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

  • Jan Lassen1, 2Email author,

    Affiliated with

    • Morten Kargo Sørensen1,

      Affiliated with

      • Per Madsen1 and

        Affiliated with

        • Vincent Ducrocq3

          Affiliated with

          Genetics Selection Evolution200739:353

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

          Received: 6 November 2006

          Accepted: 21 January 2007

          Published: 6 July 2007


          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.

          stochastic simulation multitrait model genetic evaluation

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          Authors’ Affiliations

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


          © INRA, EDP Sciences 2007