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Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass

KEY MESSAGE: Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. ABSTRACT: Genomic selection, which uses genome-wide sequence polymorphism data...

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Autores principales: Pembleton, Luke W., Inch, Courtney, Baillie, Rebecca C., Drayton, Michelle C., Thakur, Preeti, Ogaji, Yvonne O., Spangenberg, German C., Forster, John W., Daetwyler, Hans D., Cogan, Noel O. I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096624/
https://www.ncbi.nlm.nih.gov/pubmed/29860624
http://dx.doi.org/10.1007/s00122-018-3121-7
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author Pembleton, Luke W.
Inch, Courtney
Baillie, Rebecca C.
Drayton, Michelle C.
Thakur, Preeti
Ogaji, Yvonne O.
Spangenberg, German C.
Forster, John W.
Daetwyler, Hans D.
Cogan, Noel O. I.
author_facet Pembleton, Luke W.
Inch, Courtney
Baillie, Rebecca C.
Drayton, Michelle C.
Thakur, Preeti
Ogaji, Yvonne O.
Spangenberg, German C.
Forster, John W.
Daetwyler, Hans D.
Cogan, Noel O. I.
author_sort Pembleton, Luke W.
collection PubMed
description KEY MESSAGE: Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. ABSTRACT: Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00122-018-3121-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-60966242018-08-24 Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass Pembleton, Luke W. Inch, Courtney Baillie, Rebecca C. Drayton, Michelle C. Thakur, Preeti Ogaji, Yvonne O. Spangenberg, German C. Forster, John W. Daetwyler, Hans D. Cogan, Noel O. I. Theor Appl Genet Original Article KEY MESSAGE: Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. ABSTRACT: Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00122-018-3121-7) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2018-06-02 2018 /pmc/articles/PMC6096624/ /pubmed/29860624 http://dx.doi.org/10.1007/s00122-018-3121-7 Text en © The Author(s) 2018 Open AccessThis article is 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 you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Pembleton, Luke W.
Inch, Courtney
Baillie, Rebecca C.
Drayton, Michelle C.
Thakur, Preeti
Ogaji, Yvonne O.
Spangenberg, German C.
Forster, John W.
Daetwyler, Hans D.
Cogan, Noel O. I.
Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass
title Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass
title_full Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass
title_fullStr Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass
title_full_unstemmed Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass
title_short Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass
title_sort exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096624/
https://www.ncbi.nlm.nih.gov/pubmed/29860624
http://dx.doi.org/10.1007/s00122-018-3121-7
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