Genomic Testing; A Modern Perspective in Bull Selection for Breed Improvement
DOI:
https://doi.org/10.14738/dafs.1405.12098Abstract
Genomic selection; A Modern perspective in Animal breeding which has revolutionized the Dairy and Beef industry. It has enabled us to predict the genetic merit of candidate animals early and accurately by means of Genome Wide testing technologies. This review article focuses on the key advancements in genomic selection methodologies, prediction models and their positive results like genetic gain, reduction of generation intervals productivity etc. Moreover, Applications of GS in milk yield, fat and protein content and climate resilience are discussed along with its role in crossbreeding programs and conservation of genetic diversity. Genomic selection still faces some challenges. Breeders need large and diverse reference populations for accurate predictions. The cost of genotyping is high. Some researchers also talk about some collateral damage of GS on the biodiversity of the gene pool. Future work should focus on using functional variants and whole-genome sequencing. Reproductive technologies like embryo selection can also improve results. Overall, genomic selection is a strong tool to improve productivity and sustainability
References
Alemu, S. W., Lopdell, T. J., Trevarton, A. J., Snell, R. G., Littlejohn, M. D., & Garrick, D. J., et al. (2025). Comparison of genomic prediction accuracies in dairy cattle lactation traits using five classes of functional variants versus generic SNP. Genetics Selection Evolution, 57, Article 20. https://doi.org/10.1186/s12711-025-00966-2
Boichard, D., Ducrocq, V., Croiseau, P., & Fritz, S. (2016). Genomic selection in domestic animals: principles, applications and perspectives. Comptes rendus biologies, 339(7-8), 274-277.
Chavan, S. P., Khan, K. D., Yadav, A., Gahlyan, R. K., Vohra, V., Alex, R., & Gowane, G. R. (2025). Exploring genomic signatures of selection for adaptation and resilience in nomadic Belahi cattle in the tropical climate. Molecular Biology Reports, 52(1), 676.
Cho, N., Park, B., Lee, Y., Kim, S., Choi, T., & Lee, D. (2020). Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic BLUP in Hanwoo beef cattle. BMC Genomic Data, 21, 43. https://doi.org/10.1186/s12863-020-00897-4
Cole, J. B., & VanRaden, P. M. (2018). Symposium review: Possibilities in an age of genomics. Journal of Dairy Science, 101(4), 3686–3701. https://doi.org/10.3168/jds.2017-13335
Curzon, A. Y., Ezra, E., Weller, J. I., Seroussi, E., Börner, V., Gershoni, M., … & Ron, M. (2024). Single-step genomic BLUP (ssGBLUP) effectively models small cattle populations: Lessons from the Israeli-Holstein Herdbook. BMC Genomics, 25, 1147. https://doi.org/10.1186/s12864-024-11074-8
Fritz, S., Capitan, A., Djari, A., Rodriguez, S. C., Barbat, A., Baur, A., … & Boichard, D. (2013). Detection of haplotypes associated with prenatal death in dairy cattle and identification of deleterious mutations in GART, SHBG and SLC37A2. PLoS ONE, 8(6), e65550.
García-Ruiz, A., Cole, J. B., VanRaden, P. M., Wiggans, G. R., Ruiz-López, F. J., & Van Tassell, C. P. (2016). Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proceedings of the National Academy of Sciences, 113(28), E3995–E4004. https://doi.org/10.1073/pnas.1519061113
Hayes, B. J., Bolormaa, S., Daetwyler, H. D., Prowell, S., Bowman, P. J., & Goddard, M. E. (2022). Functionally prioritised whole-genome sequence variants improve the accuracy of genomic prediction for heat tolerance in dairy cattle. Genetics Selection Evolution, 54, 37. https://doi.org/10.1186/s12711-022-00708-8
He, P., Yang, J., Suo, H., & Song, J. (2025). Effects of high hydrostatic pressure on the structural, physicochemical, and functional characteristics of buffalo milk casein. Food Research International, 201, 115619.
Ibtisham, F., Zhang, L., Xiao, M., An, L., Ramzan, M. B., Nawab, A., Zhao, Y., Li, G., & Xu, Y. M. (2017). Genomic selection and its application in animal breeding. In Thai Journal of Veterinary Medicine (Vol. 47, Issue 3, pp. 301–310). Chulalongkorn University Printing House. https://doi.org/10.56808/2985-1130.2838
Jeon, J. Y., Shin, Y., Mularo, A. J., Feng, X., & DeWoody, J. A. (2024). The integration of whole-genome resequencing and ecological niche modelling to conserve profiles of local adaptation. Diversity and Distributions, 30(6), e13847.
