Trait prediction through computational intelligence and machine learning applied to soybean breeding in shaded environments
DOI:
https://doi.org/10.33158/ASB.r255.v12.2026Palavras-chave:
Artificial neural networks, decision trees, boosting, random forests, bagging, importance of traits, nonlinear relationshipsResumo
This study aims to identify more relevant predictors traits, considering different prediction approaches in soybeans under different shading levels in the field, using methodologies based on artificial intelligence and machine learning. The experiments were carried out under different shading levels in a greenhouse and in the field, using sixteen cultivars. We have evaluated grain yield, which was used as a response trait, and 22 other attributes as explanatory traits. Three levels of shading were used to restrict photosynthetically active radiation (RPAR): 0%, 25%, and 48%. At full sun level (0% RPAR), the traits that presented better predictive performances using a multilayer perceptron were specific leaf area, plant height and number of pods. In the three levels of shading, the plant height trait exhibited the best performance for the radial base function network. Plant height showed the best predictive efficiency for grain yield at 25% and 48% RPAR, for all Machine Learning algorithms. Computational intelligence and machine learning methodologies have proven to be efficient in predicting soybean grain yield, regardless of shading level.
Downloads
Referências
Almeida Filho, J. E., Guimarães, J. F. R., Silva, F. F., Resende, M. D. V., Muñoz, P., Kirst, M., & Resende Jr, M. F. R. (2016). The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity, 117, 33-41. https://doi.org/10.1038/hdy.2016.23
Almeida Filho, J. E., Guimarães, J. F.R., Silva, F. F., Resende M. D. V., Muñoz, P., Kirst, M., & Resende Júnior, M. F. R. (2019). Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. G3, Genes Genomes Genetics, 9(8), 2739-2748. https://doi.org/10.1534/g3.119.201004
Amadeu, R. R., Ferrao, L. F. V., Oliveira, I. B., Benevenuto, J., Endelman, J. B., & Muñoz, P. R. (2020). Impact of dominance effects on autotetraploid genomic prediction. Crop Science. 60(2), 656-665. https://doi.org/10.1002/csc2.20075
Azevedo, C. F., Resende, M. D. V., Silva, F. F., Lopes, P. S., & Guimarães, S. E. F. (2013). Regressão via componentes independentes aplicada à seleção genômica para características de carcaça em suínos. Pesquisa Agropecuária Brasileira, 48(6), 619-626. https://doi.org/10.1590/S0100-204X2013000600007
Azevedo, C. F., Silva, F. F., Resende, M. D., Lopes, M. S., Duijvesteijn, N., Guimarães, S. E. F., Lopes, P. S., Kelly, M. J., Viana, J. M. S., Knol, E. F. (2014). Supervised independent component analysis as an alternative method for genomic selection in pigs. Journal of Animal Breeding and Genetics. 131(6), 452-461. https://doi.org/10.1111/jbg.12104
Azevedo, C.F., Nascimento, M., Silva, F.F., Resende, M.D.V., Lopes, P.S., Guimarães, S. E. F., Glória, L. S. (2015). Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs. Genetics and Molecular Research, 14(4), 12217-12227. http://dx.doi.org/10.4238/2015
Bengio, Y., & Grandvalet, Y. (2004). No Unbiased Estimator of the Variance of K-Fold Cross-Validation. Journal of Machine Learning Research, 5,1089-1105.
