Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

Genetical genomics is an approach that blends the mapping of quantitative trait loci (QTL) with microarray analysis. The approach can be used to identify associations between the allelic state of a genomic region and a gene's transcript abundance. However, the large number of microarrays required for adequate power results in high material and labor cost that prevent wide adoption of the genetical genomics strategy outside of some well funded laboratories. We present a method called selective transcriptional profiling that involves selecting an optimal subset of individuals to microarray from a larger set of individuals for which relatively inexpensive quantitative trait and molecular marker data are available. We show how to use microarray data from the selected individuals, along with the trait and marker data from all individuals, to identify genes whose transcript abundance is associated with a quantitative trait of interest through linkage to a trait QTL or correlation with the trait. Our methods for selection and analysis are derived within a missing data framework, developments based on both parametric likelihood and empirical likelihood will be presented. Empirical likelihood method for the analysis of missing data is a new development in the nonparametric setting.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.