Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as tra...

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Autores principales: Crossa, J, Pérez, P, Hickey, J, Burgueño, J, Ornella, L, Cerón-Rojas, J, Zhang, X, Dreisigacker, S, Babu, R, Li, Y, Bonnett, D, Mathews, K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3860161/
https://www.ncbi.nlm.nih.gov/pubmed/23572121
http://dx.doi.org/10.1038/hdy.2013.16
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author Crossa, J
Pérez, P
Hickey, J
Burgueño, J
Ornella, L
Cerón-Rojas, J
Zhang, X
Dreisigacker, S
Babu, R
Li, Y
Bonnett, D
Mathews, K
author_facet Crossa, J
Pérez, P
Hickey, J
Burgueño, J
Ornella, L
Cerón-Rojas, J
Zhang, X
Dreisigacker, S
Babu, R
Li, Y
Bonnett, D
Mathews, K
author_sort Crossa, J
collection PubMed
description Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.
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spelling pubmed-38601612014-01-01 Genomic prediction in CIMMYT maize and wheat breeding programs Crossa, J Pérez, P Hickey, J Burgueño, J Ornella, L Cerón-Rojas, J Zhang, X Dreisigacker, S Babu, R Li, Y Bonnett, D Mathews, K Heredity (Edinb) Original Article Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required. Nature Publishing Group 2014-01 2013-04-10 /pmc/articles/PMC3860161/ /pubmed/23572121 http://dx.doi.org/10.1038/hdy.2013.16 Text en Copyright © 2014 The Genetics Society http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Original Article
Crossa, J
Pérez, P
Hickey, J
Burgueño, J
Ornella, L
Cerón-Rojas, J
Zhang, X
Dreisigacker, S
Babu, R
Li, Y
Bonnett, D
Mathews, K
Genomic prediction in CIMMYT maize and wheat breeding programs
title Genomic prediction in CIMMYT maize and wheat breeding programs
title_full Genomic prediction in CIMMYT maize and wheat breeding programs
title_fullStr Genomic prediction in CIMMYT maize and wheat breeding programs
title_full_unstemmed Genomic prediction in CIMMYT maize and wheat breeding programs
title_short Genomic prediction in CIMMYT maize and wheat breeding programs
title_sort genomic prediction in cimmyt maize and wheat breeding programs
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3860161/
https://www.ncbi.nlm.nih.gov/pubmed/23572121
http://dx.doi.org/10.1038/hdy.2013.16
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