INRA, UMR1313 Génétique Animale et Biologie Intégrative, F-78350 Jouy-en-Josas, France; INRA, UR444 Laboratoire de génétique cellulaire, F-31320 CastanetTolosan, France; INRA, UMR1286; CNRS, UMR5226, Laboratoire PsyNuGen, F-33076 Bordeaux, France; INRA, UE967 Génétique expérimentale en productions animales, F-17700 Surgères, France; INRA, UAR2 Services déconcentrés d’appui à la recherche – Poit...

Quantitative genetics and quantitative trait locus (QTL) mapping have undergone a revolution in the last decade. Progress in the next decade promises to be at least as rapid, and strategies for fine-mapping QTLs and identifying underlying genes will be radically revised. In this Commentary we address several key issues: first, we revisit a perennial challenge--how to identify individual genes a...

We consider some practical statistical issues in QTL analysis where several crosses originate in multiple inbred parents. Our results show that ignoring background polygenic variation in different crosses may lead to biased interval mapping estimates of QTL effects or loss of efficiency. Threshold and power approximations are derived by extending earlier results based on the Ornstein-Uhlenbeck ...

Genetic mapping of quantitative trait loci (QTLs) is performed typically by using a parametric approach, based on the assumption that the phenotype follows a normal distribution. Many traits of interest, however, are not normally distributed. In this paper, we present a nonparametric approach to QTL mapping applicable to any phenotypic distribution. The method is based on a statistic ZW, which ...

Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, ...