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
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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]
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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
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