The DOI matches its Crossref record; 179 of the 179 references it cites were found in a registry or an index.
‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*
Abstract
Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization—the random assortment of genes from parents to offspring that occurs during gamete formation and conception—provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization—the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures. Genetic epidemiology—the theme of this issue of the International Journal of Epidemiology—is seen by many to be the only future for epidemiology, perhaps reflecting a growing awareness of the limitations of observational epidemiology1 (Box 1). Genetic epidemiology is concerned with understanding heritable aspects of disease risk, individual susceptibility to disease, and ultimately with contributing to a comprehensive molecular understanding of pathogenesis. The massive investment and expansion of human genetics, if it is to return value for the common good, must be integrated into public health functions. The human genome epidemiology network (HuGE Net—http://www.cdc.gov/genetics/huge.htm) has been established to promote the use of genetic knowledge—in terms of genetic tests and services—for disease prevention and health promotion.2,3 A broad taxonomy of human genome studies of public health relevance has been developed4 (Box 2). In this issue of the IJE, we publish a paper by Miguel Porta,5 who highlights the need for a more rational approach to genetic testing, given the likely low penetrance of many genes associated with cancers,6 likening the role of the genome to a jazz score that is interpreted and developed through experience and context—and is seldom predictable. Such insights may well temper enthusiasm for genetic testing in populations. However, in parallel to the approaches advocated by HuGE, genetic epidemiology can lead to a more robust understanding of environmental determinants of disease (e.g. dietary factors, occupational exposures, and health-related behaviours) relevant to whole populations (and not simply to genetically susceptible sub-populations).7–10 This approach has recently been referred to as ‘Mendelian randomization’.11–15 Here we begin by briefly reviewing reasons for current concerns about aetiological findings generated by conventional observational epidemiology and then we outline the potential contribution (and limitations) of Mendelian randomization. ‘Epidemiology set to get fast-track treatment’ ‘A consortium of leading European research centres and pharmaceutical companies will this week announce a plan to transform epidemiology by combining it with the new techniques of high-throughput biology. They plan to create a new field of study—genomic epidemiology—by using screening technologies derived from the human genome project … We think it is important to expand classical epidemiology and genetic epidemiology to take it to this high-throughput mode, says Esper Boel, vice-president of biotechnology research at Novo Nordisk. We want to use post-genomic technologies to create a new clinical science, to turn functional genomics into real clinical chemistry.’ From: Butler D. Epidemiology set to get fast-track treatment. Nature 2001;414:139. Reprinted with permission. Surveillance Population frequency of gene variants predisposing to specific diseases Population frequency of morbidity and mortality from such diseases Population frequency and effects of environmental factors known to interact with gene variants Economic costs of genetic components of diseases Coverage, access, and uptake of genetic tests and services Aetiology Magnitude of disease risk associated with gene variants in different populations Contribution of gene variants to the overall level of disease in different populations Magnitude of disease risk associated with gene–gene and gene–environment interactions in different populations Heal
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The essence of an MR design is that the association between a genetic variant (G) and an outcome (Y) can be used to test whether and by how much the exposure of interest (X) influences the outcome, provided that the genetic variant is associated with the exposure of interest and has no other source of association with the outcome.1,9”
From Mendelian randomization (2022) · cites this paper as [1]
In the citing paper
“When initially presented, it was concluded that “[MR] offers a more robust approach to understanding the effect of some modifiable exposures on health outcomes than does much conventional observational epidemiology”1 and that where possible, RCTs should follow to establish the effects of interventions. This conclusion remains unchanged, although moving towards formal triangulation of all pertinent”
From Mendelian randomization (2022) · cites this paper as [1]
In the citing paper
“Canalization can occur when polymorphic phenotypes expressed during fetal development lead to the development of compensating pathways to mitigate the effects of that expression. 1,178,179”
From Mendelian randomization (2022) · cites this paper as [1]
In the citing paper
“Estimating effect sizes through the instrumental variable approach imposes a fourth assumption, usually homogeneity of effects of the exposure on the outcome.16 58 59 60”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [16]
In the citing paper
“Most MR studies rely on three core IV assumptions (relevance, independence, and exclusion restriction; fig 1, box 3) to carry out testing for causal effects of the exposure on the outcome.16”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [16]
In the citing paper
“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 [34]
In the citing paper
“but may be confounded by population stratification.33,34”
From Triangulation in aetiological epidemiology (2017) · cites this paper as [34]
In the citing paper
“Human genetics can provide compelling evidence of causation through approaches such as Mendelian randomization (MR).”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [10]
In the citing paper
“Performed well, such studies called Mendelian randomization (MR) analyses, can provide rigorous evidence of causation 9,10 .”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [10]
In the citing paper
“We can exploit the properties of the human genome (specifically, the random segregation of alleles and their non-modifiable nature) to make causal inferences about the relationships between risk factors and disease 10 .”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [10]
In the citing paper
“Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome.”
From Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. (2015) · cites this paper as [1]
In the citing paper
“If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation.”
From Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. (2015) · cites this paper as [1]
Citations
Cites
OLSSON, A. G., McMurray, J. J. V., Thorgeirsson, G., Spaulding, C. M., Slattery, J., Pyörälà, K., Færgeman, O., Wright, R. S., Berg, K. M., Wilhelmsen, L., et al. (1994) · The Lancet · Cited by 8,467
Prevention of Coronary Heart Disease with Pravastatin in Men with Hypercholesterolemia
Shepherd, J., Cobbe, S. M., Ford, I., Isles, C. G., Lorimer, A. R., Macfarlane, P. W., McKillop, J. H., & Packard, C. J. (1995) · New England Journal of Medicine · doi:10.1056/nejm199511163332001 · Cited by 7,449
Collaboration, A. T. (2002) · BMJ · doi:10.1136/bmj.324.7329.71 · Cited by 6,943
(1994) · New England Journal of Medicine · doi:10.1056/nejm199404143301501 · Cited by 4,737
Spielman, R. S., McGinnis, R. E., & Ewens, W. J. (1993) · PubMed · Cited by 3,679
Effects of a combination of beta carotene and vitamin A on lung cancer and cardiovascular disease
Omenn, G. S., Goodman, G. E., Thornquist, M. D., Balmes, J., Cullen, M. R., Glass, A., Keogh, J. P., Meyskens, F. L., Valanis, B., Williams, J. H., et al. (1996) · New England Journal of Medicine · doi:10.1056/nejm199605023341802 · Cited by 3,638
SCANDINAVIANSIMVASTATINSURVIVAL (1994) · The Lancet · doi:10.1016/s0140-6736(94)90566-5 · Cited by 3,638
Prevention of neural tube defects: Results of the Medical Research Council Vitamin Study
(1991) · The Lancet · doi:10.1016/0140-6736(91)90133-a · Cited by 3,472
CANALIZATION OF DEVELOPMENT AND THE INHERITANCE OF ACQUIRED CHARACTERS
WADDINGTON, C. H. (1942) · Nature · doi:10.1038/150563a0 · Cited by 3,255
Research works printed but not linked: 3
- Skrabanek P. False Premises, False Promises. Whithorn: Tarragon Press, 2000.
- http://www.ncbi.nlm.nih.gov/Omim (accessed 12 Dec 2002).
