Animal Molecular Breeding 2024, Vol.14, No.1, 119-129 http://animalscipublisher.com/index.php/amb 120 potential drawbacks of MAS in livestock breeding. By synthesizing findings from multiple studies, this study will offer insights into the practical applications of MAS and its implications for the future of livestock breeding. This comprehensive analysis will inform future breeding strategies to optimize both productivity and genetic diversity. 1 Theoretical Framework 1.1 Genetic principles of MAS Marker-Assisted Selection (MAS) leverages genetic markers to enhance the selection process in breeding programs. Genetic markers are specific DNA sequences that are associated with particular traits, such as disease resistance or productivity. The fundamental principle behind MAS is the identification of quantitative trait loci (QTLs) that are linked to these markers. Once these associations are established, the markers can be used to predict the presence of desirable traits in breeding populations, thereby facilitating the selection of superior individuals (Osei et al., 2018; Eze, 2019). The genetic mechanisms underlying MAS involve the use of polymorphic DNA markers, such as restriction fragment length polymorphisms (RFLPs), microsatellites, and single nucleotide polymorphisms (SNPs). These markers are used in linkage analysis and association studies to identify QTLs that influence traits of interest. The efficiency of MAS depends on the strength of the linkage between the marker and the QTL, as well as the heritability of the trait (Feng et al., 2020; Shepelev et al., 2023). By integrating molecular genetic information with traditional phenotypic selection, MAS can significantly improve the accuracy and efficiency of breeding programs (Kumawat et al., 2020; Tiwari et al., 2022). 1.2 Technological advancements Recent technological innovations have greatly enhanced the effectiveness of MAS. Advances in genotyping technologies, such as high-throughput DNA sequencing and microarray analysis, have made it possible to screen large numbers of markers quickly and cost-effectively. These technologies have enabled the development of highly saturated genetic maps for various livestock species, providing a robust framework for MAS programs (Raina et al., 2020; Shepelev et al., 2023). The completion of genome sequencing projects for key livestock species has also been a major milestone. For example, the sequencing of the cattle, swine, and sheep genomes has facilitated the precise identification and mapping of genes associated with economically important traits. This genomic information is crucial for the detection, evaluation, and implementation phases of MAS, allowing for more accurate prediction of genetic merit and improved selection outcomes (Boopathi, 2020; Raina et al., 2020). Furthermore, the integration of MAS with other genomic selection techniques, such as genomic selection (GS) and genome editing, has opened new avenues for enhancing livestock productivity and genetic diversity. These combined approaches can accelerate the breeding process and enable the introduction of desirable traits with greater precision (Singh et al., 2022; Tiwari et al., 2022). 1.3 Comparison with other breeding techniques MAS offers several advantages over traditional selective breeding and genomic selection. Traditional selective breeding relies solely on phenotypic selection, which can be time-consuming and less accurate due to the influence of environmental factors and the complexity of genetic traits. In contrast, MAS uses genetic markers to directly target specific traits, thereby increasing the accuracy and speed of selection (Eze, 2019; Hasan et al., 2021). Compared to genomic selection, which uses genome-wide marker information to predict the genetic value of individuals, MAS focuses on specific markers linked to QTLs. While genomic selection can capture the effects of many small-effect loci across the genome, MAS is particularly effective for traits controlled by a few major QTLs. This makes MAS a valuable tool for improving traits that are difficult to measure, have low heritability, or are controlled by recessive alleles (Eze, 2019; Kumawat et al., 2020).
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