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Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses
KEY MESSAGE: Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers. ABSTRACT: Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263548/ https://www.ncbi.nlm.nih.gov/pubmed/33768281 http://dx.doi.org/10.1007/s00122-021-03815-0 |
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author | Gonzalez, Maria Y. Zhao, Yusheng Jiang, Yong Stein, Nils Habekuss, Antje Reif, Jochen C. Schulthess, Albert W. |
author_facet | Gonzalez, Maria Y. Zhao, Yusheng Jiang, Yong Stein, Nils Habekuss, Antje Reif, Jochen C. Schulthess, Albert W. |
author_sort | Gonzalez, Maria Y. |
collection | PubMed |
description | KEY MESSAGE: Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers. ABSTRACT: Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-021-03815-0. |
format | Online Article Text |
id | pubmed-8263548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-82635482021-07-20 Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses Gonzalez, Maria Y. Zhao, Yusheng Jiang, Yong Stein, Nils Habekuss, Antje Reif, Jochen C. Schulthess, Albert W. Theor Appl Genet Original Article KEY MESSAGE: Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers. ABSTRACT: Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-021-03815-0. Springer Berlin Heidelberg 2021-03-25 2021 /pmc/articles/PMC8263548/ /pubmed/33768281 http://dx.doi.org/10.1007/s00122-021-03815-0 Text en © The Author(s) 2021 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 | Original Article Gonzalez, Maria Y. Zhao, Yusheng Jiang, Yong Stein, Nils Habekuss, Antje Reif, Jochen C. Schulthess, Albert W. Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses |
title | Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses |
title_full | Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses |
title_fullStr | Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses |
title_full_unstemmed | Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses |
title_short | Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses |
title_sort | genomic prediction models trained with historical records enable populating the german ex situ genebank bio-digital resource center of barley (hordeum sp.) with information on resistances to soilborne barley mosaic viruses |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263548/ https://www.ncbi.nlm.nih.gov/pubmed/33768281 http://dx.doi.org/10.1007/s00122-021-03815-0 |
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