This article is a US government work, and, as such, is in the public domain in the United States of America.
Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs†
Article first published online: 12 MAY 2008
Published 2008 Wiley-Liss, Inc.
Volume 32, Issue 7, pages 615–626, November 2008
How to Cite
Mukherjee, B., Ahn, J., Gruber, S. B., Rennert, G., Moreno, V. and Chatterjee, N. (2008), Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs. Genet. Epidemiol., 32: 615–626. doi: 10.1002/gepi.20337
- Issue published online: 27 OCT 2008
- Article first published online: 12 MAY 2008
- Manuscript Accepted: 5 MAR 2008
- Manuscript Revised: 13 FEB 2008
- Manuscript Received: 1 DEC 2007
- NSF. Grant Number: DMS 07-06935
- NIH. Grant Numbers: R03 CA130045-01, R01 CA81488
- Spanish Secretaria de Estado de Universidades e Investigacion. Grant Number: PR2006-04743
- National Heart Lung and Blood Institute. Grant Number: R01 HL091172-01
- National Cancer Institute
- case-only designs;
- empirical Bayes;
- gene-environment interaction;
- genome-wide scan;
- Molecular Epidemiology of Colorectal Cancer
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. Genet. Epidemiol. 2008. Published 2008 Wiley-Liss, Inc.