International Journal of Molecular Zoology 2024, Vol.14, No.5, 265-272 http://animalscipublisher.com/index.php/ijmz 269 reproductive efficiency, indicating that GS could be effectively used to select more fertile cows in extensive production systems (Jiménez et al., 2023). 5 Case Study 5.1 Overview of a specific beef cattle breed or population This case study focuses on the composite gene combination (CGC) breed, which is a composite of 50% Red Angus, 25% Charolais, and 25% Tarentaise. The CGC breed was selected for its unique genetic makeup, combining traits from these three breeds to enhance overall performance, including fertility traits. The study population consisted of 1 365 first parity females born between 2002 and 2011, with a pedigree file including 5 374 animals (Toghiani et al., 2017). 5.2 Methodology of genomic selection applied Genomic selection (GS) was applied to improve fertility traits in the CGC breed using a combination of univariate and multivariate classical quantitative models and univariate genomic approaches. The study utilized different density SNP chips (3 K to 50 K SNP) for genotyping 3 902 animals, with low-density arrays imputed to higher density using FImpute. Three Bayesian methods (BayesA, BayesB, and BayesCπ) were implemented to estimate heritabilities and genetic correlations for traits such as age of puberty (AOP), age at first calving (AOC), and heifer pregnancy status (HPS). 5.3 Results and analysis of fertility trait improvements The heritability estimates for the fertility traits varied, with AOP showing higher heritability (0.2) compared to AOC (0.03). The heritability of pregnancy status was 0.15 using univariate analysis and 0.09 using multivariate analysis. Genetic correlations between pregnancy status and other traits were low, with 0.08 for AOP and -0.10 for AOC. The accuracy of genomic prediction was higher for AOP than AOC, likely due to the higher heritability of AOP. The prediction accuracy for binary pregnancy status, measured using the area under the curve, increased by 27%-29% compared to a random classifier. 5.4 Implications for the beef industry The application of genomic selection in the CGC breed demonstrated significant potential for improving fertility traits, which are crucial for the profitability of the beef industry. The increased accuracy of genomic predictions and the identification of key genetic variants associated with fertility traits can lead to more efficient breeding programs (Porto-Neto et al., 2023). This, in turn, can enhance reproductive efficiency, reduce economic losses due to subfertility, and improve overall productivity in beef cattle herds (Fonseca et al., 2020; Keogh et al., 2020). The findings underscore the importance of incorporating genomic tools in breeding strategies to achieve sustainable genetic gains in fertility traits, ultimately benefiting the beef industry at large. 6 Future Perspectives 6.1 Potential advancements in genomic technologies The future of genomic selection in beef cattle fertility traits is promising, with several potential advancements on the horizon. One significant area of development is the use of whole-genome sequencing, which can provide a more comprehensive understanding of the genetic architecture underlying fertility traits. This approach allows for the identification of rare variants and the fine-mapping of quantitative trait loci (QTL) that are crucial for reproductive performance (Meuwissen et al., 2016). Additionally, advancements in high-throughput genotyping technologies will enable the cost-effective analysis of large populations, thereby increasing the accuracy of genomic predictions. The integration of multi-omics data, including transcriptomics and proteomics, can further elucidate the biological pathways involved in fertility, leading to more targeted breeding strategies (Tahir et al., 2021). 6.2 Integration of genomic selection with other breeding strategies Integrating genomic selection with traditional breeding methods and other advanced technologies can enhance the overall effectiveness of breeding programs (Yang, 2024). For instance, combining genomic selection with marker-assisted selection (MAS) can improve the accuracy of selecting animals with desirable fertility traits (Ma
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