Bayesian Analysis of Studies of Gene-Environment Interaction

Bhramar Mukherjee
Department of Biostatistics
University of Michigan

Wednesday, April 4, 2007
3:30pm
Moos 2-690
Minneapolis Campus

Abstract:

Bhramar Mukherjee will discuss the problem of retrospective modelling of case-control data for studying gene-environment interactions in a semiparametric Bayesian framework. The special feature of gene-environment interaction studies is that in many situations it is scientifically plausible to assume that the genetic and environmental factors are independent in the underlying population. Under this additional constraint of gene-environment independence, on can derive more efficient estimation techniques than the traditional prospective logistic regression analysis (Piegorsch et al, 1994; Chatterjee and Carroll, 2005). However, the efficient estimates from the retrospective likelihood may be severely biased under the violation of the independence assumption. Stratification effects present in the population could potentially induce non-independence among genetic factors and environmental exposures. She will first provide a semiparametric Bayesian approach to model stratification effects under the assumption of gene-environment independence. She will then propose an alternative to relax the constraint of gene-environment independence in a natural Bayesian framework to strike a compromise between efficiency and robustness. She will also analyze data from a case-control study on ovarian cancer patients conducted in Israel to illustrate their methods. Collaborators on material related to the presented work are: Nilanjan Chatterjee (National Cancer Institute), Malay Ghosh (University of Florida), Li Zhang (Cleveland Clinical Foundation), and Samiran Sinha (Texas A & M).

A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.biostat.umn.edu/seminar_academic.html