- Heart Protection Study Collaborative Group. MRC/BHF heart protection Study of cholesterol lowering with simvastatin in 20 536 high-risk individuals. Lancet2002;360:7–22. · DOI · Paper page
Cited by
Sorted by citations
Items 101–200 of 1,037
Haycock, 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 595
Morrison, J., Knoblauch, N., Marcus, J. H., Stephens, M., & He, X. (2020) · Nature Genetics · doi:10.1038/s41588-020-0631-4 · Cited by 587
Warrington, N. M., Beaumont, R. N., Horikoshi, M., Day, F. R., Helgeland, Ø., Laurin, C., Bačelis, J., Peng, S., Hao, K., Feenstra, B., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0403-1 · Cited by 590
Cordell, H. J., & Clayton, D. G. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67424-7 · Cited by 587
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
Nordestgaard, B. G., & Langsted, A. (2016) · Journal of Lipid Research · doi:10.1194/jlr.r071233 · Cited by 575
An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus
Patel, C. J., Bhattacharya, J., & Butte, A. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010746 · Cited by 601
Burgess, S., & Labrecque, J. A. (2018) · European Journal of Epidemiology · doi:10.1007/s10654-018-0424-6 · Cited by 569
Smith, G. D., Lawlor, D. A., Harbord, R., Timpson, N., Day, I., & Ebrahim, S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040352 · Cited by 569
Self-citationRelton, C. L., & Davey Smith, G. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dyr233 · Cited by 567
Methodological Challenges in Mendelian Randomization
VanderWeele, T. J., Tchetgen Tchetgen, E. J., Cornelis, M., & Kraft, P. (2014) · Epidemiology · doi:10.1097/ede.0000000000000081 · Cited by 555
Elevated C-reactive protein in the diagnosis, prognosis, and cause of cancer
Allin, K. H., & Nordestgaard, B. G. (2011) · Critical Reviews in Clinical Laboratory Sciences · doi:10.3109/10408363.2011.599831 · Cited by 563
What can mendelian randomisation tell us about modifiable behavioural and environmental exposures?
Smith, G. D., & Ebrahim, S. (2005) · BMJ · doi:10.1136/bmj.330.7499.1076 · Cited by 545
Self-citationBurgess, S., Daniel, R. M., Butterworth, A. S., Thompson, S. G., & the EPIC-InterAct Consortium (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyu176 · Cited by 543
When are observational studies as credible as randomised trials?
Vandenbroucke, J. P. (2004) · The Lancet · doi:10.1016/s0140-6736(04)16261-2 · Cited by 538
Proximal, Distal, and the Politics of Causation: What’s Level Got to Do With It?
Krieger, N. (2008) · American Journal of Public Health · doi:10.2105/ajph.2007.111278 · Cited by 535
Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality
Evans, D. M., & Smith, G. D. (2015) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090314-050016 · Cited by 529
Homocysteine lowering interventions for preventing cardiovascular events
Martí-Carvajal, A. J., Solà, I., Lathyris, D., & Salanti, G. (2009) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd006612.pub2 · Cited by 520
Homocysteine, B Vitamins, and Cognitive Impairment
Smith, A. D., & Refsum, H. (2016) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-071715-050947 · Cited by 526
The food metabolome: a window over dietary exposure
Scalbert, A., Brennan, L., Manach, C., Andres-Lacueva, C., Dragsted, L. O., Draper, J., Rappaport, S. M., van der Hooft, J. J., & Wishart, D. S. (2014) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.113.076133 · Cited by 519
Wilson, D. P., Jacobson, T. A., Jones, P. H., Koschinsky, M. L., McNeal, C. J., Nordestgaard, B. G., & Orringer, C. E. (2019) · Journal of clinical lipidology · doi:10.1016/j.jacl.2019.04.010 · Cited by 522
Menopause accelerates biological aging
