Keywords: Deep Learning Genomic selection Prediction methods
Montesinos-López, O. A., Montesinos-López, A., Pérez-Rodríguez, P., Barrón-López, J. A., Martini, J. W. R., Fajardo-Flores, S. B., Gaytan-Lugo, L. S., Santana-Mancilla, P. C. & Crossa, J. (2021). A review of deep learning applications for genomic selection. BMC Genomics 22, 19. https://doi.org/10.1186/s12864-020-07319-x
2021 BMC Genomics
URL: https://doi.org/10.1186/s12864-020-07319-x
We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications.