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Genetics Selection Evolution

, 35:605 | Cite as

A comparison of bivariate and univariate QTL mapping in livestock populations

  • Peter SørensenEmail author
  • Mogens Sandø Lund
  • Bernt Guldbrandtsen
  • Just Jensen
  • Daniel Sorensen
Open Access
Research

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.

Keywords

multivariate QTL mapping livestock 

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Copyright information

© INRA, EDP Sciences 2003

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Peter Sørensen
    • 1
    Email author
  • Mogens Sandø Lund
    • 1
  • Bernt Guldbrandtsen
    • 1
  • Just Jensen
    • 1
  • Daniel Sorensen
    • 1
  1. 1.Danish Institute of Agricultural Sciences, Department of Animal Breeding and GeneticsResearch Centre FoulumTjeleDenmark

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