G = E: What GWAS Can Tell Us about the Environment.
paper
Cited
Public
- Authors
- Gage, Suzanne H; Davey Smith, George; Ware, Jennifer J; Flint, Jonathan; Munafò, Marcus R
- Year
- 2016
- Journal
- PLoS genetics
- PMID
- 26866486
- DOI
- 10.1371/journal.pgen.1005765
- PMCID
- PMC4750859
As our understanding of genetics has improved, genome-wide association studies (GWAS) have identified numerous variants associated with lifestyle behaviours and health outcomes. However, what is sometimes overlooked is the possibility that genetic variants identified in GWAS of disease might reflect the effect of modifiable risk factors as well as direct genetic effects. We discuss this possibility with illustrative examples from tobacco and alcohol research, in which genetic variants that predict behavioural phenotypes have been seen in GWAS of diseases known to be causally related to these behaviours. This consideration has implications for the interpretation of GWAS findings.
Illustration of the Mendelian randomization framework.In Mendelian randomization, if there is a causal effect of the exposure (e.g., smoking heaviness) that is being captured by the genotype on the outcome (e.g., lung cancer), then an association of genotype with the outcome should be detectable in a sufficiently large unstratified GWAS (panel A). This can be confirmed in a stratified analysis, where an association of genotype with the outcome should only be seen in the exposed group (i.e., smokers, panel B) and not the unexposed group (i.e., never-smokers, panel C). This is a special case of gene × environment (G × E) interaction, where both G and E are known, although it will not always be possible to stratify on the exposure, and stratification (which can be considered a form of statistical adjustment) can introduce other potential biases in certain circumstances (see Box 3).
Illustration of collider bias.Panel A shows the basic premise of collider bias. In this example, a bell is sounded whenever either coin come up “heads.” The result of one coin toss is independent of the other. However, if we stratify on the bell ringing, seeing “heads” on both coins is not independent and a spurious correlation is induced. Panel B shows this with the example of stratifying on smoking status. If the variant used as an instrument for heaviness of smoking is also associated with smoking status (i.e., ever-smoker versus never-smoker), and if BMI also influences smoking status, then there is a risk of collider bias if we stratify on smoking status. Panel C shows an example where stratification will not introduce collider bias, as sex is not an effect of either possession of a genetic variant that predicts alcohol consumption or of blood pressure.
Association of ADH1B genotype with risk of upper aerodigestive cancer.Risk of upper aerodigestive cancer by ADH1B genetic variation, stratified by drinking intensity and smoking status, is shown as the odds ratio (OR) of upper aerodigestive cancer by re1229984 (ADH1B) genotype comparing rare allele (dominant model) carriers versus common allele homozygous genotype. ORs are standardised by age, sex, study centre, cumulative alcohol consumption, and, when relevant, smoking. ORs and 95% CI are derived from fixed effects models. Figure adapted from Hashibe et al. (2008) [57] with permission granted by Nature Publishing Group.
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| Name | Type |
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| acetaldehyde | drug |
| acetic acid | drug |
| alcohol | phenotype |
| Alcohol_use | phenotype |
| Alcohol Use | phenotype |
| ALDH2 | gene |
| ALDH2 minor allele local | variant |
| antipsychotic medication prescription local | phenotype |
| behavioral phenotypes | phenotype |
| blood pressure | phenotype |
| BMI | phenotype |
| caffeine | drug |
| cannabis use | phenotype |
| cardiovascular events | phenotype |
| Chrna3 | gene |
| CHRNA5 | gene |
| CHRNA5-A3-B4 local | gene |
| CHRNA5-A3-B4 genotype local | variant |
| CHRNA5-CHRNA3-CHRNB4 locus local | gene |
| Chrnb4 | gene |
| chronic obstructive pulmonary disease | phenotype |
| cigarettes | phenotype |
| coronary heart disease | phenotype |
| cotinine | drug |
| cotinine levels | drug |
| daily smokers local | cohort |
| disease outcome | phenotype |
| disease outcomes local | phenotype |
| East Asian | cohort |
| esophageal cancer | phenotype |
| European ancestry | cohort |
| ever-smokers local | cohort |
| Ever-smokers local | cohort |
| exposure | phenotype |
| FTO | gene |
| genetic variants | cohort |
| genocopy local | phenotype |
| heavy smoking | phenotype |
| hypertension | phenotype |
| intelligence | phenotype |
| LDL cholesterol | phenotype |
| LDL cholesterol level local | phenotype |
| lung cancer | phenotype |
| Metabolite levels local | phenotype |
| modifiable exposure local | drug |
| never smokers | phenotype |
| nicotine | drug |
| NPC1L1 local | gene |
| obesity | phenotype |
| outcome | phenotype |
| peripheral arterial disease | phenotype |
| personality traits | phenotype |
| phenocopy local | phenotype |
| polygenic risk score | cohort |
| Polygenic risk score for schizophrenia local | variant |
| population | cohort |
| Psychiatric Genomics Consortium | cohort |
| psychosis | phenotype |
| schizophrenia | phenotype |
| smoking | phenotype |
| Smoking-associated variants local | variant |
| smoking heaviness | phenotype |
| substance use | phenotype |
| tobacco use | phenotype |
| Tobacco_use local | phenotype |
| tobacco-use phenotypes local | phenotype |
| total cholesterol | phenotype |
| UK Biobank | cohort |
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