Genomic selection for boar taint compounds and carcass traits in a commercial pig population

Abstract

This study aimed to compare two different Genome-Wide Selection (GWS) methods (Ridge Regression BLUP À RR-BLUP and Bayesian LASSO À BL) to predict the genomic estimated breeding values (GEBV) of four phenotypes, including two boar taint compounds, i.e., the concentrations of androstenone (andro) and skatole (ska), and two carcass traits, i.e., backfat thickness (fat) and loin depth (loin), which were measured in a commercial male pig line. Six hundred twenty-two boars were genotyped for 2,500 previously selected single nucleotide polymorphisms (SNPs). The accuracies of the GEBV using both methods were estimated based on Jack-knife cross-validation. The BL showed the best performance for the andro, ska and loin traits, which had accuracy values of 0.65, 0.58 and 0.33, respectively; for the fat trait, the RR-BLUP accuracy of 0.61 outperformed the BL accuracy of 0.56. Considering that BL was more accurate for the majority of the traits, this method is the most favoured for GWS under the conditions of this study. The most relevant SNPs for each trait were located in the chromosome regions that were previously indicated as QTL regions in other studies, i.e., SSC6 for andro and ska, SSC2 for fat, and SSC11, SSC15 and SSC17 for loin.

Genomic selection in pigs: state of the art and perspectives

“…These two components were considered both in GWAS studies (be e.g. in Duijvesteijn et al 2010) and more recently in GEBV prediction analyses (Azevedo et al 2014;de Campos et al 2015). In detail, de Campos et al (2015) reported GEBV accuracy values of 0.65 for androstenone and 0.58 for skatole levels.…”

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs

“…Badke et al (2014) estimated the accuracy of genomic breeding values (GEBV) for three traits (backfat thickness, number of days to 250 lb, and loin muscle area) in a Yorkshire pig population; de Campos et al (2015) predicted the GEBV for fat related traits (backfat thickness and loin depth) in a commercial pig population.…”

The optimal number of partial least squares components in genomic selection for pork pH

“…Besides the predictive analysis inherent to genomic selection, it is also interesting to identify the SNP markers with higher estimated effects (CAMPOS et al, 2015) because the PLS method can directly estimate these for each marker. These markers may reveal relevant chromosomal regions affecting each trait.…”