Buehler-Martin Lecturer Series - March 31, 2005
University of Minnesota
School of Statistics
College of Liberal Arts

Established by Mr. and Mrs. Thomas Martin
in Memory of
Robert J. Buehler, Professor of Statistics (1963-1988)

Semiparametric Methods for Gene-environment Case-control Studies When Gene and Environment Are Independent in the Population.

Raymond J. Carroll
Department of Statistics
Texas A & M University

Thursday, March 31, 2005
3:30 PM, 115 Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall

Abstract

We consider case-control studies of gene and environment interactions using prospective logistic regression models. In a typical case-control study, neither the intercept of the logistic regression nor the population probability of disease can be identified. However, in many cases it is reasonable to assume that genotype and environment are independent in the population, possibly conditional on covariates to account for population stratification. In such as case, we show that the intercept and population probability of disease are identified. We develop a semiparametric likelihood approach for this problem, showing that it leads to more efficient estimates of gene-environment interaction parameters than the standard approach. In addition, if the probability of disease is known in the population, we show efficiency gains for estimating gene-environment interactions, again in contrast to the standard approach. Multiple extensions are discussed, including to missing genotype data, haplotype data, and measurement error in genotypes or environmental variables. Applications to two important data sets are discussed.

This is joint work with Nilanjan Chatterjee (National Cancer Institute).