Animal Molecular Breeding 2024, Vol.14, No.1, 95-105 http://animalscipublisher.com/index.php/amb 101 3.2 Genetic optimization in swine for enhanced meat quality In swine, genomic prediction has been applied to improve traits related to feed efficiency, growth, carcass, and meat quality. The accuracy of genomic predictions varies by trait and method, with traits having a large number of recorded and genotyped animals and high heritability showing the greatest accuracy. Methods such as BayesR and genomic BLUP (GBLUP) have been used to calculate GEBVs, with BayesR often providing higher accuracies for traits with known genes of moderate to large effect mutations. Genomic selection is beneficial for traits that are difficult to improve by conventional selection, such as tenderness and residual feed intake. The study of genomic predictions in beef cattle, including Bos taurus and Bos indicus, has shown that genomic selection can still be beneficial despite lower accuracies compared to dairy cattle (Bolormaa et al., 2013; Lopes et al., 2020). Lopes et al. (2020) presented descriptive statistics involving different traits such as Ribeye Area (REA), Back Fat (BF), Rump Fat (RF), and Warner-Bratzler Shear Force (WBSF) in the Nelore cattle breeding program (Table 1). Data for Ribeye Area and Back Fat were derived from cross-sectional images of the longissimus muscle between the 12th and 13th ribs. Rump Fat was measured at the intersection of the biceps femoris and the gluteus medius between the ilium and ischium. Warner-Bratzler Shear Force was measured after the meat samples had been stored for seven days. These data reflect the diversity in genetic and phenotypic information and their statistical characteristics in Nelore cattle. Table 1 Descriptive statistics for rib eye area, back fat thickness, rump fat and meat tenderness for genotyped in Nelore cattle (Lopes et al., 2020) Statistics REA BF RF WBSF Contemporary group 381 383 384 13 Number of animals 3675 3680 3 660 524 Number of sires 425 426 426 93 Number of dams 3118 3105 3 116 318 Mean 51.93 2.69 4.11 4.07 Standard deviation 12.94 1.91 2.23 1.41 CV 0.25 0.71 0.54 0.35 Note: REA: rib eye area; BF: back fat thickness; RF: rump fat; WBSF: Warner-Bratzler shear force 3.3 Conservation and improvement of rare livestock breeds Genomic selection also offers tools for the conservation and improvement of rare livestock breeds. By integrating genomic information, breeding programs can maintain genetic diversity while improving economically important traits. For example, in Gyr (Bos indicus) dairy cattle, genomic selection has been used to predict breeding values for milk yield, fat yield, protein yield, and age at first calving. The use of different SNP chips and the effect of imputed data on genomic prediction accuracy have been studied, showing that a reduced panel of markers can yield similar accuracies to high-density markers. This suggests that genomic selection can be effectively implemented in indicine breeds to accelerate genetic progress. Additionally, the integration of cow information into the reference population can increase the reliability of genomic predictions, which is particularly beneficial for breeds with limited reference populations (Boison et al., 2017). Boison et al. (2017) demonstrated the reliability estimates (R2PEV) for milk production, fat content, protein content, and age at first calving in dairy cattle using the genomic BLUP model with only bulls (TR1) and with both bulls and cows (TR2) as reference populations (Figure 4). The reliability of each trait was calculated from data obtained using chips with different genetic marker densities (such as 50K, 20Ki, 75Ki). The graph shows the reliability for all traits in both TR1 and TR2, and error bars indicate the standard deviation of reliability calculated per animal. This data reflects the variation in prediction accuracy under different testing conditions. In summary, genomic prediction is a powerful tool for enhancing breeding strategies in livestock, with applications ranging from improving productivity in dairy cattle to optimizing meat quality in swine and conserving rare breeds. The case studies demonstrate the versatility and impact of genomic selection across different livestock species and traits.
RkJQdWJsaXNoZXIy MjQ4ODY0NQ==