Abstract
Accuracy of genomic prediction was compared using three parametric and semi parametric methods, including BayesA, Bayesian LASSO and Reproducing kernel Hilbert spaces regression under various levels of heritability (0.15, 0.3 and 0.45), different number of markers (500, 750 and 1000) and generation intervals of validating set. A historical population of 1000 individuals with equal sex ratio was simulated for 100 generations at constant size. It followed by 100 extra generations of gradually reducing size down to 500 individuals in generation 200. Individuals of generation 200 were mated randomly for 10 more generations applying litter size of 5 to expand the historical generation. Finally, 50 males and 500 females chosen from generation 210 were randomly mated to generate 10 more generations of recent population. Individuals born in generation 211 considered as the training set while the validation set was composed of individuals either from generations 213, 215 or 217. The genome comprised one chromosome of 100 cM length carrying 50 QTLs. There was no significant difference between accuracy of investigated methods (p > 0.05) but among three methods, the highest mean accuracy (0.659) was observed for BayesA. By increasing the heritability, the average genomic accuracy increased from 0.53 to 0.75 (p < 0.05). The number of SNPs affected the accuracy and accuracies increased as number of SNPs increased; therefore, the highest accuracy was for the case number of SNPs=1000. With getting away from validating set, the accuracies decreased and the most severe decay observed in the case of low heritability. Decreasing the accuracy across generations affected by marker density but was independent from investigated methods.
Highlights
Animal breeding aims to improve economic efficiency of livestock species through selection under a changing cost and income scenario
Meuwissen et al (2001) suggested using genetic marker information in a statistical model of prediction of animal breeding values. They used three statistical models: a model assigning random effects to all available genetic markers (“genomic best linear unbiased prediction (BLUP)”), assuming that all markers effects are drawn from the same normal distribution, and two Bayesian models, where all (“BayesA”) or a subset (“BayesB”) of the random marker effects are drawn from distributions with different variances
Genomic selection provides a greater genetic progress in comparison with the traditional methods by increasing the accuracy of estimated breeding values and reducing generation intervals
Summary
Animal breeding aims to improve economic efficiency of livestock species through selection under a changing cost and income scenario. Meuwissen et al (2001) suggested using genetic marker information in a statistical model of prediction of animal breeding values. The estimated effects of the genetic markers are used to predict genomic breeding values for individuals of validating population (animals with marker information and without phenotypic information). They used three statistical models: a model assigning random effects to all available genetic markers (“genomic BLUP”), assuming that all markers effects are drawn from the same normal distribution, and two Bayesian models, where all (“BayesA”) or a subset (“BayesB”) of the random marker effects are drawn from distributions with different variances. Various types of these methods and additional methods have been subsequently suggested (Gianola, Campos, Hill, Manfredi, & Fernando, 2009). Gianola, Fernando, and Stella (2006) and Gianola and van Kaam (2008) have suggested a non-parametric treatment of genomic information by using Reproducing Kernel Hilbert Spaces (RKHS) regression, that subsequently have demonstrated with real data (González-Recio et al, 2008; González-Recio, Gianola, Rosa, Weigel, & Kranis, 2009)
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