Carlborg, O., & Haley, C. S. (2004). Epistasis: too often neglected in complex trait studies? Nature Reviews Genetics, 5, 618-625. https://doi.org/10.1038/nrg1407
Costa, J. A., Azevedo, C. F., Nascimento, M., Silva, F. F., Resende, M. D. V., Nascimento, A. C. C. (2020). Genomic prediction with the additive-dominant model by dimensionality reduction methods. Pesquisa Agropecuária Brasileira, 55, e01713. https://doi.org/10.1590/S1678-3921.pab2020.v55.01713
Crossa, J., Pérez, P., Hickey, J., Burgueño, J., Ornella, L., Cerón-Rojas, J., Zangh, X., Dreisigacker, S., Babu, R., Li, Y., Bonnett, D., & Mathews, K. (2014). Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity, 112, 48-60. https://doi.org/10.1038/hdy.2013.16
Crossa, J., Pérez-Rodríguez, P., Cuevas, J., et al. (2017). Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci., 22(11),961-975. https://doi.org/10.1016/j.tplants.2017.08.011
Cruz, C. D. (2016). Genes Software – extended and integrated with the R, Matlab and Selegen. Acta Scientiarum - Agronomy. 38(4), 547-552. https://doi.org/10.4025/actasciagron.v38i4.32629
Dekkers, J. C. M., & Hospital, F. (2002). Agricultural populations. Nature Reviews Genetics, 3, 22-32. https://doi.org/10.1038/nrg701
Dias, K. O. G., Gezan, S. A., Guimarães, C. T., Nazarian, A., Silva, L. C., Parentoni, S. N., & et al. (2018). Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi‐environment trials. Heredity, 121(1), 24–37. https://doi.org/ 10.1038/s41437-018-0053-6
Falconer, D. S., & Mackay, T. F.C. (1996). Introduction to quantitative genetics. (4th ed.). Edinburgh: Longman Group Limited.
Gianola, D., Fernando, R. L., & Stella, A. (2006). Genomic assisted prediction of genetic value with semiparametric procedures. Genetics, 173(3), 1761-1776. https://doi.org/10.1534/genetics.105.049510
Guo, Z., Tucker, D. M., Basten, C. J., & et al. (2014). The impact of population structure on genomic prediction in stratified populations. Theoretical and Applied Genetics, 127, 749-762. https://doi.org/10.1007/s00122-013-2255-x
Habier, D., Fernando, R. L., & Dekkers, J. C. M. (2009). Genomic selection using low-density marker panels. Genetics, 182(1), 343-353. https://doi.org/10.1534/genetics.108. 100289
Holland, J. B. (2006). Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Science, 46, 642-654. https://doi.org/10.2135/cropsci2005.0191
Islam, M. S., Fang, D. D., Jenkins, J. N., Guo, J., McCarty, J. C., Jones, D. C. (2020). Evaluation of genomic selection methods for predicting fiber quality traits in Upland cotton. Molecular Genetics and Genomics, 295, 67-79. https://doi.org/10.1007/s00438-019-01599-z
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. (Vol. 112). New York: Springer.
Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22, 79-86. http://dx.doi.org/10.1214/aoms/1177729694
Lima, L. P., Azevedo, C. F., Resende, M. D. V., Silva, F. F., Viana, J. M. S., Oliveira, E. J. (2019). Triple categorical regression for genomic selection: application to cassava breeding. Scientia Agricola, 76(5), 368-375. https://doi.org/10.1590/1678-992x-2017-0369
Long, N., Gianola, D., Rosa, G. J., Weigel, K. A., & Avendano, S. (2007). Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. Journal of Animal Breeding and Genetics, 124(6), 377-389. https://doi.org/10.1111/j.1439-0388.2007.00694.x
Lyra, D. H., Galli, G., Alves, F. C., Granato, Í. S. C., Vidotti, M. S., Sousa M. B., Morosini, J. S., Crossa, J., & Fritsche-Neto, R. (2019). Modeling copy number variation in the genomic prediction of maize hybrids. Theoretical and Applied Genetics, 132(1), 273-288. https://doi.org/10.1007/ s00122-018-3215-2
Lyra, D. H., Granato, I. S. C., Morais, P. P. P., Alves, F. C., Santos, A. R. M., Yu, X., Guo, T., Yu, J., & Fritsche-Neto, R. (2018). Controlling population structure in the genomic prediction of tropical maize hybrids. Molecular Breeding, 38, article number 126. https://doi.org/10.1007/s11032-018-0882-2
