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

- Genomic selection for boar taint and carcass traits in a commercial pig population.
- Calculate GEBVs using different GWS methods, like RR-BLUP and Bayesian LASSO.
- The Jack-knife cross-validation technique was proposed here.
- All computational codes used in statistical methodology are available.

This study aimed to compare two different Genome-Wide Selection (GWS) methods (Ridge Regression BLUP − RR-BLUP and Bayesian LASSO − 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.