Open Access

An approximate multitrait model for genetic evaluation in dairy cattle with a robust estimation of genetic trends (Open Access publication)

  • Jan Lassen1, 2Email author,
  • Morten Kargo Sørensen1,
  • Per Madsen1 and
  • Vincent Ducrocq3
Genetics Selection Evolution200739:353

DOI: 10.1186/1297-9686-39-4-353

Received: 6 November 2006

Accepted: 21 January 2007

Published: 6 July 2007

Abstract

In a stochastic simulation study of a dairy cattle population three multitrait models for estimation of genetic parameters and prediction of breeding values were compared. The first model was an approximate multitrait model using a two-step procedure. The first step was a single trait model for all traits. The solutions for fixed effects from these analyses were subtracted from the phenotypes. A multitrait model only containing an overall mean, an additive genetic and a residual term was applied on these preadjusted data. The second model was similar to the first model, but the multitrait model also contained a year effect. The third model was a full multitrait model. Genetic trends for total merit and for the individual traits in the breeding goal were compared for the three scenarios to rank the models. The full multitrait model gave the highest genetic response, but was not significantly better than the approximate multitrait model including a year effect. The inclusion of a year effect into the second step of the approximate multitrait model significantly improved the genetic trend for total merit. In this study, estimation of genetic parameters for breeding value estimation using models corresponding to the ones used for prediction of breeding values increased the accuracy on the breeding values and thereby the genetic progress.

Keywords

stochastic simulation multitrait model genetic evaluation

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Authors’ Affiliations

(1)
Department of Genetics and Biotechnology, Danish Institute of Agricultural Sciences
(2)
Department of Large Animal Sciences, The Royal Veterinary and Agricultural University
(3)
Station de génétique quantitative et appliquée, UR 337, INRA

Copyright

© INRA, EDP Sciences 2007

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