Advertisement

An Effective Strategy for Trait Combinations in Multiple-Trait Genomic Selection

  • Zhixu Qiu
  • Yunjia Tang
  • Chuang Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10362)

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.

Keywords

Breeding Cross validation Genomic selection Multiple-trait analysis RR-BLUP Single-trait analysis 

Notes

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.

References

  1. 1.
    Desta, Z.A., Ortiz, R.: Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci. 19(9), 592–601 (2014)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Jia, Y., Jannink, J.L.: Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192(4), 1513–1522 (2012)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Calus, M.P., Veerkamp, R.F.: Accuracy of multi-trait genomic selection using different methods. Genet. Sel. Evol. 43(1), 26 (2011)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)zbMATHGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    De los Campos, G., Sorensen, D., Gianola, D.: Genomic Heritability: What Is It? Plos Genetics. 11(5) (2015)Google Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    Zhou, J., Chen, J., Ye, J.: MALSAR: Multi-task Learning via Structural Regularization (2012). http://www.public.asu.edu/~jye02/Software/MALSAR
  21. 21.
    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 CrossRefGoogle Scholar
  22. 22.
    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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Center of Bioinformatics, College of Life SciencesNorthwest A&F UniversityYanglingChina

Personalised recommendations