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Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)

Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits...

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Autores principales: Bhatta, Madhav, Gutierrez, Lucia, Cammarota, Lorena, Cardozo, Fernanda, Germán, Silvia, Gómez-Guerrero, Blanca, Pardo, María Fernanda, Lanaro, Valeria, Sayas, Mercedes, Castro, Ariel J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056970/
https://www.ncbi.nlm.nih.gov/pubmed/31974097
http://dx.doi.org/10.1534/g3.119.400968
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author Bhatta, Madhav
Gutierrez, Lucia
Cammarota, Lorena
Cardozo, Fernanda
Germán, Silvia
Gómez-Guerrero, Blanca
Pardo, María Fernanda
Lanaro, Valeria
Sayas, Mercedes
Castro, Ariel J.
author_facet Bhatta, Madhav
Gutierrez, Lucia
Cammarota, Lorena
Cardozo, Fernanda
Germán, Silvia
Gómez-Guerrero, Blanca
Pardo, María Fernanda
Lanaro, Valeria
Sayas, Mercedes
Castro, Ariel J.
author_sort Bhatta, Madhav
collection PubMed
description Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.
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spelling pubmed-70569702020-03-12 Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.) Bhatta, Madhav Gutierrez, Lucia Cammarota, Lorena Cardozo, Fernanda Germán, Silvia Gómez-Guerrero, Blanca Pardo, María Fernanda Lanaro, Valeria Sayas, Mercedes Castro, Ariel J. G3 (Bethesda) Genomic Prediction Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles. Genetics Society of America 2020-01-23 /pmc/articles/PMC7056970/ /pubmed/31974097 http://dx.doi.org/10.1534/g3.119.400968 Text en Copyright © 2020 Bhatta et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Genomic Prediction
Bhatta, Madhav
Gutierrez, Lucia
Cammarota, Lorena
Cardozo, Fernanda
Germán, Silvia
Gómez-Guerrero, Blanca
Pardo, María Fernanda
Lanaro, Valeria
Sayas, Mercedes
Castro, Ariel J.
Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)
title Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)
title_full Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)
title_fullStr Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)
title_full_unstemmed Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)
title_short Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)
title_sort multi-trait genomic prediction model increased the predictive ability for agronomic and malting quality traits in barley (hordeum vulgare l.)
topic Genomic Prediction
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056970/
https://www.ncbi.nlm.nih.gov/pubmed/31974097
http://dx.doi.org/10.1534/g3.119.400968
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