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Genomic prediction is a method for predicting genomic breeding values (GEBVs) of quantitative traits for plants and animals, using nucleotide variation throughout the genome. This approach can increase genetic gains by accelerating the breeding cycle. Several factors affecting prediction accuracy should be well evaluated if breeders exploit genomic selection to its full potential. In this study, a panel of 170 natural rubber trees genotyped with 14,155 single nucleotide polymorphism (SNP) markers to investigate the effect of marker density and statistical models was examined for genomic prediction accuracy of latex yield in the dry (YD) and wet (YW) seasons. The performance of two different genomic prediction methods that differ with respect to assumptions regarding distribution of marker effects, including ridge regression-best linear unbiased prediction (RR–BLUP), and Bayesian LASSO (least absolute shrinkage and selection operator, BL) was evaluated. The predictive ability of the methods was evaluated using a cross-validation approach. RR-BLUP had higher predictive ability than BL for both YD and YW. This suggests that latex yield in the dry and wet seasons is controlled by many genes of equal contribution of all markers to the observed variation. Accuracy can be improved by increasing the optimal marker diversity. These findings represent a resource for plant breeders and contribute to the collective knowledge for genomic selection in rubber tree.
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Ballesta, P., Bush, D., Silva, F.F. and Mora, F., 2020, Genomic predictions using low-density SNP markers, pedigree and GWAS information: A case study with the non-model species Eucalyptus cladocalyx, Plants 9: 99.
Campos, G.D.L., Naya, H., Gianola, D., Crossa, J., Legarra, A., Manfredi., Weigel, K. and Cotes, J.M., 2009, Predicting quantitative traits with regression models for dense molecular markers and pedigree, Genetics 182: 375-385.
Cericola, F., Jahoor, A., Orabi, J., Andersen, J.R., Janss, L.L. and Jensen, J., 2017, Optimizing training population size and genotyping strategy for genomic prediction using association study results and pedigree information: A case of study in advanced wheat breeding lines, PLoS One 12(1): e0169606.
Chanroj, V., Rattanawong, R., Phunichai, T., Tangphatsornrung, S. and Ukoskit, K., 2017, Genome-wide association mapping of latex yield and girth in amazonian accessions of Hevea brasiliensis grown in a suboptimal climate zone, Genomics 109: 475-484.
Clark, S.A., Hickey, J.M. and van der Werf, J.H., 2011, Different models of genetic variation and their effect on genomic evaluation, Genet. Sel. Evol. 43: 18.
Cros, D., Mbo-Nkoulou, L., Bell, J.M., Oum, J., Masson, A., Soumahoro, M., Tran, D.M., Achour, A., Guen, V.L. and Demange, A.C., 2019, Within-family genomic selection in rubber tree (Hevea Brasiliensis) increases genetic gain for rubber production, Ind. Crops Prod. 138: 111464.
Daetwyler, H.D., Pong-Wong, R., Villanueva, B. and Woolliams, J.A., 2010, The impact of genetic architecture on genome-wide evalua tion methods, Genetics 185: 1021-1031.
Duangjit, J., Causse, M. and Sauvage, C., 2016, Efficiency of genomic selection for tomato fruit quality, Mol Breed. 36(3).
Gianola, D. and van Kaam, J.B.C.H.M., 2008, Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits, Genetics 178: 2298-2303.
Grattapaglia, D., 2017, Status and Perspectives of Genomic Selection in Forest Tree Breeding, pp. 199-249, In Varshney, R.K., Roorkiwal, M. and Sorrells, M.E. (Eds.), Genomic Selection for Crop Improvement, Springer International Publishing, New York.
Hayes, B.J., Bowman, P.J., Chamberlain, A.J. and Goddard, M.E., 2009, Invited review – Genomic selection in dairy cattle: Progress and Challenges, J. Dairy Sci. 92: 433-443.
Honarvar, M. and Rostami, M., 2013, Accuracy of genomic prediction using RR-BLUP and Bayesian LASSO, Eur. J. Exp. Biol. 3: 42-47.
Howard, R., Carriquiry, A.L. and Beavis, W.D., 2014, Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures, G3-Genes Genomes Genet. 4: 1027-1046.
Kwong, Q.B., Ong, A.L., Teh, C.K., Chew, F.T., Tammi, M., Mayes, S., Kulaveerasingam, H., Yeoh, S.H., Harikrishna, J.A. and Appleton, D.R., 2017, Genomic selection in commercial perennial crops: Applicability and improvement in oil palm (Elaeis guineensis Jacq.), Sci. Rep. 7: 2872/1-2872/9.
Macciotta, N.P., Gaspa, G., Carnier, P. and Dimauro, C., 2009, Pre-selection of most significant SNPS for the estimation of genomic breeding values, BMC Proceed. 3(Suppl. 1): S14.
Meuwissen, T.H.E., Hayes, B.J. and Goddard, M.E., 2001, Prediction of total genetic value using genome-wide dense marker maps, Genetics 157: 1819-1829.
Park, T. and Casella, G., 2008, The Bayesian Lasso, ASA 103: 681-686.
Resende, J.M., Mun, O.P., Resende, M., Garrick, D., Fernando, R., Dav’ s, J., Jokela, E., Martin, T., Peter, G. and Kirst, M., 2012, Accuracy of genomic selection methods in a standard data set of Loblolly Pine (Pinus taeda L.), Genetics 190: 1503-1510.
Solberg, T.R., Sonesson, A.K., Woolliams, J.A. and Meuwissen T.H.E., 2008, Genomic selection using different marker types and densities, J. Animal Sci. 86: 2447-2454.
Zhang, Z., Ding, X., Liu, J. and Zhang, Q., 2011, Accuracy of genomic prediction using low-density marker panels, J. Dairy Sci. 94: 3642-3650.