title: Genetic association and gene-environment interaction: a new method for overcoming the lack of exposure information in controls.
abstract: The use of a reference control panel in genome-wide association studies is an interesting solution to the problem of how to reduce costs. In such designs, data on relevant environmental factors are usually collected only in cases, making it more difficult to deal with potential gene-environment interactions when testing for genetic association. However, under certain circumstances, neglecting an existing interaction with the environment may be detrimental in terms of statistical power to detect the genetic factor. In this paper, the authors propose a novel method based on a multinomial logistic regression model to overcome the lack of environmental exposure information in controls, by contrasting both exposed and unexposed cases with the control sample. For each case group, a genetic effect-size parameter is estimated, and the genetic association and the gene-environment interaction are tested jointly. The authors evaluate the performance of this method through asymptotic computations and simulations of cases and population controls under different models. In the presence of a gene-environment interaction, this approach outperforms other available methods that test for genetic association and gene-environment interaction either separately or jointly. Interestingly, it even has better power than the joint test requiring full knowledge of the environmental information in both cases and controls.
authors.affiliation: Université Paris-Sud, Le Kremlin Bicêtre, France. remi.kazma@inserm.fr
authors.forename: Rémi
authors.initials: R
authors.lastname: Kazma
authors.forename: Marie-Claude
authors.initials: MC
authors.lastname: Babron
authors.forename: Emmanuelle
authors.initials: E
authors.lastname: Génin
mesh_terms: D004777:Environment; D004781:Environmental Exposure; D015340:Epidemiologic Research Design; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D016015:Logistic Models