Animal Molecular Breeding 2024, Vol.14, No.4, 252-261 http://animalscipublisher.com/index.php/amb 254 maximum likelihood estimation, and Bayesian models, are employed to identify QTLs. Single QTL mapping methods focus on detecting one QTL at a time, while multiple QTL mapping combines multiple regression analysis with interval mapping to include all significant QTLs in the genetic model. This approach can be extended to handle data from multiple cross populations and for joint analysis of multiple traits (Singh and Singh, 2015). Fine mapping of QTL regions is often achieved using homozygous lines derived from near-isogenic lines (NILs) and intercross recombinant inbred lines. Additionally, QTL meta-analysis integrates results from different studies to refine the number of QTLs affecting a trait and reduce confidence intervals (Halladakeri et al., 2023). 3.2 Genome-wide association studies (GWAS) Genome-Wide Association Studies (GWAS) have become a preferred method for mapping quantitative traits in livestock due to the availability of dense SNP panels. GWAS involves scanning the genome to identify SNPs associated with traits of interest. Different statistical models, including single-marker and Bayesian multi-marker models, are used in GWAS to account for nonadditive genetic effects and genotype-by-environment interactions (Schmid and Bennewitz, 2017). Multibreed GWAS can enhance mapping precision and power by leveraging conserved linkage disequilibrium across breeds, as demonstrated in dairy cattle studies. This approach can detect more QTLs compared to within-breed GWAS, especially when multiple populations are combined (Berg et al., 2016). GWAS results can be further refined through conditional analyses and fine mapping to identify significant SNPs and candidate genes (Li et al., 2020). 3.3 Selection indices and breeding values Selection indices and breeding values are critical tools in livestock breeding, enabling the selection of individuals with desirable genetic traits. These indices combine information from multiple traits to provide a comprehensive measure of an individual's genetic potential. Breeding values are estimated using statistical models that incorporate phenotypic and genotypic data, allowing breeders to predict the genetic merit of animals for specific traits. The integration of genomic data into selection indices has improved the accuracy of breeding value predictions, particularly for complex traits like fertility in dairy cattle (Ma et al., 2019). Genomic selection has stabilized and even reversed unfavorable trends in traits such as dairy fertility, demonstrating its effectiveness in modern breeding programs. In summary, QTL mapping, GWAS, and selection indices are essential tools in quantitative genetics, each contributing uniquely to the understanding and improvement of complex traits in livestock. These methods enable the identification of genetic loci, the prediction of breeding values, and the enhancement of breeding strategies through genomic selection. 4 Applications of Quantitative Genetics in Livestock Breeding 4.1 Enhancing productivity traits: milk, meat, and egg yield Quantitative genetics plays a crucial role in enhancing productivity traits such as milk, meat, and egg yield in livestock. These traits are typically quantitative, meaning they are influenced by multiple genes and environmental factors, leading to continuous variability in the population (Núñez-Torres and Almeida-Secaira, 2022). Techniques such as genome-wide association studies (GWAS) and marker-assisted selection (MAS) have been employed to identify and select for genes associated with these economically important traits (Khalil and Gonda, 2020). By understanding the genetic basis of these traits, breeders can make informed decisions to improve yield and efficiency in livestock production (Khatib and Gonda, 2015). 4.2 Improving health and disease resistance Quantitative genetics also contributes significantly to improving health and disease resistance in livestock. Although individual disease traits often have low heritability, the genetic variance for disease prevalence can be substantial due to indirect genetic effects. This implies that selection against infectious diseases can be more effective than previously thought. By integrating quantitative genetics with epidemiology, breeders can develop strategies to enhance disease resistance, thereby reducing the impact of pathogens on livestock (Bijma, 2021). Additionally, genome editing techniques, such as the promotion of alleles by genome editing (PAGE), offer potential for improving health traits by targeting specific quantitative trait nucleotides (QTN) (Jenko et al., 2015).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==