Persistency of accuracy of genomic breeding values for different simulated pig breeding programs in developing countries

Abstract

Genetic improvement of pigs in tropical developing countries has focused on imported exotic populations which have been subjected to intensive selection with attendant high population-wide linkage disequilibrium (LD). Presently, indigenous pig population with limited selection and low LD are being considered for improvement. Given that the infrastructure for genetic improvement using the conventional BLUP selection methods are lacking, a genome-wide selection (GS) program was proposed for developing countries. A simulation study was conducted to evaluate the option of using 60 K SNP panel and observed amount of LD in the exotic and indigenous pig populations. Several scenarios were evaluated including different size and structure of training and validation populations, different selection methods and long-term accuracy of GS in different population/breeding structures and traits. The training set included previously selected exotic population, unselected indigenous population and their crossbreds. Traits studied included number born alive (NBA), average daily gain (ADG) and back fat thickness (BFT). The ridge regression method was used to train the prediction model. The results showed that accuracies of genomic breeding values (GBVs) in the range of 0.30 (NBA) to 0.86 (BFT) in the validation population are expected if high density marker panels are utilized. The GS method improved accuracy of breeding values better than pedigree-based approach for traits with low heritability and in young animals with no performance data. Crossbred training population performed better than purebreds when validation was in populations with similar or a different structure as in the training set. Genome-wide selection holds promise for genetic improvement of pigs in the tropics.

Genomic selection in pigs: state of the art and perspectives

“…On the other hand, accuracy of GEBV is influenced by other factors defined in the genomic selection approach. The precision of the estimation of the prediction equations is affected by the size of the training population on which the marker effects are estimated and its closeness to the prediction population (Habier et al 2010;Akanno et al 2014). A drop in accuracy due to loss of pedigree relationships is expected after the first generation by limiting the value of genomic predictions and in close breeding population a regular re-training is recommended (Wolc et al 2011).…”

Genomic selection in a pig population including information from slaughtered full sibs of boars within a sib-testing program

“…According to this, when a trait of moderate heritability is under selection, the training population -that is, the group of pigs with both genotypes and phenotypes -should be closely related to the selection candidates and possibly include them. Finally, new pigs should be routinely added to the training population (Sonesson and Meuwissen, 2009), as the accuracy of genomic prediction strongly depends on how close the predicted population is related to training pigs (Habier et al, 2010;Akanno et al, 2014).…”

“…Finally, large training populations, increasing over time, in a genomic selection scenario (Akanno et al, 2014) should increase the accuracy of genomic evaluation also for traits with moderate heritability, especially when phenotypes are measured on the sibs (Tribout et al, 2013). Recently, it was suggested that the minimum size of the training population to have a sufficient accuracy of GEBV is of 2000 individuals, with better results with a multigenerational training population (Akanno et al, 2014).…”

“…Recently, it was suggested that the minimum size of the training population to have a sufficient accuracy of GEBV is of 2000 individuals, with better results with a multigenerational training population (Akanno et al, 2014). Finally, the implementation of a successful genomic selection program should balance the advantages derived from the enlargement of the training population and the extra costs involved in genotyping to assure the maximum benefit associated with selection (Akanno et al, 2014). A potential perspective could include the reorganization of the current swine breeding programs to exploit benefits associated with genomic selection (Dekkers et al, 2011;Akanno et al, 2014).…”

“…To overcome this issue, sib-testing programs have been implemented. Potential advantages of genomic selection in pig breeding have been reported by several simulation studies that modeled reproduction or performance traits collected on living animals (Lillehammer et al, 2011;Henryon et al, 2012;Tribout et al, 2012 andAkanno et al, 2014). To our knowledge, no studies have evaluated yet the integration of genomic selection in pig breeding programs based on full-sib testing of candidate boars.…”