CMB_2024v14n4

Computational Molecular Biology 2024, Vol.14, No.4, 145-154 http://bioscipublisher.com/index.php/cmb 153 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 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(9): 3039-3047. https://doi.org/10.1534/g3.118.200435 Heslot N., Jannink J., and Sorrells M., 2015, Perspectives for genomic selection applications and research in plants, Crop Science, 55: 1-12. https://doi.org/10.2135/CROPSCI2014.03.0249 Iheshiulor O.O.M., Woolliams J.A., Svendsen M., Solberg T., and Meuwissen T.H.E., 2017, Simultaneous fitting of genomic-blup and bayes-c components in a genomic prediction model, Genetics Selection Evolution : GSE, 49: 1-13. https://doi.org/10.1186/s12711-017-0339-9 Jeon D., Kang Y., Lee S., Choi S., Sung Y., Lee T.H., and Kim C., 2023, Digitalizing breeding in plants: a new trend of next-generation breeding based on genomic prediction, Frontiers in Plant Science, 14: 1092584. https://doi.org/10.3389/fpls.2023.1092584 Jighly A., Hayden M., and Daetwyler H., 2021, Integrating genomic selection with a genotype plus genotype x environment (GGE) model improves prediction accuracy and computational efficiency, Plant Cell and Environment, 44(10): 3459-3470. https://doi.org/10.1111/pce.14145 Kemper K., 2021, Insights into complex traits from human genetics, Journal of Animal Science, 2021: 99. https://doi.org/10.1093/jas/skab235.052 Koning D.J., 2016, Meuwissen et al. on genomic selection, Genetics, 203(1): 5-7. https://doi.org/10.1534/genetics.116.189795 Krishnappa G., Savadi S., Tyagi B.S., Singh S.K., Masthigowda M., Kumar S., Mishra C.N., Khan H.M., Krishnappa G., Govindareddy U., Singh G., and Singh G.P., 2021, Integrated genomic selection for rapid improvement of crops, Genomics, 113(3): 1070-1086. https://doi.org/10.1016/j.ygeno.2021.02.007 Larkin D.L., Lozada D.N., and Mason R.E., 2019, Genomic selection—considerations for successful implementation in wheat breeding programs, Agronomy, 9(9): 479. https://doi.org/10.3390/AGRONOMY9090479 Li H.D., Su G.S., Jiang L., and Bao Z.M., 2017, An efficient unified model for genome-wide association studies and genomic selection, Genetics Selection Evolution : GSE, 49: 1-8. https://doi.org/10.1186/s12711-017-0338-x Li J.Q., and Jiong F., 2024, Genomic diversity and evolutionary mechanisms in the Oryza genus: a comparative analysis, Genomics and Applied Biology, 15(1): 54-63. https://doi.org/10.5376/gab.2024.15.0008 Liu X.G., Wang H.W., Hu X.J., Li K., Liu Z.F., Wu Y.J., and Huang C.L., 2019, Improving genomic selection with quantitative trait loci and nonadditive effects revealed by empirical evidence in maize, Frontiers in Plant Science, 10: 1129. https://doi.org/10.3389/fpls.2019.01129 Liu X.G., Wang H.W., Wang H., Guo Z.F., Xu X.J., Liu J.C., Wang S.H., Li W.X., Zou C., Prasanna B.M., Olsen M.S., Huang C.L., and Xu Y.B., 2018, Factors affecting genomic selection revealed by empirical evidence in maize, The Crop Journal, 6(4): 341-352. https://doi.org/10.1016/J.CJ.2018.03.005 Merrick L.F., Herr A.W., Sandhu K.S., Lozada D.N., and Carter A.H., 2022, Optimizing plant breeding programs for genomic selection, Agronomy, 12(3): 714. https://doi.org/10.20944/preprints202202.0048.v1 Meuwissen T., Hayes B., and Goddard M., 2016, Genomic selection: a paradigm shift in animal breeding, Animal Frontiers, 6(1): 6-14. https://doi.org/10.2527/AF.2016-0002 Misztal I., Aguilar I., Lourenco D., Ma L., Steibel J.P., and Toro M., 2021, Emerging issues in genomic selection, Journal of Animal Science, 99(6): skab092. https://doi.org/10.1093/jas/skab092 Pérez-Rodríguez P., Montesinos-López O., Montesinos‐López A., and Crossa J., 2020, Bayesian regularized quantile regression: A robust alternative for genome-based prediction of skewed data, Crop Journal, 8: 713-722. https://doi.org/10.1016/j.cj.2020.04.009 Rabier C.E., Barre P., Asp T., Charmet G., and Mangin B., 2016, On the accuracy of genomic selection, PLoS ONE, 11(6): e0156086. https://doi.org/10.1371/journal.pone.0156086 Rice B.R., and Lipka A.E., 2021, Diversifying maize genomic selection models, Molecular Breeding,41(5): 33. https://doi.org/10.1007/s11032-021-01221-4 Unêda-Trevisoli S.H., Silva F.M., and Mauro A.O., 2017, Marker-assisted selection and genomic selection, Soybean Breeding, 2017: 275-291. https://doi.org/10.1007/978-3-319-57433-2_14 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.2019-17684

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