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Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials
Genomic prediction has been established in breeding programs to predict the genotypic values of selection candidates without phenotypic data. First results in wheat showed that genomic predictions can also prove useful to select among material for which phenotypic data are available. In such a scena...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9072862/ https://www.ncbi.nlm.nih.gov/pubmed/35528936 http://dx.doi.org/10.3389/fpls.2022.735256 |
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author | Terraillon, Jérôme Frisch, Matthias Falke, K. Christin Jaiser, Heidi Spiller, Monika Cselényi, László Krumnacker, Kerstin Boxberger, Susanna Habekuß, Antje Kopahnke, Doris Serfling, Albrecht Ordon, Frank Zenke-Philippi, Carola |
author_facet | Terraillon, Jérôme Frisch, Matthias Falke, K. Christin Jaiser, Heidi Spiller, Monika Cselényi, László Krumnacker, Kerstin Boxberger, Susanna Habekuß, Antje Kopahnke, Doris Serfling, Albrecht Ordon, Frank Zenke-Philippi, Carola |
author_sort | Terraillon, Jérôme |
collection | PubMed |
description | Genomic prediction has been established in breeding programs to predict the genotypic values of selection candidates without phenotypic data. First results in wheat showed that genomic predictions can also prove useful to select among material for which phenotypic data are available. In such a scenario, the selection candidates are evaluated with low intensity in the field. Genome-wide effects are estimated from the field data and are then used to predict the genotypic values of the selection candidates. The objectives of our simulation study were to investigate the correlations r(y, g) between genomic predictions y and genotypic values g and to compare these with the correlations r(p, g) between phenotypic values p and genotypic values g. We used data from a yield trial of 250 barley lines to estimate variance components and genome-wide effects. These parameters were used as basis for simulations. The simulations included multiple crossing schemes, population sizes, and varying sizes of the components of the masking variance. The genotypic values g of the selection candidates were obtained by genetic simulations, the phenotypic values p by simulating evaluation in the field, and the genomic predictions y by RR-BLUP effect estimation from the phenotypic values. The correlations r(y, g) were greater than the correlations r(p, g) for all investigated scenarios. We conclude that using genomic predictions for selection among candidates tested with low intensity in the field can proof useful for increasing the efficiency of barley breeding programs. |
format | Online Article Text |
id | pubmed-9072862 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90728622022-05-07 Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials Terraillon, Jérôme Frisch, Matthias Falke, K. Christin Jaiser, Heidi Spiller, Monika Cselényi, László Krumnacker, Kerstin Boxberger, Susanna Habekuß, Antje Kopahnke, Doris Serfling, Albrecht Ordon, Frank Zenke-Philippi, Carola Front Plant Sci Plant Science Genomic prediction has been established in breeding programs to predict the genotypic values of selection candidates without phenotypic data. First results in wheat showed that genomic predictions can also prove useful to select among material for which phenotypic data are available. In such a scenario, the selection candidates are evaluated with low intensity in the field. Genome-wide effects are estimated from the field data and are then used to predict the genotypic values of the selection candidates. The objectives of our simulation study were to investigate the correlations r(y, g) between genomic predictions y and genotypic values g and to compare these with the correlations r(p, g) between phenotypic values p and genotypic values g. We used data from a yield trial of 250 barley lines to estimate variance components and genome-wide effects. These parameters were used as basis for simulations. The simulations included multiple crossing schemes, population sizes, and varying sizes of the components of the masking variance. The genotypic values g of the selection candidates were obtained by genetic simulations, the phenotypic values p by simulating evaluation in the field, and the genomic predictions y by RR-BLUP effect estimation from the phenotypic values. The correlations r(y, g) were greater than the correlations r(p, g) for all investigated scenarios. We conclude that using genomic predictions for selection among candidates tested with low intensity in the field can proof useful for increasing the efficiency of barley breeding programs. Frontiers Media S.A. 2022-04-22 /pmc/articles/PMC9072862/ /pubmed/35528936 http://dx.doi.org/10.3389/fpls.2022.735256 Text en Copyright © 2022 Terraillon, Frisch, Falke, Jaiser, Spiller, Cselényi, Krumnacker, Boxberger, Habekuß, Kopahnke, Serfling, Ordon and Zenke-Philippi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Terraillon, Jérôme Frisch, Matthias Falke, K. Christin Jaiser, Heidi Spiller, Monika Cselényi, László Krumnacker, Kerstin Boxberger, Susanna Habekuß, Antje Kopahnke, Doris Serfling, Albrecht Ordon, Frank Zenke-Philippi, Carola Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials |
title | Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials |
title_full | Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials |
title_fullStr | Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials |
title_full_unstemmed | Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials |
title_short | Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials |
title_sort | genomic prediction can provide precise estimates of the genotypic value of barley lines evaluated in unreplicated trials |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9072862/ https://www.ncbi.nlm.nih.gov/pubmed/35528936 http://dx.doi.org/10.3389/fpls.2022.735256 |
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