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Genetics Selection Evolution

Open Access

A comparison of bivariate and univariate QTL mapping in livestock populations

  • Peter Sørensen1Email author,
  • Mogens Sandø Lund1,
  • Bernt Guldbrandtsen1,
  • Just Jensen1 and
  • Daniel Sorensen1
Genetics Selection Evolution200335:605

Received: 22 April 2002

Accepted: 6 March 2003

Published: 15 November 2003


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.


multivariateQTL mappinglivestock

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Authors’ Affiliations

Danish Institute of Agricultural Sciences, Department of Animal Breeding and Genetics, Research Centre Foulum, Tjele, Denmark


© INRA, EDP Sciences 2003