Levine, M. E., Lu, A. T.-H., Chen, B. H., Hernandez, D. G., Singleton, A., Ferrucci, L., Bandinelli, S., Salfati, E., Manson, J. E., Quach, A., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604558113 · Cited by 510
Multiple-bias modelling for analysis of observational data
Greenland, S. (2005) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2004.00349.x · Cited by 497
Genetics of Human Cardiovascular Disease
Kathiresan, S., & Srivastava, D. (2012) · Cell · doi:10.1016/j.cell.2012.03.001 · Cited by 513
A genome-wide association study identifies protein quantitative trait loci (pQTLs)
Melzer, D., Perry, J. R. B., Hernandez, D., Corsi, A.-M., Stevens, K., Rafferty, I., Lauretani, F., Murray, A., Gibbs, J. R., Paolisso, G., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000072 · Cited by 480
Kunutsor, S. K., Whitehouse, M. R., Blom, A. W., Beswick, A. D., & INFORM Team (2016) · PLoS ONE · doi:10.1371/journal.pone.0150866 · Cited by 480
Genetic architecture: the shape of the genetic contribution to human traits and disease
Timpson, N. J., Greenwood, C. M. T., Soranzo, N., Lawson, D. J., & Richards, J. B. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.101 · Cited by 434
Frikke-Schmidt, R. (2008) · JAMA · doi:10.1001/jama.299.21.2524 · Cited by 464
Use of allele scores as instrumental variables for Mendelian randomization
Burgess, S., & Thompson, S. G. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt093 · Cited by 452
Association of Habitual Alcohol Intake With Risk of Cardiovascular Disease
Biddinger, K. J., Emdin, C. A., Haas, M. E., Wang, M., Hindy, G., Ellinor, P. T., Kathiresan, S., Khera, A. V., & Aragam, K. G. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.3849 · Cited by 468
Vitamin D and Risk of Multiple Sclerosis: A Mendelian Randomization Study
Mokry, L. E., Ross, S., Ahmad, O. S., Forgetta, V., Smith, G. D., Leong, A., Greenwood, C. M. T., Thanassoulis, G., & Richards, J. B. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001866 · Cited by 445
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
Homocysteine and stroke: evidence on a causal link from mendelian randomisation
Casas, J. P., Bautista, L. E., Smeeth, L. L., Sharma, P. K., & Hingorani, A. D. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17742-3 · Cited by 442
Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses
Wallace, C. (2020) · PLOS Genetics · doi:10.1371/journal.pgen.1008720 · Cited by 441
Lawlor, D. A., Smith, G. D., Bruckdorfer, K. R., Kundu, D., & Ebrahim, S. (2004) · The Lancet · doi:10.1016/s0140-6736(04)16260-0 · Cited by 438
Self-citationLarsson, S. C., Bäck, M., Rees, J. M. B., Mason, A. M., & Burgess, S. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehz388 · Cited by 436
Data dredging, bias, or confounding: They can all get you into the BMJ and the Friday papers
Smith, G. D., & Ebrahim, S. (2002) · BMJ · doi:10.1136/bmj.325.7378.1437 · Cited by 434
Self-citationYao, C., Chen, G., Song, C., Keefe, J., Mendelson, M., Huan, T., Sun, B. B., Laser, A., Maranville, J. C., Wu, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05512-x · Cited by 431
Heikkilä, K., Ebrahim, S. B. J., & Lawlor, D. A. (2007) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2006.051292 · Cited by 424
Self-citationBias in causal estimates from Mendelian randomization studies with weak instruments
Burgess, S., & Thompson, S. G. (2011) · Statistics in Medicine · doi:10.1002/sim.4197 · Cited by 421
Lotta, L. A., Scott, R. A., Sharp, S. J., Burgess, S., Luan, J., Tillin, T., Schmidt, A. F., Imamura, F., Stewart, I. D., Perry, J. R. B., et al. (2016) · PLOS Medicine · doi:10.1371/journal.pmed.1002179 · Cited by 417
