Statistical Genetic Methods for Cancer Gene Studies

Project: Research project

Project Details


DESCRIPTION (provided by applicant): Finding the genetic determinants of
common complex disorders is a formidable task, since many genetic and
environmental factors, either individually or in combination, influence trait
expression. The use of association studies to tackle this challenge is
becoming more widespread, especially with the release of the human genome
sequence. However, large-scale association studies with sufficient power will
be required to find these genes with modest effect sizes. Family-based
association studies have consistently been shown to be less powerful than
case-control studies, but case-control studies do not efficiently use the data
collected for linkage studies and national registries. Therefore, the overall
goal of this mentored proposal is to develop and investigate novel study
designs and statistical analysis methods for association studies that are a
combination of the two aforementioned designs. Specifically, we will:
(1) examine sampling strategies for our proposed hybrid design in order to
provide guidelines that enable researchers to choose the most informative
(2) derive and evaluate statistical methods to be used for our proposed hybrid
design. We will consider methods for stratified analyses, for matched
designs, for DNA pooling, and for testing interaction effects.
(3) develop and examine statistical methods that correct for population
stratification, since our proposed designs may be susceptible to the effects
of population stratification.
(4) develop user-friendly software that can be used for the design and
analysis of our proposed hybrid designs.
Additionally, through the planned didactic training in areas including
statistical genetics and molecular biology, this mentored proposal will
provide me the opportunity to build a strong foundation upon which to build
career conducting methodological research involving genetic studies of cancer.
Effective start/end date8/1/027/31/07


  • Medicine(all)