title: Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs.
abstract: To evaluate the risk of a disease associated with the joint effects of genetic susceptibility and environmental exposures, epidemiologic researchers often test for non-multiplicative gene-environment effects from case-control studies. In this article, we present a comparative study of four alternative tests for interactions: (i) the standard case-control method; (ii) the case-only method, which requires an assumption of gene-environment independence for the underlying population; (iii) a two-step method that decides between the case-only and case-control estimators depending on a statistical test for the gene-environment independence assumption and (iv) a novel empirical-Bayes (EB) method that combines the case-control and case-only estimators depending on the sample size and strength of the gene-environment association in the data. We evaluate the methods in terms of integrated Type I error and power, averaged with respect to varying scenarios for gene-environment association that are likely to appear in practice. These unique studies suggest that the novel EB procedure overall is a promising approach for detection of gene-environment interactions from case-control studies. In particular, the EB procedure, unlike the case-only or two-step methods, can closely maintain a desired Type I error under realistic scenarios of gene-environment dependence and yet can be substantially more powerful than the traditional case-control analysis when the gene-environment independence assumption is satisfied, exactly or approximately. Our studies also reveal potential utility of some non-traditional case-control designs that samples controls at a smaller rate than the cases. Apart from the simulation studies, we also illustrate the different methods by analyzing interactions of two commonly studied genes, N-acetyl transferase type 2 and glutathione s-transferase M1, with smoking and dietary exposures, in a large case-control study of colorectal cancer.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Bhramar
authors.initials: B
authors.lastname: Mukherjee
authors.forename: Jaeil
authors.initials: J
authors.lastname: Ahn
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Gruber
authors.forename: Gad
authors.initials: G
authors.lastname: Rennert
authors.forename: Victor
authors.initials: V
authors.lastname: Moreno
authors.forename: Nilanjan
authors.initials: N
authors.lastname: Chatterjee
mesh_terms: D001499:Bayes Theorem; D016022:Case-Control Studies; D003110:Colonic Neoplasms / Q000453:epidemiology / Q000235:genetics; D004777:Environment; D004781:Environmental Exposure; D004812:Epidemiologic Methods; D004813:Epidemiology / Q000592:standards; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D016017:Odds Ratio; D015203:Reproducibility of Results; D012107:Research Design; D012307:Risk Factors