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A review of deep learning applications for genomic selection
BACKGROUND: Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered i...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789712/ https://www.ncbi.nlm.nih.gov/pubmed/33407114 http://dx.doi.org/10.1186/s12864-020-07319-x |
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author | Montesinos-López, Osval Antonio Montesinos-López, Abelardo Pérez-Rodríguez, Paulino Barrón-López, José Alberto Martini, Johannes W. R. Fajardo-Flores, Silvia Berenice Gaytan-Lugo, Laura S. Santana-Mancilla, Pedro C. Crossa, José |
author_facet | Montesinos-López, Osval Antonio Montesinos-López, Abelardo Pérez-Rodríguez, Paulino Barrón-López, José Alberto Martini, Johannes W. R. Fajardo-Flores, Silvia Berenice Gaytan-Lugo, Laura S. Santana-Mancilla, Pedro C. Crossa, José |
author_sort | Montesinos-López, Osval Antonio |
collection | PubMed |
description | BACKGROUND: Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. MAIN BODY: 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. CONCLUSIONS: The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets. |
format | Online Article Text |
id | pubmed-7789712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77897122021-01-07 A review of deep learning applications for genomic selection Montesinos-López, Osval Antonio Montesinos-López, Abelardo Pérez-Rodríguez, Paulino Barrón-López, José Alberto Martini, Johannes W. R. Fajardo-Flores, Silvia Berenice Gaytan-Lugo, Laura S. Santana-Mancilla, Pedro C. Crossa, José BMC Genomics Review BACKGROUND: Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. MAIN BODY: 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. CONCLUSIONS: The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets. BioMed Central 2021-01-06 /pmc/articles/PMC7789712/ /pubmed/33407114 http://dx.doi.org/10.1186/s12864-020-07319-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Montesinos-López, Osval Antonio Montesinos-López, Abelardo Pérez-Rodríguez, Paulino Barrón-López, José Alberto Martini, Johannes W. R. Fajardo-Flores, Silvia Berenice Gaytan-Lugo, Laura S. Santana-Mancilla, Pedro C. Crossa, José A review of deep learning applications for genomic selection |
title | A review of deep learning applications for genomic selection |
title_full | A review of deep learning applications for genomic selection |
title_fullStr | A review of deep learning applications for genomic selection |
title_full_unstemmed | A review of deep learning applications for genomic selection |
title_short | A review of deep learning applications for genomic selection |
title_sort | review of deep learning applications for genomic selection |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789712/ https://www.ncbi.nlm.nih.gov/pubmed/33407114 http://dx.doi.org/10.1186/s12864-020-07319-x |
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