Biostatistics Advance Access published online on May 8, 2007
Biostatistics, doi:10.1093/biostatistics/kxm011
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Retrospective analysis of haplotype-based case–control studies under a flexible model for gene–environment association
Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, People's Republic of China
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Boulevard, EPS 8038, Rockville, MD 20852, USA chattern{at}mail.nih.gov
Department of Statistics, Texas A&M University, TAMU 3143, College Station, TX 77843-3143, USA
* To whom correspondence should be addressed.
Genetic epidemiologic studies often involve investigation of the association of a disease with a genomic region in terms of the underlying haplotypes, that is the combination of alleles at multiple loci along homologous chromosomes. In this article, we consider the problem of estimating haplotype–environment interactions from case–control studies when some of the environmental exposures themselves may be influenced by genetic susceptibility. We specify the distribution of the diplotypes (haplotype pair) given environmental exposures for the underlying population based on a novel semiparametric model that allows haplotypes to be potentially related with environmental exposures, while allowing the marginal distribution of the diplotypes to maintain certain population genetics constraints such as Hardy–Weinberg equilibrium. The marginal distribution of the environmental exposures is allowed to remain completely nonparametric. We develop a semiparametric estimating equation methodology and related asymptotic theory for estimation of the disease odds ratios associated with the haplotypes, environmental exposures, and their interactions, parameters that characterize haplotype–environment associations and the marginal haplotype frequencies. The problem of phase ambiguity of genotype data is handled using a suitable expectation–maximization algorithm. We study the finite-sample performance of the proposed methodology using simulated data. An application of the methodology is illustrated using a case–control study of colorectal adenoma, designed to investigate how the smoking-related risk of colorectal adenoma can be modified by "NAT2," a smoking-metabolism gene that may potentially influence susceptibility to smoking itself.
Keywords: Case–control studies; EM algorithm; Gene–environment interactions; Haplotype; Semiparametric methods
Received October 13, 2006; revised March 2, 2007; accepted for publication March 14, 2007.