An Empirical Bayes Approach to Interval-mapping of Expression Trait Loci

• Activity Number: 200
• Type: Contributed
• Date/Time: Tuesday, August 10, 2004 : 9:00 AM to 10:50 AM
• Sponsor: Biometrics Section
• Author(s): Meng Chen*+ and Christina Kendziorski and Hong Lan and Alan Attie
• Companies: University of Wisconsin, Madison and University of Wisconsin, Madison and University of Wisconsin, Madison and University of Wisconsin, Madison
• Keywords: empirical Bayes ; mixture model ; microarray ; QTL mapping ; expression trait loci (ETL) mapping

Abstract

Genetic linkage analysis has traditionally focused on mapping loci that affect one or more phenotypes. With microarray technology, we can apply such analysis to global patterns of gene expression, treating messenger RNA transcript abundances as quantitative traits and associating levels of gene expression with genotyping data. A number of groups have recently tackled this problem by applying QTL-mapping ideas. However, in mapping transcript expression levels, there are usually thousands of traits to be considered simultaneously, which greatly inhibits the efficiency of traditional QTL-mapping techniques. A model-based systematic framework is needed. We have developed an empirical Bayes approach to enable interval-mapping of ETL based on mixing over both unknown genotype and unknown expression levels. This formulation introduces computational challenges beyond those of standard QTL-mapping or differential expression analysis. We present a solution and assess the performance of the proposed method using data from an F2 mouse intercross in a study of diabetes.