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.
Highlights
The full multitrait model led to the highest genetic progress for total merit, but from the result of 20 replicates this model was not significantly better than an approximate multitrait model with a time effect to correct for selection bias
A MACE approach [5] as well as the model proposed in this study [18] can use indirect, direct and combined information from yield and functional traits to improve the genetic evaluation for longevity
The present study shows that with biased models, one can have a distorted image of the accuracy of breeding values and genetic trends
Summary
The selection differential for the 10% best bulls increased for SCC (somatic cell count), functional longevity and female fertility when comparing the approximate model with the model used previously. In this approach single trait models were used for each trait in the breeding goal and EBV were weighted together afterwards. Applying this approximate multitrait method in laying hens gave similar results as shown by [8] It increased the selection differential for the 10% best animals for all traits in the breeding goal except precocity as compared to using a single trait model for all traits [1]. The method has been shown to be superior in terms of genetic progress compared to a single trait approach [11]
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