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A joint learning approach for genomic prediction in polyploid grasses
Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strat...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304331/ https://www.ncbi.nlm.nih.gov/pubmed/35864135 http://dx.doi.org/10.1038/s41598-022-16417-7 |
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author | Aono, Alexandre Hild Ferreira, Rebecca Caroline Ulbricht Moraes, Aline da Costa Lima Lara, Letícia Aparecida de Castro Pimenta, Ricardo José Gonzaga Costa, Estela Araujo Pinto, Luciana Rossini Landell, Marcos Guimarães de Andrade Santos, Mateus Figueiredo Jank, Liana Barrios, Sanzio Carvalho Lima do Valle, Cacilda Borges Chiari, Lucimara Garcia, Antonio Augusto Franco Kuroshu, Reginaldo Massanobu Lorena, Ana Carolina Gorjanc, Gregor de Souza, Anete Pereira |
author_facet | Aono, Alexandre Hild Ferreira, Rebecca Caroline Ulbricht Moraes, Aline da Costa Lima Lara, Letícia Aparecida de Castro Pimenta, Ricardo José Gonzaga Costa, Estela Araujo Pinto, Luciana Rossini Landell, Marcos Guimarães de Andrade Santos, Mateus Figueiredo Jank, Liana Barrios, Sanzio Carvalho Lima do Valle, Cacilda Borges Chiari, Lucimara Garcia, Antonio Augusto Franco Kuroshu, Reginaldo Massanobu Lorena, Ana Carolina Gorjanc, Gregor de Souza, Anete Pereira |
author_sort | Aono, Alexandre Hild |
collection | PubMed |
description | Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains. |
format | Online Article Text |
id | pubmed-9304331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93043312022-07-23 A joint learning approach for genomic prediction in polyploid grasses Aono, Alexandre Hild Ferreira, Rebecca Caroline Ulbricht Moraes, Aline da Costa Lima Lara, Letícia Aparecida de Castro Pimenta, Ricardo José Gonzaga Costa, Estela Araujo Pinto, Luciana Rossini Landell, Marcos Guimarães de Andrade Santos, Mateus Figueiredo Jank, Liana Barrios, Sanzio Carvalho Lima do Valle, Cacilda Borges Chiari, Lucimara Garcia, Antonio Augusto Franco Kuroshu, Reginaldo Massanobu Lorena, Ana Carolina Gorjanc, Gregor de Souza, Anete Pereira Sci Rep Article Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains. Nature Publishing Group UK 2022-07-21 /pmc/articles/PMC9304331/ /pubmed/35864135 http://dx.doi.org/10.1038/s41598-022-16417-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Aono, Alexandre Hild Ferreira, Rebecca Caroline Ulbricht Moraes, Aline da Costa Lima Lara, Letícia Aparecida de Castro Pimenta, Ricardo José Gonzaga Costa, Estela Araujo Pinto, Luciana Rossini Landell, Marcos Guimarães de Andrade Santos, Mateus Figueiredo Jank, Liana Barrios, Sanzio Carvalho Lima do Valle, Cacilda Borges Chiari, Lucimara Garcia, Antonio Augusto Franco Kuroshu, Reginaldo Massanobu Lorena, Ana Carolina Gorjanc, Gregor de Souza, Anete Pereira A joint learning approach for genomic prediction in polyploid grasses |
title | A joint learning approach for genomic prediction in polyploid grasses |
title_full | A joint learning approach for genomic prediction in polyploid grasses |
title_fullStr | A joint learning approach for genomic prediction in polyploid grasses |
title_full_unstemmed | A joint learning approach for genomic prediction in polyploid grasses |
title_short | A joint learning approach for genomic prediction in polyploid grasses |
title_sort | joint learning approach for genomic prediction in polyploid grasses |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304331/ https://www.ncbi.nlm.nih.gov/pubmed/35864135 http://dx.doi.org/10.1038/s41598-022-16417-7 |
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