International Journal of Molecular Veterinary Research, 2025, Vol.15, No.1, 13-21 http://animalscipublisher.com/index.php/ijmvr 19 7 Integration and Comparison of Disease Resistance Breeding Strategies in Chickens 7.1 Analysis of consistency and differences among studies The majority of research concurs that genetic selection, particularly with genomic tools, can increase resistance to diseases such as Newcastle disease, Mycoplasma gallisepticum, and avian leukosis. The genes, markers, and pathways pinpointed, however, tend to vary by environment, disease, and breed. For example, MMP7 was found to be a key gene for Mycoplasma resistance in some Chinese breeds, while MHC genes or novel loci are emphasized for viral resistance in African and commercial breeds by other research (Broom and Kogut, 2019; Gul et al., 2022). Such inconsistencies reflect the polygenic and context-dependent aspect of disease resistance. 7.2 Comparative effectiveness of genomic selection methods High-density genotyping (for example, SNP chips, whole-genome sequencing) and advanced statistical models (GBLUP, Bayesian) progressively improve the accuracy of selection for disease resistance. Affordable panels of SNPs have been successfully applied in African indigenous chickens, achieving cost vs. predictive capability equilibrium (Zhou et al., 2024). Integration of GWAS-transcriptomics improves marker discovery even further, yet reduced reference populations and low heritability can reduce precision (Touko et al., 2021). 7.3 Integration of genotyping, phenotyping, and statistical models Effective breeding programs nowadays incorporate high-throughput genotyping, precise phenotypic data, and well-supported statistical models. These can be combined to enable multi-trait selection (productivity, adaptation, disease resistance) as well as locally adapted, resistant chicken line development (Looi et al., 2018; Gul et al., 2022). 7.4 Practical implications for optimizing breeding strategies Enhanced strategies must incorporate genomic and traditional selection, consider farmer preferences (for example, productivity, appearance), and include differing reference populations. Utilization of natural resistance local breeds and model validation in different environments are key to improvement that is enduring. Greater cooperation and data sharing are required for maximum impact from these approaches (Mpenda et al., 2019; Touko et al., 2021). 8 Concluding Remarks Evidence in the last decade indicates that genomic selection (GS) successfully improves the accuracy and efficiency of disease resistance breeding in chickens. Research has proved that GS can accurately predict genomic estimated breeding values (GEBVs) for several infectious disease traits, such as resistance to Newcastle disease, Marek's disease, and infectious bronchitis, between populations and environments. GS has also facilitated the inclusion of sophisticated polygenic information, reflecting both main and minor gene effects not always reflected in conventional selection methods. Moreover, combining GS with conventional phenotypic and pedigree-based selection has been demonstrated to have synergistic effects, furthering overall genetic gains and accelerating disease-resistant line development. The major advantage of GS is its ability to enable early, precise, and multi-trait concurrent selection, with a reduction in generation intervals and improved accuracy of selection. Through the application of genome-wide marker information, GS allows breeders to make selections for better individuals before phenotypic expression of disease resistance, particularly for low-heritability or late-expressing traits. Besides, GS ensures the intelligent utilization and conservation of genetic resources to enhance long-term breeding sustainability and genetic diversity and disease resistance in commercial poultry populations. This review synthesizes existing evidence on GS application for disease resistance in chickens, focusing on methodological advancement, utilitarian approaches, and key challenges for application. Results reported herein offer theoretical guidelines as well as utilitarian points of reference for breeding program planning, genomic prediction model calibration, and application of GS with other state-of-the-art technologies. Overall, syntheses of
RkJQdWJsaXNoZXIy MjQ4ODYzNA==