American Statistical Association
We propose a mixture modeling framework for identifying and characterizing multi-locus genotype-trait associations. Important advances offered by this paradigm include that the mixture modeling framework: (1) addresses the degrees-of-freedom challenge inherent in application of a fixed effects analysis of covariance; (2) relaxes the restrictive single normal assumption of a classical mixed effects model; and (3) offers an exploratory framework for discovery of underlying structure across multiple genetic loci. An application to data arising from a study of anti-retroviral associated dyslipidemia in HIV-infected individuals is presented. Extensive simulations studies are also implemented to investigate the performance of this approach. Finally, we describe extensions for unobservable haplotype-trait association analysis using a fully Bayesian model fitting approach.
Dr. Andrea Foulkes is an Associate Professor of Biostatistics and Head of the Biostatistics Program at the University of Massachusetts, Amherst. Her research is focused on developing analytic methods for high-dimensional data in HIV and cardiovascular disease research. Specifically, she heads an active research program on characterizing the relationships among molecular level markers, cellular immune modulation and clinical measures of disease progression. Among her accomplishments, Dr. Foulkes recently completed a graduate-level text on Applied Statistical Genetics with R (Springer, 2009).
|Date:||Thursday, February 25, 2010|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York