den Heijer, M., Lewington, S., & Clarke, R. (2005) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2005.01141.x · Cited by 417
Adult height, nutrition, and population health
Perkins, J. M., Subramanian, S. V., Davey Smith, G., & Özaltin, E. (2016) · Nutrition Reviews · doi:10.1093/nutrit/nuv105 · Cited by 405
Ebrahim, S., & Davey Smith, G. (2008) · Human Genetics · doi:10.1007/s00439-007-0448-6 · Cited by 410
Self-citationProbabilistic fine-mapping of transcriptome-wide association studies
Mancuso, N., Freund, M. K., Johnson, R., Shi, H., Kichaev, G., Gusev, A., & Paşaniuc, B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0367-1 · Cited by 391
Association Between Depressive Symptoms and Incident Cardiovascular Diseases
Harshfield, E. L., Pennells, L. A. M., Schwartz, J. E., Willeit, P., Kaptoge, S. K., Bell, S., Shaffer, J. A., Bolton, T. R., Spackman, S., Wassertheil‐Smoller, S., et al. (2020) · JAMA · doi:10.1001/jama.2020.23068 · Cited by 416
Rees, J. M. B., Wood, A., & Burgess, S. (2017) · Statistics in Medicine · doi:10.1002/sim.7492 · Cited by 396
Gut Microbiota in Hypertension and Atherosclerosis: A Review
Verhaar, B. J. H., Prodan, A., Nieuwdorp, M., & Muller, M. (2020) · Nutrients · doi:10.3390/nu12102982 · Cited by 425
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 404
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
Proceeding From Observed Correlation to Causal Inference: The Use of Natural Experiments
Rutter, M. L. (2007) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2007.00050.x · Cited by 409
Newton‐Cheh, C., Larson, M. G., Vasan, R. S., Levy, D. A., Bloch, K. D., Surti, A., Guiducci, C., Kathiresan, S., Benjamin, E. J., Struck, J., et al. (2009) · Nature Genetics · doi:10.1038/ng.328 · Cited by 397
Lotta, L. A., Sharp, S. J., Burgess, S., Perry, J. R. B., Stewart, I. D., Willems, S. M., Luan, J., Ardanaz, E., Arriola, L., Balkau, B., et al. (2016) · JAMA · doi:10.1001/jama.2016.14568 · Cited by 394
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
Genome-wide association studies of cancer: current insights and future perspectives
Sud, A., Kinnersley, B., & Houlston, R. S. (2017) · Nature reviews. Cancer · doi:10.1038/nrc.2017.82 · Cited by 391
Selecting instruments for Mendelian randomization in the wake of genome-wide association studies.
Swerdlow, D. I., Kuchenbaecker, K. B., Shah, S., Sofat, R., Holmes, M. V., White, J., Mindell, J. S., Kivimaki, M., Brunner, E. J., Whittaker, J. C., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw088 · Cited by 387
Emdin, C. A., Khera, A. V., Natarajan, P., Klarin, D., Zekavat, S. M., Hsiao, A. J., & Kathiresan, S. (2017) · JAMA · doi:10.1001/jama.2016.21042 · Cited by 378
Batty, G. D., Russ, T. C., Stamatakis, E., & Kivimäki, M. (2017) · BMJ · doi:10.1136/bmj.j108 · Cited by 377
Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects
Howe, L. J., Nivard, M. G., Morris, T. T., Hansen, A. F., Rasheed, H., Cho, Y., Chittoor, G., Ahlskog, R., Lind, P. A., Palviainen, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01062-7 · Cited by 377
“MR was originally conceptualized in the context of parent–offspring trios where offspring inherit a random allele from each parent 24 .”
Palmer, L. J. (2007) · The Lancet · doi:10.1016/s0140-6736(07)60924-6 · Cited by 387
Mendelian Randomisation and Causal Inference in Observational Epidemiology
Sheehan, N. A., Didelez, V., Burton, P. R., & Tobin, M. D. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050177 · Cited by 373
Richmond, R. C., Simpkin, A. J., Woodward, G., Gaunt, T. R., Lyttleton, O., McArdle, W. L., Ring, S. M., Smith, A. D. A. C., Timpson, N. J., Tilling, K., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddu739 · Cited by 372
