We consider the problem of identifying the genetic loci (called
quantitative trait loci (QTLs)) contributing to variation in a
quantitative trait, with data on an experimental cross. A large
number of different statistical approaches to this problem have
been described; most make use of multiple tests of hypotheses, and
many consider models allowing only a single QTL. We feel that the
problem is best viewed as one of model selection. We discuss the
use of model selection ideas to identify QTLs in experimental
crosses. We focus on a back-cross experiment, with strictly
additive QTLs, and concentrate on identifying QTLs, considering
estimation of their effects and precise locations of secondary
importance. We present the results of a simulation study to
compare the performance of the more prominent methods.
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