Identifying the genetic loci responsible for variation in traits which are quantitative in nature (such as the yield from an agricultural crop or the number of abdominal bristles on a fruit fly) is a problem of great importance to biologists. The number and effects of such loci help us to understand the biochemical basis of these traits, and of their evolution in populations over time. Moreover, knowledge of these loci may aid in designing selection experiments to improve the traits.
We focus on data from a large experimental cross. The usual methods for analyzing such data use multiple tests of hypotheses. We feel the problem is best viewed as one of model selection. After a brief review of the major methods in this area, we discuss the use of model selection to identify quantitative trait loci. Forward selection using a BIC-type criterion is found to perform quite well. Simulation studies are used to compare the performance of the major approaches. In addition, we present the analysis of data from a real experiment.