Axes of a revolution: challenges and promises of big data in healthcare
Shilo, S., Rossman, H., & Segal, E. (2020) · Nature Medicine · doi:10.1038/s41591-019-0727-5 · Cited by 370
Vimaleswaran, K. S., Cavadino, A., Berry, D. J., Jorde, R., Dieffenbach, A. K., Lu, C., Alves, A. C., Heerspink, H. J. L., Tikkanen, E., Eriksson, J., et al. (2014) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(14)70113-5 · Cited by 373
C‐reactive protein and coronary heart disease: a critical review
Casas, J. P., Shah, T., Hingorani, A. D., Danesh, J., & Pepys, M. B. (2008) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2008.02015.x · Cited by 367
Guan, Y., & Stephens, M. (2011) · The Annals of Applied Statistics · doi:10.1214/11-aoas455 · Cited by 357
Gilbody, S. M., Lewis, S. J., & Lightfoot, T. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj347 · Cited by 361
Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits
Shi, H., Mancuso, N., Spendlove, S. J., & Paşaniuc, B. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.022 · Cited by 351
Brion, M.-J. A., Lawlor, D. A., Matijasevich, A., Horta, B., Anselmi, L., Araújo, C. L., Menezes, A. M. B., Victora, C. G., & Smith, G. D. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr020 · Cited by 350
Henderson, J., Northstone, K., Lee, S. P., Liao, H., Zhao, Y., Pembrey, M. E., Mukhopadhyay, S., Smith, G. D., Palmer, C. N. A., McLean, W. H. I., et al. (2008) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2008.01.026 · Cited by 352
The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers
Amos, C. I., Dennis, J., Wang, Z., Byun, J., Schumacher, F. R., Gayther, S. A., Casey, G., Hunter, D. J., Sellers, T. A., Gruber, S. B., et al. (2016) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-16-0106 · Cited by 342
Multi-omics integration in biomedical research – A metabolomics-centric review
Wörheide, M. A., Krumsiek, J., Kastenmüller, G., & Arnold, M. (2020) · Analytica Chimica Acta · doi:10.1016/j.aca.2020.10.038 · Cited by 373
A life-course approach to the aetiology of late-onset dementias
Whalley, L. J., Dick, F. D., & McNeill, G. (2005) · The Lancet Neurology · doi:10.1016/s1474-4422(05)70286-6 · Cited by 358
C-reactive protein and its role in metabolic syndrome: mendelian randomisation study
Timpson, N. J., Lawlor, D. A., Harbord, R. M., Gaunt, T. R., Day, I. N. M., Palmer, L. J., Hattersley, A. T., Ebrahim, S. B. J., Lowe, G. D. O., Rumley, A., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67786-0 · Cited by 345
Self-citationBjørge, T., Engeland, A., Tverdal, A., & Smith, G. D. (2008) · American Journal of Epidemiology · doi:10.1093/aje/kwn096 · Cited by 348
Using Mendelian Randomization to Improve the Design of Randomized Trials
Ference, B. A., Holmes, M. V., & Smith, G. D. (2021) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a040980 · Cited by 338
Smith, G. D. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr117 · Cited by 339
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 333
Holmes, M. V., Newcombe, P. J., Hubacek, J. A., Sofat, R., Ricketts, S. L., Cooper, J. A., Breteler, M. M. B., Bautista, L. E., Sharma, P. K., Whittaker, J. C., et al. (2011) · The Lancet · doi:10.1016/s0140-6736(11)60872-6 · Cited by 331
Self-citationPalmer, T., Sterne, J. A. C., Harbord, R. M., Lawlor, D. A., Sheehan, N. A., Meng, S., Granell, R., Smith, G. D., & Didelez, V. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr026 · Cited by 326
Porcu, 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
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 333
Can the impact of childhood adiposity on disease risk be reversed? A Mendelian randomization study.