Ma, Y., Reif, J. C., Jiang, Y., & et al. (2016). Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.). Molecular Breeding, 36, article numer 113, 1-10. https://doi.org/10.1007/s11032-016-0504-9
Macciotta, N. P. P., Gaspa, G., Steri, R., Pieramati, C., Carnier, P., & Dimauro, C. (2009). Pre-selection of most significant SNPS for the estimation of genomic breeding values. BMC Proceedings, 3, article number S14. https://doi.org/10.1186/1753-6561-3-s1-s14
Martini, J. W. R., Gao, N., Cardoso, D. F., Wimmer, V., Erbe, M., Cantet, R. J. C., & Simianer, H. (2017). Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE). BMC Bioinformatics, 18, article number 3. https://doi.org/10.1186/s12859-016-1439-1
Mathew, B., Léon, J., Sannemann, W., & Sillanpää, M. J. (2018). Detection of epistasis for flowering time using bayesian multilocus estimation in a Barley MAGIC population. Genetics, 208(2), 525-536. https://doi.org/10.1534/genetics.117.300546
McKinney, B. A., & Pajewski, N. M. (2012). Six degrees of epistasis: Statistical network models for GWAS. Frontiers in Genetics, 2, 109. https://doi.org/10.3389/fgene.2011.00109
McQuarrie, A. D. R., & Tsai, C. L. (1998). Regression and time series model selection. Singapore: World Scientific. DOI: https://doi.org/10.1142/3573
Melchinger, A. E., Utz, H. F., Piepho, H. P., Zeng, Z. B., & Schon, C. C. (2007). The role of epistasis in the manifestation of heterosis: a systems-oriented approach. Genetics, 177(3), 1815-1825. https://doi.org/10.1534/genetics.107.077537
Meuwissen, T. H., 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
Moser, G., Khatkar, M. S., Hayes, B. J., & Raadsma, H.W. (2010). Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genetics Selection Evolution, 42, article number 37. https://doi.org/10.1186/1297-9686-42-37
Muñoz, P. R., Resende, M. F. R., Gezan, S. A., Resende, M. D. V., Campos, G., Kirst, M., Huber, D., & Peter, G. F. (2014). Unraveling additive from nonadditive effects using genomic relationship matrices. Genetics, 198(4), 1759-1768. https://doi.org/10.1534/genetics.114.171322
Nishio, M., & Satoh, M. (2014). Including dominance efects in the genomic BLUP method for genomic evaluation. PLoS One, 9, e85792. https://doi.org/10.1371/journal.pone.0085792
Pérez-Rodríguez, P., Gianola, D., González-Camacho, J. M., Crossa, J., Manès, Y., & Dreisigacker, S. (2013). Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3, Genes Genomes Genetics, 2(12), 1595-1605. https://doi.org/10.1534/g3.112.003665
Piepho, H. P., & Gauch Jr., H. G. (2001). Marker pair selection for mapping quantitative trait loci. Genetics, 157(1), 433-444. https://doi.org/10.1093/genetics/157.1.433
R Core Team. (2019; 3). R: A language and environment for statistical computing. Available: https://www.r-project.org/
Resende Jr., M. F. R., Muñoz, P., Resende, M. D. V., Garrick, D. J., Fernando, R. L., Davis, J. M,, Jokela, E. J., Martin, T. A., Peter, G. F., & Kirst, M. (2012). Accuracy of Genomic Selection Methods in a Standard Data Set of Loblolly Pine (Pinus taeda L.). Genetics, 190(4), 1503-1510. https://doi.org/10.1534/genetics.111.137026
Resende, M. D. V., Lopes, O. S., Silva, R. L., & Pires, I. E. (2008). Seleção genômica ampla (GWS) e maximização da eficiência do melhoramento genético. Pesquisa Florestal Brasileira, 56, 63-77.
Resende, M. D. V., Silva, F. F., & Azevedo, C. F. (2014). Estatística matemática, biométrica e computacional: Modelos Mistos, Multivariados, Categóricos e Generalizados (REML/BLUP), Inferência Bayesiana, Regressão Aleatória, Seleção Genômica, QTL-GWAS, Estatística Espacial e Temporal, Competição Sobrevivência. Viçosa: Suprema.
Resende, M. D. V. (2007). Matemática e estatística na análise de experimentos e no melhoramento genético. Colombo: Embrapa Florestas.
