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Multi-environment genomic prediction for soluble solids content in peach (Prunus persica)
Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly du...
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/PMC9583944/ https://www.ncbi.nlm.nih.gov/pubmed/36275520 http://dx.doi.org/10.3389/fpls.2022.960449 |
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author | Hardner, Craig M. Fikere, Mulusew Gasic, Ksenija da Silva Linge, Cassia Worthington, Margaret Byrne, David Rawandoozi, Zena Peace, Cameron |
author_facet | Hardner, Craig M. Fikere, Mulusew Gasic, Ksenija da Silva Linge, Cassia Worthington, Margaret Byrne, David Rawandoozi, Zena Peace, Cameron |
author_sort | Hardner, Craig M. |
collection | PubMed |
description | Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual’s narrow-sense and broad-sense heritability for SSC were high (0.57–0.73 and 0.66–0.80, respectively), with 19–32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement. |
format | Online Article Text |
id | pubmed-9583944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95839442022-10-21 Multi-environment genomic prediction for soluble solids content in peach (Prunus persica) Hardner, Craig M. Fikere, Mulusew Gasic, Ksenija da Silva Linge, Cassia Worthington, Margaret Byrne, David Rawandoozi, Zena Peace, Cameron Front Plant Sci Plant Science Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual’s narrow-sense and broad-sense heritability for SSC were high (0.57–0.73 and 0.66–0.80, respectively), with 19–32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement. Frontiers Media S.A. 2022-10-06 /pmc/articles/PMC9583944/ /pubmed/36275520 http://dx.doi.org/10.3389/fpls.2022.960449 Text en Copyright © 2022 Hardner, Fikere, Gasic, da Silva Linge, Worthington, Byrne, Rawandoozi and Peace. 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 Hardner, Craig M. Fikere, Mulusew Gasic, Ksenija da Silva Linge, Cassia Worthington, Margaret Byrne, David Rawandoozi, Zena Peace, Cameron Multi-environment genomic prediction for soluble solids content in peach (Prunus persica) |
title | Multi-environment genomic prediction for soluble solids content in peach (Prunus persica) |
title_full | Multi-environment genomic prediction for soluble solids content in peach (Prunus persica) |
title_fullStr | Multi-environment genomic prediction for soluble solids content in peach (Prunus persica) |
title_full_unstemmed | Multi-environment genomic prediction for soluble solids content in peach (Prunus persica) |
title_short | Multi-environment genomic prediction for soluble solids content in peach (Prunus persica) |
title_sort | multi-environment genomic prediction for soluble solids content in peach (prunus persica) |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9583944/ https://www.ncbi.nlm.nih.gov/pubmed/36275520 http://dx.doi.org/10.3389/fpls.2022.960449 |
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