title: A comparison of bivariate and univariate QTL mapping in livestock populations.
abstract: This study presents a multivariate, variance component-based QTL mapping model implemented via restricted maximum likelihood (REML). The method was applied to investigate bivariate and univariate QTL mapping analyses, using simulated data. Specifically, we report results on the statistical power to detect a QTL and on the precision of parameter estimates using univariate and bivariate approaches. The model and methodology were also applied to study the effectiveness of partitioning the overall genetic correlation between two traits into a component due to many genes of small effect, and one due to the QTL. It is shown that when the QTL has a pleiotropic effect on two traits, a bivariate analysis leads to a higher statistical power of detecting the QTL and to a more precise estimate of the QTL's map position, in particular in the case when the QTL has a small effect on the trait. The increase in power is most marked in cases where the contributions of the QTL and of the polygenic components to the genetic correlation have opposite signs. The bivariate REML analysis can successfully partition the two components contributing to the genetic correlation between traits.
authors: Danish Institute of Agricultural Sciences, Department of Animal Breeding and Genetics, Research Centre Foulum, PO Box 50, 8830 Tjele, Denmark. pso@genetics.agrsci.dk
authors: Peter
authors: P
authors: Sørensen
authors: Mogens Sandø
authors: MS
authors: Lund
authors: Bernt
authors: B
authors: Guldbrandtsen
authors: Just
authors: J
authors: Jensen
authors: Daniel
authors: D
authors: Sorensen
mesh_terms: D000818:Animals; D000829:Animals, Domestic / Q000235:genetics*; D016013:Likelihood Functions; D015999:Multivariate Analysis; D040641:Quantitative Trait Loci