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Challenges to interpretation of gene-environment research.

ICCA Genomics Workshop, Orlando, FL, March 7-8, 2001. Brussels, Belgium: International Council of Chemical Associations, 2001 Mar; :1-2
Epidemiologists increasingly are presented with powerful new tools to piece together the roles of environmental/occupational and genetic factors in assessing disease risks. High throughput tools, such as microarrays and 2D gels, present new opportunities, but their utility for assessing disease risks in populations is still some time from being realized. Making sense from population studies incorporating genes and environment has a rich history, but1 one fraught with problems. Despite notable examples to the contrary, the nature-nurture debate has given way to the fundamental realization that both genes and environment playa role in disease. Unfortunately, however, the degree of increased risk in exposed genetically susceptible individuals is highly dependent on the relationship between genes and exposure in terms of their effect on disease risk; and we seldom have lad information about this. Depending on the type of model for gene-environment interaction, risks can vary dramatically. At least six different types of gene-environment interaction have been described. These have been generally considered with the dichotomous conditions, a single susceptibility genotype (present or absent), and a single environmental factor (present or absent). For example, in a paper by Khoury and Wagener, the risk to an exposed person with a rare (1 % prevalence) susceptibility genotype ranges from8% (under a multiplicative model of interaction) to 95.6% (when we assume that the exposure has no effect in susceptibles, but the genotype raises risk in unexposed as well as exposed persons). Various study designs have been identified for the detection of gene-environment interaction. These include: case-only designs, case-control study designs using unrelated controls, case-control design using related controls, case-control designs using relatives of cases and population- or hospital-based controls, twin-study designs, family-study designs, and combined segregation and linkage analyses. There is currently a debate whether bias from population stratification (the mixture of individuals from heterogeneous genetic backgrounds) undermines the credibility of epidemiologic studies designed to estimate the association between genotype and risk of disease. However, Wacholder et al. found only a small bias from stratification in a well-designed case-control study of genetic factors that ignored ethnicity among non-Hispanic, U.S. Caucasians of European origin. When important confounding caused by population stratification does occur, it should be controllable by the usual design and analytical features employed by epidemiologists.
Epidemiology; Statistical-analysis; Genetic-factors; Genes; Racial-factors; Case-studies
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ICCA Genomics Workshop, Orlando, FL, March 7-8, 2001