Resende, M. D. V., Aguiar, A. M., Abad, J. I. M., Missiaggia, A. A., Sansaloni, C., Petroli, C.,
Grattapaglia, D., & Resende Júnior, M. F. R. (2010). Computação da Seleção Genômica
Ampla (GWS). Colombo: Embrapa Florestas.
Schaeffer, L. R. (2006). Strategy for applying genome-wide selection in dairy cattle. Journal of animal breeding and genetics, 123, 218-223. https://doi.org/10.1111/j.1439-0388.2006.00595.x
Santos, J. P. R., Vasconcellos, R. C. C., Pires, L. P. M., Balestre, M., & Von Pinho, R. G. (2016). Inclusion of dominance efects in the multivariate gblup model. PLoS One, 11(4), 1–21. https://doi.org/10.1371/ journal.pone.0152045
Sousa, T. V., Caixeta, E. T., Alkimim, E. R., Oliveira, A. C. B., Pereira, A. A., Sakiyma, N. S., Zambolim, L., & Resende, M. D. V. (2018). Early selection enabled by the implementation of genomic selection in Coffea arabica breeding. Frontiers in Plant Science, 9. https://doi.org/10.3389/fpls.2018.01934
Sousa, M., Galli, G., Lyra, D. H., Granato, I., Matias, F., Alves, F. C., & Fritsche-Netto, R. (2019). Increasing accuracy and reducing costs of genomic prediction by marker selection. Euphytica, 215(2), 18. https://doi.org/10.1007/ s10681-019-2339-z
Tayeh, N., Klein, A., & Paslier, M. C. & et al. (2015). Genomic prediction in pea: effect of marker density and training population size and composition on prediction accuracy. Frontiers in Plant Science, 6, 1–11. https://doi.org/10.3389/fpls.2015.00941
Usai, M. G., Goddard, M. E., & Hayes, B. J. (2009). LASSO with cross-validation for genomic selection. Genetics Research, 91(6), 427-436. https://doi.org/10.1017/S0016672309990334
Varona, L., Legarra, A., Toro, M. A., & Vitezica, Z. G. (2018). Non-additive effects in genomic selection. Frontiers in Genetics, 9, 78. https://doi.org/10.3389/fgene.2018.00078
Vazquez, A. I., Rosa, G. J. M., Weigel, K. A., Campos, G., Gianola, D., & Allison, D. B. (2010). Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins. Journal of Dairy Science, 93(12), 5942-5949. https://doi.org/10.3168/jds.2010-3335
Weigel, K. A., Campos, G., Vazquez, A. I., Rosa, G. J. M., Gianola, D., & Tassell, C. P. (2010).
Accuracy of direct genomic values derived from imputed single nucleotidepolymorphism genotypes in Jersey cattle. Journal of Dairy Science, 93(11), 5423-5435. https://doi.org/10.3168/jds.2010-3149
Weigel, K. A., Campos, G., González-Recio, O., Naya, H., Wu, X. L., Long, N., Rosa, G. J. M., & Gianola, D. (2009). Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. Journal of Dairy Science, 92(10), 5248-5257. https://doi.org/10.3168/jds.2009-2092
Wilkie, A. O. M. (1994). The molecular basis of genetic dominance. Journal of Medical Genetics, 31(2), 89-98. doi: 10.1136/jmg.31.2.89
Zhang, X., Pérez-Rodríguez, P., Semagn, K., Beyene, Y., Babu, R., López-Cruz, M. A., & et al. (2015). Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity, 114, 291-299. https://doi.org/10.1038/hdy.2014.99
Zhang, Z., Liu, J., Ding, X., Bijma, P., Koning, D-J., Zhang, Q. (2010). Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. PLoS One, 5, 1-8. https://doi.org/10.1371/journal.pone.0012648
Downloads
Publicado
Edição
Seção
Licença
Copyright (c) 2026 Antônio Carlos da Júnior, Weverton Gomes Costa , Amanda Guimaraes , Waldênia de Melo Moura , Leonardo Lopes Bhering , Cosme Damião Cruz, Renata Oliveira Batista , Jose Barbosa Santos, Wellington Ferreira Campos, Anderson Barbosa Evaristo
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Como Citar
