Epidemiological methods for studying genes and environmental factors in complex diseases

doi:10.1016/S0140-6736(01)06418-2

The Lancet

Volume 358, Issue 9290, 20 October 2001, Pages 1356-1360

https://doi.org/10.1016/S0140-6736(01)06418-2 Get rights and content

Summary

Exploration of the human genome presents new challenges and opportunities for epidemiological research. Although the case-control design is quicker and cheaper for study of associations between genotype and risk of disease than the cohort design, cohort studies have been recommended because they can be used to study gene-environment interactions. Although the scientific relevance of statistical interaction is pertinent, the main disadvantage of the case-control design—susceptibility to bias when estimating effects of exposures that are measured retrospectively—does not necessarily apply when studying statistical interaction between genotype and environmental exposure. Because correctly designed genetic association studies are equivalent to randomised comparisons between genotypes, conclusions about cause can be drawn from genetic associations even when the risk ratio is modest. For adequate statistical power to detect such modest risk ratios, the case-control design is more feasible than the cohort design.

Section snippets

Statistical and biological interaction

The meaning of the term “interaction” can be a cause of confusion. Because of this ambiguity, statisticians commonly preface their discussion of interaction with a disclaimer that statistical interaction should not be confused with biological or causal interaction.⁴ Cox⁵ noted that “The notion of interaction and indeed the very word itself are widely used in scientific discussion. This is largely due to the relation between interaction and causal connexion. Interaction in the statistical sense

Estimation of statistical interactions as a basis for targeting interventions

One argument for trying to obtain quantitative estimates of the joint effects of genotype and environment is that these estimates provide a basis for targeting interventions at individuals at high risk.⁷ However, the rationale for targeted intervention does not generally depend on the ability to detect statistical interaction in terms of lack of fit to a multiplicative model. In a multiplicative model, lack of statistical interaction between genotype and an environmental risk factor implies

Effect of considering gene and environment on statistical power

If disease is caused by an interplay of genetic and environmental factors, then, plausibly, studies will be more powerful if they measure both types of factor and model their joint effects in the analysis; this assumption, however, is too simplistic. Even if there are subgroups of genetically susceptible individuals, and the size of effect associated with an environmental exposure varies with genotype, the direction of this effect is unlikely to vary with genotype. This lack of variation limits

Usefulness of cohort studies in the study of statistical interactions

We have argued that study of statistical interactions between genetic and environmental factors in epidemiological studies is, perhaps, not as interesting as it might seem at first sight. Nevertheless, the quantification of joint effects remains a legitimate aim of such studies. Detection of departure from the widely used model of multiplicative effects provides some interesting lessons. In particular, we can re-examine the presumption that cohort studies are better than case-control studies

Using genetic associations to test hypotheses about causal pathways

Optimism about prospects for epidemiology in the post-genome era contrasts with a pessimistic and widely quoted view that modern epidemiology “faces its limits”.¹⁰ Most epidemiological research now focuses on attempts to estimate modest risk ratios associated with environmental or behavioural exposures that cannot be measured accurately. In this situation, standard techniques for control of bias and confounding become untrustworthy because the effects under study are small in relation to the

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ReviewEpidemiological methods for studying genes and environmental factors in complex diseases

Summary

Section snippets

Statistical and biological interaction

Estimation of statistical interactions as a basis for targeting interventions

Effect of considering gene and environment on statistical power

Usefulness of cohort studies in the study of statistical interactions

Using genetic associations to test hypotheses about causal pathways

J Clin Epidemiol

J Clin Epidemiol

Lancet

Am J Hum Genet

Am J Hum Genet

Lancet

J Nutr

Am J Hum Genet

Lancet

Review
Epidemiological methods for studying genes and environmental factors in complex diseases