogen, social position, and "Mendelian randomisation"
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The shape of life: how much is written in stone?
(2000) · doi:10.1002/1521-1878(200012)22:12
Untitled work
(2000) · doi:10.1002/1098-2272(200101)20:1

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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · see citing works
Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · 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
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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
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Reading 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
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Interpreting findings from Mendelian randomization using the MR-Egger method.
Burgess, S., & Thompson, S. G. (2017) · European Journal of Epidemiology · doi:10.1007/s10654-017-0255-x · Cited by 4,571 · see citing works
Avoiding bias from weak instruments in Mendelian randomization studies
Burgess, S., Thompson, S. G., & CRP CHD Genetics Collaboration (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr036 · Cited by 3,952 · see citing works
Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel
Ference, B. A., Ginsberg, H. N., Graham, I., Ray, K. K., Packard, C. J., Bruckert, E., Hegele, R. A., Krauss, R. M., Raal, F. J., Schunkert, H., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx144 · Cited by 3,962 · see citing works
Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement
Skrivankova, V. W., Richmond, R. C., Woolf, B. A. R., Yarmolinsky, J., Davies, N. M., Swanson, S. A., VanderWeele, T. J., Higgins, J. P. T., Timpson, N. J., Dimou, N., et al. (2021) · JAMA · doi:10.1001/jama.2021.18236 · Cited by 3,994 · see citing works
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Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3538 · Cited by 3,352 · see citing works
Cohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort
Fraser, A., Macdonald-Wallis, C., Tilling, K., Boyd, A., Golding, J., Davey Smith, G., Henderson, J., Macleod, J., Molloy, L., Ness, A., et al. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dys066 · Cited by 2,494 · see citing works
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Mendelian Randomization as an Approach to Assess Causality Using Observational Data
Sekula, P., Del Greco M, F., Pattaro, C., & Köttgen, A. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2016010098 · Cited by 2,351 · see citing works
Mendelian randomization
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
Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants
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
Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors
EPIC- 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
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Calculating statistical power in Mendelian randomization studies.
Brion, M.-J. A., Shakhbazov, K., & Visscher, P. M. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt179 · Cited by 1,751 · 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
An atlas of genetic influences on human blood metabolites
The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Shin, S.-Y., Fauman, E. B., Petersen, A.-K., Krumsiek, J., Santos, R., Huang, J., Arnold, M., Erte, I., Forgetta, V., et al. (2014) · Nature Genetics · doi:10.1038/ng.2982 · Cited by 1,515 · see citing works
Bias due to participant overlap in two‐sample Mendelian randomization
Burgess, S., Davies, N. M., & Thompson, S. G. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21998 · Cited by 1,624 · see citing works
Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.
Burgess, S., & Thompson, S. G. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu283 · Cited by 1,605 · see citing works
Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators.
Pierce, B. L., & Burgess, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt084 · Cited by 1,389 · see citing works
Using multiple genetic variants as instrumental variables for modifiable risk factors
Palmer, T. M., Lawlor, D. A., Harbord, R. M., Sheehan, N. A., Tobias, J. H., Timpson, N. J., Smith, G. D., & Sterne, J. A. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210394459 · Cited by 1,289 · see citing works
Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods
Burgess, S., Dudbridge, F., & Thompson, S. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6835 · Cited by 1,153 · see citing works
Guidelines 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
Mendelian randomisation for mediation analysis: current methods and challenges for implementation
Carter, A. R., Sanderson, E., Hammerton, G., Richmond, R. C., Davey Smith, G., Heron, J., Taylor, A. E., Davies, N. M., & Howe, L. D. (2021) · European Journal of Epidemiology · doi:10.1007/s10654-021-00757-1 · Cited by 1,215 · see citing works
Remnant Cholesterol as a Causal Risk Factor for Ischemic Heart Disease
Varbo, A., Benn, M., Tybjærg-Hansen, A., Jørgensen, A. B., Frikke-Schmidt, R., & Nordestgaard, B. G. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.08.1026 · Cited by 1,115 · see citing works
