University of Wisconsin Department of Biostatistics and Medical Informatics

Traditional genetic mapping has largely focused on the identification of loci affecting one, or at most a few, complex traits. Microarrays allow for measurement of thousands of gene expression abundances, themselves complex traits, and a number of recent investigations have considered these measurements as phenotypes in mapping studies. Combining traditional quantitative trait loci (QTL) mapping methods with microarray data is a powerful approach with demonstrated utility in a number of recent biological investigations. These expression trait loci (ETL) studies are similar to traditional QTL studies, as a main goal is to identify the genomic locations to which the expression traits are linked. However, ETL studies probe thousands of expression transcripts; and as a result, standard multi-trait QTL mapping methods, designed to handle at most tens of traits, do not directly apply. One possible approach is to use single trait QTL mapping methods to analyze each transcript separately. This leads to an increased number of false discoveries, as multiple tests across transcripts are not adjusted for. Similarly, the repeated application, at each marker, of methods for identifying differentially expressed transcripts suffers from multiple tests across markers. Here, we demonstrate the deficiencies of these approaches and propose a mixture over markers (MOM) model which shares information across both markers and transcripts. The utility of all methods is evaluated using simulated data as well as data from an F2 mouse cross in a study of diabetes. Results from simulation studies indicate that the MOM model is best at controlling false discoveries, without sacrificing power. The MOM model is also the only one capable of finding two genome regions previously shown to be involved in diabetes.