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Cited by 577Genetic Associations and EpidemiologyStatistical Methods in EpidemiologyAdvanced Causal Inference Techniques

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Clustered Environments and Randomized Genes: A Fundamental Distinction between Conventional and Genetic Epidemiology

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Smith, George Davey, Lawlor, Debbie A, Harbord, Roger, Timpson, Nic, Day, Ian, Ebrahim, Shah·2007·PLoS Medicine
DOI 10.1371/journal.pmed.0040352PMC PMC2121108
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Abstract

BACKGROUND: In conventional epidemiology confounding of the exposure of interest with lifestyle or socioeconomic factors, and reverse causation whereby disease status influences exposure rather than vice versa, may invalidate causal interpretations of observed associations. Conversely, genetic variants should not be related to the confounding factors that distort associations in conventional observational epidemiological studies. Furthermore, disease onset will not influence genotype. Therefore, it has been suggested that genetic variants that are known to be associated with a modifiable (nongenetic) risk factor can be used to help determine the causal effect of this modifiable risk factor on disease outcomes. This approach, mendelian randomization, is increasingly being applied within epidemiological studies. However, there is debate about the underlying premise that associations between genotypes and disease outcomes are not confounded by other risk factors. We examined the extent to which genetic variants, on the one hand, and nongenetic environmental exposures or phenotypic characteristics on the other, tend to be associated with each other, to assess the degree of confounding that would exist in conventional epidemiological studies compared with mendelian randomization studies. METHODS AND FINDINGS: We estimated pairwise correlations between nongenetic baseline variables and genetic variables in a cross-sectional study comparing the number of correlations that were statistically significant at the 5%, 1%, and 0.01% level (alpha = 0.05, 0.01, and 0.0001, respectively) with the number expected by chance if all variables were in fact uncorrelated, using a two-sided binomial exact test. We demonstrate that behavioural, socioeconomic, and physiological factors are strongly interrelated, with 45% of all possible pairwise associations between 96 nongenetic characteristics (n = 4,560 correlations) being significant at the p < 0.01 level (the ratio of observed to expected significant associations was 45; p-value for difference between observed and expected < 0.000001). Similar findings were observed for other levels of significance. In contrast, genetic variants showed no greater association with each other, or with the 96 behavioural, socioeconomic, and physiological factors, than would be expected by chance. CONCLUSIONS: These data illustrate why observational studies have produced misleading claims regarding potentially causal factors for disease. The findings demonstrate the potential power of a methodology that utilizes genetic variants as indicators of exposure level when studying environmentally modifiable risk factors.

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Research works printed but not linked: 45

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Metadata

Authors
Smith, G. D., Lawlor, D. A., Harbord, R., Timpson, N., Day, I., & Ebrahim, S.
Affiliations
  • University of Bristol (GB)
  • London School of Hygiene & Tropical Medicine (GB)
Corresponding author
Smith, George Davey
ORCID
Venue
PLoS Medicine
Publisher
Public Library of Science (PLoS)
Published
2007-12-11
Volume
4
Issue
12
Pages
e352
ISSN
  • 1549-1277 (ISSN-L)
  • 1549-1676
DOI
10.1371/journal.pmed.0040352
PMC
PMC2121108
OpenAlex
W2093053972
Cited by
569
References
51
Citations by year
2026: 20 · 2025: 45 · 2024: 74 · 2023: 44 · 2022: 46 · 2021: 30 · 2020: 26 · 2019: 22 · 2018: 22 · 2017: 31 · 2016: 34 · 2015: 18 · 2014: 24 · 2013: 26 · 2012: 23
FWCI
10.35
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Explore Bristol Research (submittedVersion)
Funders
  • British Heart Foundation
  • Medical Research Council
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
confounding, genetic variants, Mendelian randomization, genetic epidemiology, reverse causality, non-genetic risk factors, socioeconomic factors, behavioral factors, physiological factors, causal inference
MeSH terms
Aged, Cross-Sectional Studies, Environment, Epidemiologic Methods, Female, Genotype, United Kingdom, Heart Diseases, Humans, Life Style, Middle Aged, Phenotype, Risk Factors, Socioeconomic Factors, Genetic Variation, Reproducibility of Results, Health Behavior, Confounding Factors, Epidemiologic, Cluster Analysis, Risk Assessment, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide

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