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The apple REFPOP—a reference population for genomics-assisted breeding in apple
Breeding of apple is a long-term and costly process due to the time and space requirements for screening selection candidates. Genomics-assisted breeding utilizes genomic and phenotypic information to increase the selection efficiency in breeding programs, and measurements of phenotypes in different...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603508/ https://www.ncbi.nlm.nih.gov/pubmed/33328447 http://dx.doi.org/10.1038/s41438-020-00408-8 |
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author | Jung, Michaela Roth, Morgane Aranzana, Maria José Auwerkerken, Annemarie Bink, Marco Denancé, Caroline Dujak, Christian Durel, Charles-Eric Font i Forcada, Carolina Cantin, Celia M. Guerra, Walter Howard, Nicholas P. Keller, Beat Lewandowski, Mariusz Ordidge, Matthew Rymenants, Marijn Sanin, Nadia Studer, Bruno Zurawicz, Edward Laurens, François Patocchi, Andrea Muranty, Hélène |
author_facet | Jung, Michaela Roth, Morgane Aranzana, Maria José Auwerkerken, Annemarie Bink, Marco Denancé, Caroline Dujak, Christian Durel, Charles-Eric Font i Forcada, Carolina Cantin, Celia M. Guerra, Walter Howard, Nicholas P. Keller, Beat Lewandowski, Mariusz Ordidge, Matthew Rymenants, Marijn Sanin, Nadia Studer, Bruno Zurawicz, Edward Laurens, François Patocchi, Andrea Muranty, Hélène |
author_sort | Jung, Michaela |
collection | PubMed |
description | Breeding of apple is a long-term and costly process due to the time and space requirements for screening selection candidates. Genomics-assisted breeding utilizes genomic and phenotypic information to increase the selection efficiency in breeding programs, and measurements of phenotypes in different environments can facilitate the application of the approach under various climatic conditions. Here we present an apple reference population: the apple REFPOP, a large collection formed of 534 genotypes planted in six European countries, as a unique tool to accelerate apple breeding. The population consisted of 269 accessions and 265 progeny from 27 parental combinations, representing the diversity in cultivated apple and current European breeding material, respectively. A high-density genome-wide dataset of 303,239 SNPs was produced as a combined output of two SNP arrays of different densities using marker imputation with an imputation accuracy of 0.95. Based on the genotypic data, linkage disequilibrium was low and population structure was weak. Two well-studied phenological traits of horticultural importance were measured. We found marker–trait associations in several previously identified genomic regions and maximum predictive abilities of 0.57 and 0.75 for floral emergence and harvest date, respectively. With decreasing SNP density, the detection of significant marker–trait associations varied depending on trait architecture. Regardless of the trait, 10,000 SNPs sufficed to maximize genomic prediction ability. We confirm the suitability of the apple REFPOP design for genomics-assisted breeding, especially for breeding programs using related germplasm, and emphasize the advantages of a coordinated and multinational effort for customizing apple breeding methods in the genomics era. |
format | Online Article Text |
id | pubmed-7603508 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76035082020-11-02 The apple REFPOP—a reference population for genomics-assisted breeding in apple Jung, Michaela Roth, Morgane Aranzana, Maria José Auwerkerken, Annemarie Bink, Marco Denancé, Caroline Dujak, Christian Durel, Charles-Eric Font i Forcada, Carolina Cantin, Celia M. Guerra, Walter Howard, Nicholas P. Keller, Beat Lewandowski, Mariusz Ordidge, Matthew Rymenants, Marijn Sanin, Nadia Studer, Bruno Zurawicz, Edward Laurens, François Patocchi, Andrea Muranty, Hélène Hortic Res Article Breeding of apple is a long-term and costly process due to the time and space requirements for screening selection candidates. Genomics-assisted breeding utilizes genomic and phenotypic information to increase the selection efficiency in breeding programs, and measurements of phenotypes in different environments can facilitate the application of the approach under various climatic conditions. Here we present an apple reference population: the apple REFPOP, a large collection formed of 534 genotypes planted in six European countries, as a unique tool to accelerate apple breeding. The population consisted of 269 accessions and 265 progeny from 27 parental combinations, representing the diversity in cultivated apple and current European breeding material, respectively. A high-density genome-wide dataset of 303,239 SNPs was produced as a combined output of two SNP arrays of different densities using marker imputation with an imputation accuracy of 0.95. Based on the genotypic data, linkage disequilibrium was low and population structure was weak. Two well-studied phenological traits of horticultural importance were measured. We found marker–trait associations in several previously identified genomic regions and maximum predictive abilities of 0.57 and 0.75 for floral emergence and harvest date, respectively. With decreasing SNP density, the detection of significant marker–trait associations varied depending on trait architecture. Regardless of the trait, 10,000 SNPs sufficed to maximize genomic prediction ability. We confirm the suitability of the apple REFPOP design for genomics-assisted breeding, especially for breeding programs using related germplasm, and emphasize the advantages of a coordinated and multinational effort for customizing apple breeding methods in the genomics era. Nature Publishing Group UK 2020-11-01 /pmc/articles/PMC7603508/ /pubmed/33328447 http://dx.doi.org/10.1038/s41438-020-00408-8 Text en © The Author(s) 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Jung, Michaela Roth, Morgane Aranzana, Maria José Auwerkerken, Annemarie Bink, Marco Denancé, Caroline Dujak, Christian Durel, Charles-Eric Font i Forcada, Carolina Cantin, Celia M. Guerra, Walter Howard, Nicholas P. Keller, Beat Lewandowski, Mariusz Ordidge, Matthew Rymenants, Marijn Sanin, Nadia Studer, Bruno Zurawicz, Edward Laurens, François Patocchi, Andrea Muranty, Hélène The apple REFPOP—a reference population for genomics-assisted breeding in apple |
title | The apple REFPOP—a reference population for genomics-assisted breeding in apple |
title_full | The apple REFPOP—a reference population for genomics-assisted breeding in apple |
title_fullStr | The apple REFPOP—a reference population for genomics-assisted breeding in apple |
title_full_unstemmed | The apple REFPOP—a reference population for genomics-assisted breeding in apple |
title_short | The apple REFPOP—a reference population for genomics-assisted breeding in apple |
title_sort | apple refpop—a reference population for genomics-assisted breeding in apple |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603508/ https://www.ncbi.nlm.nih.gov/pubmed/33328447 http://dx.doi.org/10.1038/s41438-020-00408-8 |
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