SCIENCE AT THE EDGE SEMINAR
QB/GEDD
Friday, May 14 at 11:30am
Room 1400 Biomedical and Physical Sciences Bldg.
Refreshments at 11:15
Raymond J. Carroll
Department of Statistics, Texas A&M University
We consider population-based case-control studies of gene-environment and gene-gene interactions using prospective logistic regression models. Data sets like this arise when studying pathways based on haplotypes as well as in multistage
genome wide association studies (GWAS). In a typical case-control study, logistic regression is used and there is little power for detecting interactions. However, in many cases it is reasonable to assume that, for example, genotype and environment are independent
in the population, possibly conditional on factors to account for population stratification. In such as case, we have developed an extremely statistically powerful semiparametric approach for this problem, showing that it leads to much more efficient estimates
of gene-environment interaction parameters and the gene main effect than the standard approach: decreases of standard errors for the former are often by factors of 50% and more. The issue of course that arises is the very assumption of conditional independence,
because if that assumption is violated, biases result so that one can announce gene-environment interactions or gene effects even though they do not exist. We will describe a simple, computationally fast approach for gaining robustness without losing statistical
power, one based on the idea of Empirical Bayes methodology. Examples to colorectal adenoma studies of the NAT2 gene and prostate cancer in the VDR pathway are described to illustrate the approaches.
Helen Geiger
Administrative Assistant
Quantitative Biology Initiative/
Gene Expression in Development & Disease
Michigan State University
502B Biochemistry Building
East Lansing, MI 48824
Phone: (517) 432-9895
Fax: (517) 353-9334
Web:
http://qbmi.msu.edu
http://www.bch.msu.edu/GEDD/index.htm