CoChat
Sign Up
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

Open access
Smith, George Davey, Lawlor, Debbie A, Harbord, Roger, Timpson, Nic, Day, Ian, Ebrahim, Shah·2007·PLoS Medicine
DOI 10.1371/journal.pmed.0040352PMC PMC2121108
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pmed.0040352

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Histogram of Statistically Significant (at α = 1%) Age-Adjusted Pairwise Correlation Coefficients between 96 Nongenetic Characteristics. British Women Aged 60–79 y
Figure 1 Histogram of Statistically Significant (at α = 1%) Age-Adjusted Pairwise Correlation Coefficients between 96 Nongenetic Characteristics. British Women Aged 60–79 yCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 45

  1. Davey Smith, 2001, Int J Epidemiol, 30, 1
  2. Lawlor, 2004, Lancet, 363, 1724
  3. Vandenbroucke, 2004, Lancet, 363, 1728
  4. Phillips, 1992, Stat Med, 11, 953
  5. Davey Smith, 1992, BMJ, 305, 757
  6. 1998, Modern epidemiology. 2nd edition
  7. Sterne, 2001, BMJ, 322, 226
  8. Sterne, 2001, BMJ, 323, 101
  9. Macleod, 2002, BMJ, 324, 1247
  10. Davey Smith, 2003, Int J Epidemiol, 32, 1
  11. Lewis, 2005, Cancer Epidemiol Biomarkers Prev, 14, 1967
  12. Casas, 2006, Int J Epidemiol, 35, 922
  13. Keavney, 2006, Int J Epidemiol, 35, 935
  14. Timpson, 2005, Lancet, 366, 1954
  15. Davey Smith, 2005, Arterioscler Thromb Vasc Biol, 25, 2228
  16. Davey Smith, 2007, J R Soc Med, 100, 432
  17. Nitsch, 2006, Am J Epidemiol, 163, 397
  18. Wacholder, 2000, J Natl Cancer Inst, 92, 1151
  19. Wacholder, 2002, Cancer Epidemiol Biomark Prev, 11, 513
  20. Thomas, 2002, Cancer Epidemiol Biomark Prev, 11, 505
  21. Cardon, 2003, Lancet, 361, 598
  22. Hoggart, 2003, Am J Hum Genet, 72, 1492
  23. Epstein, 2007, Am J Hum Genet, 80, 921
  24. Davey Smith, 2005, Arterioscler Thromb Vasc Biol, 25, 1051
  25. Phillips, 1991, J Clin Epidemiol, 44, 1223
  26. Davey Smith, 2002, BMJ, 325, 1437
  27. Davey Smith, 1992, Lancet, 340, 709
  28. Manson, 1993, Circulation, 88, I-70
  29. Ioannidis, 2005, J Clin Epidemiol, 58, 543
  30. Lawlor, 2004, Lancet, 364, 754
  31. Khoury, 1996, Am J Epidemiol, 144, 207
  32. Shin, 2007, Stat Appl Genet Mol Biol, 6, 1
  33. Umbach, 1997, Stat Med, 16, 1731
  34. Chatterjee, 2005, Biometrika, 92, 399
  35. Chatterjee, 2005, Genet Epidemiol, 28, 138
  36. Modan, 2001, N Engl J Med, 345, 235
  37. Davey Smith, 2007, Eur J Hum Genet
  38. Campbell, 2005, Nat Genet, 37, 868
  39. Thompson, 2003, Atherosclerosis, 167, 195
  40. Dachet, 2000, Arterioscler Thromb Vasc Biol, 20, 507
  41. Ordovas, 2000, Arterioscler Thromb Vasc Biol, 20, 1323
  42. Georges, 1996, Genet Epidemiol, 13, 179
  43. Bhatti, 2005, Cancer Epidemiol Biomarkers Prev, 14, 2449
  44. Smits, 2004, Int J Cancer, 110, 266
  45. Jaffee, 2007, Mol Psychiatry, 12, 432

Cited by

Sorted by citations

See all 577

Related

See all

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

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