Seminars

We consider the problem of identifying the genetic loci (called quantitative trait loci, QTL) contributing to variation in a quantitative trait, with data on an experimental cross (such as with mice).  In the traditional approach to QTL mapping, one considers each genomic position, one at a time, and tests for association between genotype and the quantitative phenotype.  Great attention has been placed on the adjustment for multiple hypothesis tests.  The simultaneous consideration of multiple QTL can provide greater power,  can better separate linked QTL, and allows the investigation of interactions between loci.  The problem is best viewed as one of model selection.  We describe the key issues and propose a penalized likelihood approach for model selection.  Our approach provides an automated procedure that can enable biologists with limited statistical training to obtain a more complete understanding of the set of genetic loci contributing to variation in a quantitative trait.