International Journal of Molecular Veterinary Research, 2025, Vol.15, No.1, 13-21 http://animalscipublisher.com/index.php/ijmvr 16 4 Research Progress of Genomic Selection for Chicken Disease Resistance 4.1 Phenotypic prediction and genomic estimated breeding values (GEBV) for disease resistance Genomic selection enables the prediction of disease resistance in chickens by integrating phenotypic information (e.g., survival, virus shedding, and immune response) with genome-wide marker data to estimate genomic estimated breeding values (GEBVs). The GEBVs provide a numerical measure of the genetic value of an animal to resist disease, which allows breeders to select the birds with greater resistance traits even before pathogen exposure. For example, in the case of Newcastle disease, GEBVs have been used to select for higher survival and reduced virus shedding, demonstrating the practical value of genomic prediction to breeding programs (Zhou et al., 2024). 4.2 Case studies of genomic selection for resistance to different diseases There are several case studies that mention the application of genomic selection in various chicken diseases. For African indigenous chickens, genomic selection platforms were developed to be more resilient against Newcastle disease, focusing on survival time and virus shedding after natural challenge with virulent strains (Zhou et al., 2024). In avian leukosis, genome-wide association research in Chengkou mountain chickens identified important SNPs and candidate genes (e.g., PTPN13, TTF2, DLG2, CDH5) which are associated with resistance as molecular targets for selection (Li et al., 2024). Heterophil/lymphocyte ratio, a disease resistance characteristic, has also been examined to identify major SNPs and candidate genes to be incorporated into marker-assisted and genomic selection approaches (Zhu et al., 2019). 4.3 Application models combining gs with traditional breeding Integrating genomic selection with traditional breeding enhances the efficiency and sustainability of improvement for disease resistance. In practice, genomic selection is blended with traditional selection for performance traits such as egg yield and growth rate to guarantee that disease resistance is improved without reducing overall performance. For example, African breeding programs have integrated genomic selection for Newcastle disease resistance with productivity and local adaptation selection, according to the requirement for multi-trait improvement in real situations (Zhou et al., 2024). Combined approach is most useful for low- and middle-income nations where resources for vaccination as well as biosecurity are limited. 4.4 Research outcomes across multiple populations, breeds, and environments Research has demonstrated considerable genetic variation in resistance to diseases in various populations of chickens and different environments. Common local breeds such as Wuhua yellow chicken and Taihang chicken are usually highly resistant to diseases due to natural selection and little introgression of commercial strains (Weng et al., 2020; Zhang et al., 2024). Genomic studies have identified breed-specific loci for resistance and immune-related genes to support the value of local breeds as genetic resources for disease resistance breeding. Furthermore, studies carried out in different geographical locations and production systems demonstrate that genomic selection could be tailored to address typical local diseases and environmental stresses to enhance poultry populations' resilience and productivity worldwide (Mahdabi et al., 2021; Zhang et al., 2024). 5 Advantages of Genomic Selection in Chicken Disease Resistance Breeding 5.1 Improving selection accuracy and genetic gain Genomic selection (GS) employs genome-wide marker data to predict breeding values for resistance to disease with much greater accuracy than traditional methods. By the power of capturing major and minor genetic effects, GS allows more accurate selection of more resistant birds, even in traits of low to moderate heritability. This increased accuracy translates into increased genetic gain per generation, as demonstrated in Newcastle disease resistance breeding schemes, where GS maximized survival and reduced virus shedding while maximizing growth and egg production (Gul et al., 2022). 5.2 Shortening breeding cycles GS facilitates the selection of candidates for breeding at an early stage based on their genomic estimated breeding values (GEBVs) without requiring prolonged phenotypic evaluations and challenge tests. This accelerates the
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