Triglyceride-Rich Lipoproteins and Atherosclerotic Cardiovascular Disease: New Insights From Epidemiology, Genetics, and Biology
Nordestgaard, B. G. (2016) · Circulation Research · doi:10.1161/circresaha.115.306249 · Cited by 1,116 · see citing works
Triangulation in aetiological epidemiology
Lawlor, D. A., Tilling, K., & Davey Smith, G. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyw314 · Cited by 1,129 · see citing works
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An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings
Sanderson, E., Davey Smith, G., Windmeijer, F., & Bowden, J. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyy262 · Cited by 1,085 · see citing works
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Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts
Vimaleswaran, K. S., Berry, D. J., Lu, C., Tikkanen, E., Pilz, S., Hiraki, L. T., Cooper, J. D., Dastani, Z., Li, R., Houston, D. K., et al. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001383 · Cited by 1,046 · see citing works
Causal associations between risk factors and common diseases inferred from GWAS summary data
Zhu, Z., Zheng, Z., Zhang, F., Wu, Y., Trzaskowski, M., Maier, R., Robinson, M. R., McGrath, J. J., Visscher, P. M., Wray, N. R., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02317-2 · Cited by 951 · see citing works
Effect of Long-Term Exposure to Lower Low-Density Lipoprotein Cholesterol Beginning Early in Life on the Risk of Coronary Heart Disease
Ference, B. A., Yoo, W., Alesh, I., Mahajan, N., Mirowska, K. K., Mewada, A., Kahn, J., Afonso, L., Williams, K. A., & Flack, J. M. (2012) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.09.017 · Cited by 898 · see citing works
Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique.
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
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Mendelian 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
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Commentary: Two-sample Mendelian randomization: opportunities and challenges
Lawlor, D. A. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw127 · Cited by 810 · see citing works
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Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.
Zheng, J., Haberland, V., Baird, D., Walker, V., Haycock, P., Hurle, M., Gutteridge, A., Erola, P., Liu, Y., Luo, S., et al. (2019) · doi:10.1101/627398 · Cited by 812 · see citing works
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Mendelian randomization of blood lipids for coronary heart disease
Holmes, M. V., Asselbergs, F. W., Palmer, T. M., Drenos, F., Lanktree, M. B., Nelson, C. P., Dale, C. E., Padmanabhan, S., Finan, C., Swerdlow, D. I., et al. (2015) · European Heart Journal · doi:10.1093/eurheartj/eht571 · Cited by 748 · see citing works
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Variation inPCSK9andHMGCRand Risk of Cardiovascular Disease and Diabetes
Ference, B. A., Robinson, J. G., Brook, R. D., Catapano, A. L., Chapman, M. J., Neff, D. R., Voros, S., Giugliano, R. P., Davey Smith, G., Fazio, S., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1604304 · Cited by 743 · see citing works
Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms
Jones, S. E., Lane, J. M., Wood, A. R., van Hees, V. T., Tyrrell, J., Beaumont, R. N., Jeffries, A. R., Dashti, H. S., Hillsdon, M., Ruth, K. S., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08259-7 · Cited by 691 · see citing works
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Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits
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Authors: Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N., & Davey Smith, G.
Affiliations: University of Bristol (GB)
Corresponding author: Lawlor, Debbie A.
ORCID: Lawlor, Debbie A.
Harbord, Roger M.
Sterne, Jonathan A. C.
Venue: Statistics in Medicine
Publisher: Wiley
Published: 2008-02-26
Volume: 27
Issue: 8
Pages: 1133-1163
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.3034
OpenAlex: W1972696558
Cited by: 4,618
References: 138
Citations by year: 2026: 301 · 2025: 616 · 2024: 1082 · 2023: 646 · 2022: 394 · 2021: 298 · 2020: 236 · 2019: 170 · 2018: 161 · 2017: 116 · 2016: 128 · 2015: 100 · 2014: 88 · 2013: 73 · 2012: 74
FWCI: 17.31
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Statistics in Medicine (publishedVersion)
PubMed (publishedVersion)
Explore Bristol Research (submittedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: Mendelian randomization, instrumental variables, causal inference, observational bias, confounding, reverse causality, germline genetic variants, environmental exposure, randomized controlled trials
MeSH terms: Epidemiologic Methods, Genotype, Humans, Genome, Human, Bias, Causality, Randomized Controlled Trials as Topic, Models, Econometric, Molecular Epidemiology, Genetic Diseases, Inborn

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Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology

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