Animal Molecular Breeding 2024, Vol.14, No.2, 154-164 http://animalscipublisher.com/index.php/amb 157 advanced genetic knowledge or technology. However, the effectiveness of phenotypic selection is often limited by the heritability of the traits in question and the generation interval, which can slow down genetic progress (Biermann et al., 2015). Despite its limitations, traditional breeding has been successful in achieving moderate improvements in carcass traits over time. For instance, the use of performance testing schemes that focus on phenotyped selection candidates has been shown to increase genetic gain for meat quality traits in local pig breeds (Biermann et al., 2015). However, the advent of molecular genetics and the identification of genetic markers have paved the way for more sophisticated breeding strategies that can potentially accelerate genetic improvement. 3.2 Marker-assisted selection (MAS) Marker-assisted selection (MAS) integrates molecular genetics with traditional breeding methods by using genetic markers linked to desirable traits. This approach allows for the selection of animals based on their genetic potential rather than solely on phenotypic traits. MAS has proven effective for traits controlled by a few genes with large effects, such as certain qualitative traits (Budhlakoti et al., 2022). However, its application to quantitative traits, which are influenced by many genes with small effects, has been more challenging. The efficiency of MAS in improving quantitative traits can be limited by the detectability of associations between marker loci and quantitative trait loci (QTL), as well as sampling errors in estimating the weighting coefficients in the selection index. Despite these challenges, MAS has been shown to increase selection efficiency when combined with phenotypic data. For example, a breeding strategy that includes both phenotypic and genetic marker information has resulted in a 20% increase in accuracy and selection response for meat quality traits in pigs (Biermann et al., 2015). 3.3 Genomic selection (GS) Genomic selection (GS) represents a significant advancement over MAS by utilizing genome-wide markers to predict the breeding values of individuals. Unlike MAS, which focuses on a limited number of markers, GS incorporates all available marker information into the prediction model, thereby capturing the effects of numerous small-effect QTL (Heffner et al., 2009). This comprehensive approach allows for more accurate predictions of genetic potential and can substantially accelerate the breeding cycle (Heffner et al., 2010). GS has been widely adopted in animal breeding programs due to its potential to improve selection accuracy, reduce the need for extensive phenotyping, and increase genetic gains (Budhlakoti et al., 2022). For instance, in pig breeding, GS has been shown to outperform MAS in terms of prediction accuracy and selection differentials (Arruda et al., 2016). The use of high-density SNP genotyping and advanced statistical models has further enhanced the effectiveness of GS, making it a powerful tool for improving complex traits such as carcass quality (Meuwissen et al., 2016; Merrick et al., 2022). 3.4 Comparison of different breeding strategies When comparing traditional breeding methods, MAS, and GS, it is evident that each strategy has its strengths and limitations. Traditional breeding methods are straightforward and cost-effective but may be slow in achieving genetic progress due to the reliance on phenotypic selection and longer generation intervals (Biermann et al., 2015). MAS offers a more targeted approach by using genetic markers, but its effectiveness is limited for complex traits controlled by many genes (Budhlakoti et al., 2022). In contrast, GS provides a more comprehensive and accurate method for predicting breeding values by incorporating genome-wide marker information. This approach has been shown to significantly accelerate genetic gains and improve selection accuracy compared to both traditional breeding and MAS (Heffner et al., 2009; Heffner et al., 2010; Arruda et al., 2016). However, the implementation of GS requires substantial investment in genotyping and computational resources, which may be a barrier for some breeding programs (Meuwissen et al., 2016; Merrick et al., 2022).
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