This looks interesting.
Department of Biostatistics Seminar/Workshop Series: A Multivariate Methodology for Analyzing Genome-wide Association Studies, by Janice Brodsky, PhD, UCLA.
Wednesday, December 16, 1:30-2:30pm, MRB III Conference Room 1220
Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users
In the last few years, high-dimensional genome-wide association (GWA) studies have become a common tool in genetics for investigating which genes are associated with physical traits. However, the results of many GWA studies have fewer genes than expected or even no genes at all. This does not necessarily indicate that there are no genetic associations in the data: genes with weaker associations or which only work in groups will be missed with the standard GWA statistical analysis. We present a multivariate methodology for analyzing GWA data which is designed to handle weaker signals, dependent data, and multicollinearity. We applied this method to a large GWA study, and the results were consistent with previously performed studies. We also discuss extensions of the methodology.