| dc.description.abstract | Gene-environment interactions are an area of increasing interest in complex hu-
man diseases. The first step in any study of the interactions between genes and the
environment involves identifying genes which influence the trait of interest. In this
dissertation, a new method for using the information in complex pedigrees to per-
form a joint linkage disequilibrium and linkage mapping of quantitative trait loci is
developed. Subsequently, methods are needed to determine the interaction, if any,
between these genes and environmental risk factors. Many of these factors, such as
weight or age, are continuous and little is known about their distributions. Thus, we
introduce a new method for estimating the gene-environment interaction parameters
in a logistic regression for the case-control study design. In doing so, we make the
assumption that in the underlying population, the distributions of the genetic factors
and the environmental covariates are independent. Additionally, we treat the envi-
ronmental parameters nonparametricly, utilizing the profile likelihood. Furthermore,
the methodology we develop is also general enough to be used on many different types
of genetic information, including haplotypes, and can accommodate missing genotype
data. The method is also extended to allow analysis in the presence of population
stratification or genotype misclassification. We show that the standard errors of pa-
rameter estimates using our method are smaller than those found using complete data
only. These methods are illustrated using simulations and are applied to a real data
set exploring the interaction between genotype and environment in disease risk. | en |
