Abstract
We evaluated the performance of GBLUP including dominance genetic effect (GBLUP-D) by estimating variances and predicting genetic merits in a computer simulation and 2 actual traits (T4 and T5) in pigs. In simulation data, GBLUP-D explained more than 50% of dominance genetic variance. Moreover, GBLUP-D yielded estimated total genetic effects over 1.2% more accurate than those yielded by GBLUP. In particular, when the dominance genetic variance was large, the accuracy could be substantially improved by increasing the number of markers. The dominance genetic variances in T4 and T5 accounted for 9.6% and 6.3% of the phenotypic variances, respectively. Estimates of such small dominance genetic variances contributed little to the improvement of the accuracies of estimated total genetic effects. In both simulation and pig data, there were nearly no differences in the estimates of additive genetic effects or their variance between GBLUP-D and GBLUP. Therefore, we conclude GBLUP-D is a feasible approach to improve genetic performance in crossbred populations with large dominance genetic variation and identify mating systems with good combining ability.
Highlights
Genomic selection refers to the use of genome-wide dense single nucleotide polymorphism (SNP) markers to predict breeding values and subsequently select individuals [1]
The genomic relationship coefficients are estimated with higher accuracy than when using pedigree information because genomic information can capture of Mendelian sampling across the genome
Our result indicates that genomic best linear unbiased prediction (GBLUP)-D is expected to improve performance of the crossbreds, in particular when degree of dominance is large
Summary
Genomic selection refers to the use of genome-wide dense single nucleotide polymorphism (SNP) markers to predict breeding values and subsequently select individuals [1]. One of them is the genomic best linear unbiased prediction (GBLUP), which uses genomic information in the form of a genomic relationship matrix that defines the additive genetic covariance between individuals [2,3]. It can be argued that such expansion is difficult because calculation becomes complicated and de-regressed estimated breeding values are used as phenotypes in most applications of genomic selection [9]. Genomic selection has renewed the interest in the prediction of dominance genetic effects. The dominance genetic variance accounted for 5.6% of the phenotypic variance by GBLUP including dominance genetic effect [11]. The present study aimed to evaluate the performance of GBLUP including dominance genetic effect by estimating variance components and predicting genetic effects for both simulation and actual pig data
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