Richardson, T. G., Sanderson, E., Elsworth, B., Tilling, K., & Davey Smith, G. (2019) · doi:10.1101/19008011 · Cited by 321
Lin, Z., Deng, Y., & Pan, W. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009922 · Cited by 326
Yang, Q., Köttgen, A., Dehghan, A., Smith, A. V., Glazer, N. L., Chen, M., Chasman, D. I., Aspelund, T., Eiríksdóttir, G., Harris, T. B., et al. (2010) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.109.934455 · Cited by 319
Zengini, E., Hatzikotoulas, K., Tachmazidou, I., Steinberg, J., Hartwig, F. P., Southam, L., Hackinger, S., Boer, C. G., Styrkársdóttir, U., Gilly, A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0079-y · Cited by 323
Carter, A. R., Gill, D., Davies, N. M., Taylor, A. E., Tillmann, T., Vaucher, J., Wootton, R. E., Munafo, M. R., Hemani, G., Malik, R., et al. (2019) · BMJ · doi:10.1136/bmj.l1855 · Cited by 323
Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine
Nimptsch, K., Konigorski, S., & Pischon, T. (2018) · Metabolism · doi:10.1016/j.metabol.2018.12.006 · Cited by 333
Freathy, R. M., Timpson, N. J., Lawlor, D. A., Pouta, A., Ben‐Shlomo, Y., Ruokonen, A., Ebrahim, S. B. J., Shields, B. M., Zeggini, E., Weedon, M. N., et al. (2008) · Diabetes · doi:10.2337/db07-1466 · Cited by 314
Self-citationLarsson, S. C., & Burgess, S. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104154 · Cited by 335
Chen, L., Davey Smith, G., Harbord, R. M., & Lewis, S. J. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050052 · Cited by 314
Lane, J. M., Vlasac, I., Anderson, S. G., Kyle, S. D., Dixon, W. G., Bechtold, D. A., Gill, S., Little, M. A., Luik, A., Loudon, A., et al. (2016) · Nature Communications · doi:10.1038/ncomms10889 · Cited by 312
Timpson, N. J., Nordestgaard, B. G., Harbord, R. M., Zacho, J., Frayling, T. M., Tybjærg-Hansen, A., & Davey Smith, G. (2011) · International Journal of Obesity · doi:10.1038/ijo.2010.137 · Cited by 310
Mendelian 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 313
Self-citationLewis, S. J., Ebrahim, S. B. J., & Smith, G. D. (2005) · BMJ · doi:10.1136/bmj.38611.658947.55 · Cited by 307
Self-citationHuman CRP Gene Polymorphism Influences CRP Levels
Brull, D. J., Serrano, N. C., Zito, F., Jones, L., Montgomery, H., Rumley, A., Sharma, P. K., Lowe, G. D. O., WORLD, M. J., Humphries, S. E., et al. (2003) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/01.atv.0000084640.21712.9c · Cited by 304
Inflammatory Biomarkers and Risk of Schizophrenia
Hartwig, F. P., Borges, M. C., Horta, B. L., Bowden, J., & Smith, G. D. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.3191 · Cited by 301
Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification
Dapas, M., & Dunaif, A. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnac001 · Cited by 300
Uric Acid and Cardiovascular Events
Kleber, M. E., Delgado, G., Grammer, T. B., Silbernagel, G., Huang, J., Krämer, B. K., Ritz, E., & März, W. (2015) · Journal of the American Society of Nephrology · doi:10.1681/asn.2014070660 · Cited by 302
Genetic epidemiology and public health: hope, hype, and future prospects
Smith, G. D., Ebrahim, S. B. J., Lewis, S. J., Hansell, A., Palmer, L. J., & Burton, P. R. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67601-5 · Cited by 301
Self-citation
Metadata
- Authors
- Davey Smith, G., & Ebrahim, S.
- ORCID
- Ebrahim, Shah
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-02
- Volume
- 32
- Issue
- 1
- Pages
- 1-22
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- OpenAlex
- W2106036991
- Cited by
- 5,882
- References
- 179
- Citations by year
- 2026: 333 · 2025: 673 · 2024: 1162 · 2023: 681 · 2022: 453 · 2021: 442 · 2020: 353 · 2019: 235 · 2018: 201 · 2017: 157 · 2016: 163 · 2015: 118 · 2014: 115 · 2013: 94 · 2012: 101
- FWCI
- 31.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
- Keywords
- Mendelian randomization, genetic epidemiology, environmental factors in disease, causal inference, confounding, reverse causality, linkage disequilibrium, canalization, genetic polymorphism, genetic testing, public health genetics, disease prevention, Human Genome Project, pathogenesis, cancer genetics
- MeSH terms
- Animals, Disease, Environmental Exposure, Genotype, Humans, Polymorphism, Genetic, Molecular Epidemiology, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
