Computational Molecular Biology 2024, Vol.14, No.2, 54-63 http://bioscipublisher.com/index.php/cmb 61 7.3 Ethical and regulatory issues Ethical and regulatory issues also pose challenges in the application of genomic selection in breeding programs. The use of genomic data raises concerns about data privacy and the potential misuse of genetic information. Regulatory frameworks governing the use of genomic data in breeding programs vary across regions, which can complicate international collaborations and the sharing of genetic resources (Crossa et al., 2017). Moreover, the ethical implications of manipulating genetic material, particularly in animal breeding, require careful consideration to ensure that breeding practices are conducted responsibly and sustainably (He et al., 2023). The development of clear guidelines and regulations is essential to address these ethical and regulatory challenges and to ensure the responsible use of genomic selection technologies in breeding programs. Acknowledgments Thank you to the peer reviewers for their suggestions on this manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Alkimim E.R., Caixeta ET.., Sousa T.V., Resende M.D., Silva F.L., Sakiyama N.S., and Zambolim L., 2020, Selective efficiency of genome-wide selection in Coffea canephora breeding, Tree Genetics and Genomes, 16: 1-11. https://doi.org/10.1007/s11295-020-01433-3 Bauer A., Reetz T., and Léon J., 2006, Estimation of breeding values of inbred lines using best linear unbiased prediction (BLUP) and genetic similarities, Crop Science, 46: 2685-2691. https://doi.org/10.2135/CROPSCI2006.01.0019 Bhat J.A., Ali S., Salgotra R.K., Mir Z.A., Dutta S., Jadon V., Tyagi A., Mushtaq M., Jain N., Singh P.K., Singh G.P., and Prabhu K.V., 2016, Genomic selection in the era of next generation sequencing for complex traits in plant breeding, Frontiers in Genetics, 7: 221. https://doi.org/10.3389/fgene.2016.00221 Campos G., Hickey J., Pong-Wong R., Daetwyler H., and Calus M., 2013, Whole-genome regression and prediction methods applied to plant and animal breeding, Genetics, 193: 327-345. https://doi.org/10.1534/genetics.112.143313 Campos G.D., Vazquez A.I., Fernando R., Klimentidis Y.C., and Sorensen D., 2013, Prediction of complex human traits using the genomic best linear unbiased predictor, PLoS Genetics, 9(7): e1003608. https://doi.org/10.1371/journal.pgen.1003608 Crossa J., Pérez P., Campos G., Mahuku G., Dreisigacker S., and Magorokosho C., 2011, Genomic selection and prediction in plant breeding, Journal of Crop Improvement, 25: 239-261. https://doi.org/10.1080/15427528.2011.558767 Crossa J., Pérez-Rodríguez P., Cuevas J., Montesinos-López O., Jarquín D., Campos G., Burgueño J., González-Camacho J., Pérez-Elizalde S., Beyene Y., Dreisigacker S., Singh R., Zhang X., Gowda M., Roorkiwal M., Rutkoski J., and Varshney R., 2017, Genomic selection in plant breeding: methods models and perspectives, Trends in Plant Science, 22(11): 961-975. https://doi.org/10.1016/j.tplants.2017.08.011 Cuevas J., Crossa J., Montesinos-López O., Burgueño J., Pérez-Rodríguez P., and Campos G., 2016, Bayesian genomic prediction with genotype×environment interaction kernel models, G3: Genes|Genomes|Genetics, 7: 41-53. https://doi.org/10.1534/g3.116.035584 Desta Z.A., and Ortiz R., 2014, Genomic selection: genome-wide prediction in plant improvement, Trends in Plant Science, 19(9): 592-601. https://doi.org/10.1016/j.tplants.2014.05.006 Ganal M.W., Wieseke R., Luerssen H., Durstewitz G., Graner E.M., Plieske J., and Polley A., 2014, High-throughput SNP profiling of genetic resources in crop plants using genotyping arrays, Genomics of Plant Genetic Resources: Volume 1. Managing, 2014: 113-130. https://doi.org/10.1007/978-94-007-7572-5_6 Gorjanc G., Cleveland M.R., Houston R.D., and Hickey J.M., 2015, Potential of genotyping-by-sequencing for genomic selection in livestock populations, Genetics Selection Evolution : GSE, 47: 1-14. https://doi.org/10.1186/s12711-015-0102-z Granato Í., Cuevas J., Luna-Vázquez F., Crossa J., Montesinos-López O., Burgueño J., and Fritsche‐Neto R., 2018, BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models, G3: Genes|Genomes|Genetics, 8: 3039-3047. https://doi.org/10.1534/g3.118.200435 Grattapaglia D., Silva-Junior O.B., Kirst M., Lima B.M., Faria D.A., and Pappas G.J., 2011, High-throughput SNP genotyping in the highly heterozygous genome of Eucalyptus: assay success polymorphism and transferability across species, BMC Plant Biology, 11: 1-18. https://doi.org/10.1186/1471-2229-11-65
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