International Journal of Molecular Veterinary Research, 2025, Vol.15, No.1, 13-21 http://animalscipublisher.com/index.php/ijmvr 17 breeding program, with quicker accumulation of favorable alleles for disease resistance along with other traits. The ability to select birds at an early stage or even at the embryo stage decreases generation intervals and speeds up genetic improvement (Hellmich et al., 2020; Merrick et al., 2021). 5.3 Potential to simultaneously improve multiple traits One of the advantages of GS is its capability to select for multiple traits simultaneously, such as disease resistance, growth rate, and egg production. By using genome-wide markers, breeders can construct indices of selection that balance disease resistance against productivity traits and adaptation features, thereby enhancing flock overall improvement. This coordinated approach is especially vital in resource-limited environments, where productivity and resilience are critical towards sustaining poultry production sustainability (Zhang et al., 2018; Gul et al., 2022). 5.4 Promoting effective use and conservation of genetic resources GS allows for the identification and utilization of genetic diversity, such as local and native breeds with possible novel resistance alleles. Introduction of such materials into breeding schemes through GS preserves corresponding genetic diversity since it contributes to disease resilience. Apart from improving flock health, this technique also reduces the utilization of antibiotics and vaccines, resulting in more sustainable and resistant poultry systems (Zhou et al., 2024). 6 Challenges in the Implementation of Genomic Selection 6.1 High cost and accuracy requirements of phenotypic data collection In chicken disease-resistance breeding, obtaining quality phenotypic data is an essential requirement for genomic selection. Disease-resistant phenotypes are often reliant on controlled challenge tests, regular health monitoring, and diligent record-keeping by qualified staff, all of which entail significant human and capital inputs. Under lowand medium-input production systems, inadequate technical capacity and experimental facilities add to these limitations. Yet, accurate and accurate phenotypic information are the premise for building reliable reference populations and lending authenticity to genomic predictions (Olaniyan et al., 2024). 6.2 Cost and coverage limitations of genotypic data Genomic selection relies on extensive genotypic information from high-density SNP chips or whole-genome sequencing, but the cost of these approaches restricts their implementation in most large candidate populations. To minimize costs, low-density SNP panels are utilized in conjunction with genotype imputation by most breeding programs. However, this approach may lower the prediction accuracy via imputation error or unrepresentative reference populations, and consequently change the reliability of selection against disease resistance (Herry et al., 2020; Olaniyan et al., 2024). 6.3 Prediction accuracy and limitations of cross-population models The reliability of genomic prediction is very sensitive to the reference population size and genetic diversity. Models trained in a single chicken line or breed perform poorly in other populations. This is because patterns of linkage disequilibrium and allele frequencies vary between populations, reducing the transferability of genomic prediction models. Hence, for disease-resistance breeding, population-specific reference datasets must be constructed and cross-population validation and optimization must be conducted (Meuwissen et al., 2016; Misztal et al., 2020). 6.4 Preserving genetic diversity and avoiding inbreeding Even though genomic selection has the potential to accelerate genetic advancement for disease-resistance traits, strong selection has a tendency to expedite loss of genetic diversity and increase inbreeding, especially in closed or small populations. Loss of variability due to inbreeding can compromise the capacity of the population to resist fresh pathogens. Therefore, genomic selection practices need to involve following inbreeding coefficients, managing selection intensity, and keeping rare alleles to counteract genetic gain and longevity (Figure 2) (Mahdabi et al., 2021; Li et al., 2025b).
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