Abstract
Multiple-trait genomic selection (MTGS) is a recently developed method of genomic selection for satisfying the requirements of actual breeding, which usually aims to improve multiple traits simultaneously. Although many efforts have been made to develop MTGS prediction models, how to set the trait combination for the best performance of MTGS prediction models is still under exploration. In this study, we first classified the traits into two groups according to the single-trait genomic selection predictions: traits with a relatively high and low prediction performance. Then, we constructed three trait combinations (High & High, Low & Low, and High & Low) and evaluated their effects on the performance of a state-of-the-art MTGS prediction model using phenotypic and genotypic data from a maize diversity panel. Cross-validation experimental results indicate that single trait predictions could be used as reference for trait combinations in multi-trait genomic selection.
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References
Desta, Z.A., Ortiz, R.: Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci. 19(9), 592–601 (2014)
Jannink, J.L., Lorenz, A.J., Iwata, H.: Genomic selection in plant breeding: from theory to practice. Brief. Funct. Genomics. 9(2), 166–177 (2010)
Hayes, B.J., Bowman, P.J., Chamberlain, A.J., Goddard, M.E.: Invited review: genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 92(2), 433–443 (2009)
Schmidt, M., Kollers, S., Maasberg-Prelle, A., Grosser, J., Schinkel, B., Tomerius, A., Graner, A., Korzun, V.: Prediction of malting quality traits in barley based on genome-wide marker data to assess the potential of genomic selection. Theor. Appl. Genet. 129(2), 203–213 (2016)
Momen, M., Mehrgardi, A.A., Sheikhy, A., Esmailizadeh, A., Fozi, M.A., Kranis, A., Valente, B.D., Rosa, G.J., Gianola, D.: A predictive assessment of genetic correlations between traits in chickens using markers. Genet. Sel. Evol. 49(1), 16 (2017)
Bao, Y., Kurle, J.E., Anderson, G., Young, N.D.: Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm. Mol. Breed. 35(6), 128 (2015)
Jia, Y., Jannink, J.L.: Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192(4), 1513–1522 (2012)
Dos Santos, J.P., Vasconcellos, R.C., Pires, L.P., Balestre, M., Von Pinho, R.G.: Inclusion of dominance effects in the multivariate GBLUP model. PLoS One 11(4), e0152045 (2016)
Calus, M.P., Veerkamp, R.F.: Accuracy of multi-trait genomic selection using different methods. Genet. Sel. Evol. 43(1), 26 (2011)
He, D., Kuhn, D., Parida, L.: Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction. Bioinformatics 32(12), i37–i43 (2016)
Abernethy, J., Bach, F., Evgeniou, T., Vert, J.P.: A new approach to collaborative filtering: operator estimation with spectral regularization. J. Mach. Learn. Res. 10((Mar)), 803–826 (2009)
Schulthess, A.W., Wang, Y., Miedaner, T., Wilde, P., Reif, J.C., Zhao, Y.S.: Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes. Theor. Appl. Genet. 129(2), 273–287 (2016)
Montesinos-Lopez, O.A., Montesinos-Lopez, A., Crossa, J., Toledo, F.H., Perez-Hernandez, O., Eskridge, K.M., Rutkoski, J.: A genomic bayesian multi-trait and multi-environment model. G3 (Bethesda) 6(9), 2725–2744 (2016)
Jiang, J., Zhang, Q., Ma, L., Li, J., Wang, Z., Liu, J.F.: Joint prediction of multiple quantitative traits using a bayesian multivariate antedependence model. Heredity 115(1), 29–36 (2015)
Hayashi, T., Iwata, H.: A bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC Bioinf. 14(1), 34 (2013)
De los Campos, G., Sorensen, D., Gianola, D.: Genomic Heritability: What Is It? Plos Genetics. 11(5) (2015)
Kruijer, W., Boer, M.P., Malosetti, M., Flood, P.J., Engel, B., Kooke, R., Keurentjes, J.J., van Eeuwijk, F.A.: Marker-based estimation of heritability in immortal populations. Genetics 199(2), 379–398 (2015)
Stanton-Geddes, J., Yoder, J.B., Briskine, R., Young, N.D., Tiffin, P.: Estimating heritability using genomic data. Methods Ecol. Evol. 4(12), 1151–1158 (2013)
Bradbury, P.J., Zhang, Z., Kroon, D.E., Casstevens, T.M., Ramdoss, Y., Buckler, E.S.: TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23(19), 2633–2635 (2007)
Zhou, J., Chen, J., Ye, J.: MALSAR: Multi-task Learning via Structural Regularization (2012). http://www.public.asu.edu/~jye02/Software/MALSAR
Qiu, Z., Cheng, Q., Song, J., Tang, Y., Ma, C.: Application of machine learning-based classification to genomic selection and performance improvement. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2016. LNCS, vol. 9771, pp. 412–421. Springer, Cham (2016). doi:10.1007/978-3-319-42291-6_41
Ornella, L., Perez, P., Tapia, E., Gonzalez-Camacho, J.M., Burgueno, J., Zhang, X., Singh, S., Vicente, F.S., Bonnett, D., Dreisigacker, S., Singh, R., Long, N., Crossa, J.: Genomic-enabled Prediction with classification algorithms. Heredity (Edinb). 112(6), 616–626 (2014)
Funding
This work was supported by the National Natural Science Foundation of China (31570371), the Agricultural Science and Technology Innovation and Research Project of Shaanxi Province, China (2015NY011), and the Fund of Northwest A & F University.
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Qiu, Z., Tang, Y., Ma, C. (2017). An Effective Strategy for Trait Combinations in Multiple-Trait Genomic Selection. In: Huang, DS., Jo, KH., Figueroa-GarcÃa, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_21
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DOI: https://doi.org/10.1007/978-3-319-63312-1_21
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