Kariuki, C. M., Brascamp, E. W., Komen, H., Kahi, A. K., & Van Arendonk, J. A. M. (2017). Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries. Journal of Dairy Science, 100(3), 2258–2268. Scholar Publishing
Li, W., Li, W., Song, Z., Gao, Z., Xie, K., Wang, Y., Wang, B., Hu, J., Zhang, Q., Ning, C., Wang, D., & Fan, X. (2024). Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits. Genes, 15(4), 454. https://doi.org/10.3390/genes15040454
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819–1829. https://doi.org/10.1093/genetics/157.4.1819
Meuwissen, T., Hayes, B., & Goddard, M. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6(1), 6–14.
Novika, R. G. H., Sari, A. N., Nurhidayati, S., Maulina, R., Maulida, L. F., Wahidah, N. J., … & Jevitt, C. M. (2025). Effect of β-casein A2 cow milk supplementation on physical growth, inflammation, growth-related hormones, and nutritional biomarkers in stunted children. Annals of Pediatric Endocrinology & Metabolism, 30(3), 119–126.
Ogunbawo, A. R., Mulim, H. A., Campos, G. S., Schinckel, A. P., … & van der Werf, J. (2024). Tailoring genomic selection for Bos taurus indicus: A comprehensive review of SNP arrays and reference genomes. Genes, 15(9), 1495. https://doi.org/10.3390/genes15091495
Pausch, H., MacLeod, I. M., Fries, R., Emmerling, R., Bowman, P. J., Daetwyler, H. D., … & Hayes, B. J. (2017). Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution. BMC Genomics, 18, 111. https://doi.org/10.1186/s12864-017-4263-8
Pérez-Rodríguez, P., & de Los Campos, G. (2022). Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package. Genetics, 222(1), iyac112.
Pryce, J. E., Gonzalez-Recio, O., Nieuwhof, G., Wales, W. J., Coffey, M. P., Hayes, B. J., & Goddard, M. E. (2015). Definition and implementation of a breeding value for feed saved (residual feed intake) and other traits in dairy cattle. Journal of Dairy Science, 98(1), 734–746. https://doi.org/10.3168/jds.2014-8510
Scott, B. A., Haile-Mariam, M., Cocks, B. G., & Pryce, J. E. (2021). How genomic selection has increased rates of genetic gain and inbreeding in the Australian national herd, genomic information nucleus, and bulls. Journal of Dairy Science, 104(11), 11832–11849.
Tade, B., & Melesse, A. (2024). A review on the application of genomic selection in the improvement of dairy cattle productivity. Ecological Genetics and Genomics, 31, 100257.
Tan, W. L. A., Reverter, A., Fortes, M. R. S., Porto-Neto, L. R., & Lehnert, S. A. (2023). Sequence-level genome-wide association studies reveal QTLs for semen quality and reproductive traits in bulls. BMC Genomics, 24, 1123. https://doi.org/10.1186/s12864-023-09678-2
Tian, T., Wang, S., Yang, S., Yang, Z., Liu, S., Wang, Y., … & Qin, F. (2023). Genome assembly and genetic dissection of a prominent drought-resistant maize germplasm. Nature Genetics, 55(3), 496–506.
van den Berg, I., Boichard, D., Guldbrandtsen, B., & Lund, M. S. (2016). Using sequence variants in linkage disequilibrium with causative mutations to improve across-breed prediction in dairy cattle: A simulation study. G3: Genes, Genomes, Genetics, 6(8), 2553–2561.
VanRaden, P. M. (2020). Symposium review: How to implement genomic selection. Journal of Dairy Science, 103(6), 5291–5301. https://doi.org/10.3168/jds.2019-17831 Scholar Publishing
Wiggans, G. R., Cole, J. B., Hubbard, S. M., & Sonstegard, T. S. (2017). Genomic selection in dairy cattle: The USDA experience. Annual Review of Animal Biosciences, 5, 309–327. https://doi.org/10.1146/annurev-animal-021815-111422
Yuan, C., Gillon, A., Gualdrón Duarte, J. L., Takeda, H., Coppieters, W., Georges, M., … & Druet, T. (2025). Evaluation of genomic selection models using whole genome sequence data and functional annotation in Belgian Blue cattle. Genetics Selection Evolution, 57, 10. https://doi.org/10.1186/s12711-025-00955-5
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Copyright (c) 2026 Muhammad Hassan Raza, Muhammad Ali Raza, Abdullah Basra, Humaira Haider, Laraib Zahra
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