Abstract
The aim was to predict breeding values of non-phenotyped individuals based on a dataset prepared for the 13th QTL-MAS Workshop in Wageningen. Genetic co-variance matrices between animals were estimated with three methods: one using pedigree information only and two based on SNP markers from the first chromosome. Quadratic regression of breeding values, estimated separately in each of the five time points, was used to predict the breeding values in the 6th time point. Based on the comparison (true - estimated BV) it can be concluded that SNP based methods provided better estimates (accuracy between 0.75 and 0.80) than pedigree (0.65). Even though only SNPs from chromosome 1 were used it was still possible to achieve fairly high accuracies. Most likely this was due to the fact that chromosome 1 contained the QTLs with the largest effects.
Highlights
The aim was to predict breeding values of non-phenotyped individuals based on a dataset prepared for the 13th QTL-MAS Workshop in Wageningen
The aim of this paper was to predict breeding values of the 1000 non-phenotyped animals in the 6th time point, using three different strategies based on similarity between individuals due to common ancestry, and two methods based on marker similarity
The genetic variance estimated with the PB method was the closest to the true one while variance components obtained with SNPL method were slightly overestimated
Summary
The aim was to predict breeding values of non-phenotyped individuals based on a dataset prepared for the 13th QTL-MAS Workshop in Wageningen. The analysis was based on a dataset prepared for the 13th QTL-MAS Workshop in Wageningen [1]. The aim of this paper was to predict breeding values of the 1000 non-phenotyped animals in the 6th time point, using three different strategies based on similarity between individuals due to common ancestry (pedigree records), and two methods based on marker similarity. Due to software limitations [2] only one chromosome could be included in the analysis. The first chromosome was chosen based on preliminary results of QTL mapping, performed with a single QTL model with additive effects in the GRID QTL package [3]. The most significant QTLs, affecting the analysed trait in all five time points, were found on chromosome 1
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