Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hardner, Craig M., Fikere, Mulusew, Gasic, Ksenija, da Silva Linge, Cassia, Worthington, Margaret, Byrne, David, Rawandoozi, Zena, Peace, Cameron
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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
_version_ 1784813180722085888
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
work_keys_str_mv AT hardnercraigm multienvironmentgenomicpredictionforsolublesolidscontentinpeachprunuspersica
AT fikeremulusew multienvironmentgenomicpredictionforsolublesolidscontentinpeachprunuspersica
AT gasicksenija multienvironmentgenomicpredictionforsolublesolidscontentinpeachprunuspersica
AT dasilvalingecassia multienvironmentgenomicpredictionforsolublesolidscontentinpeachprunuspersica
AT worthingtonmargaret multienvironmentgenomicpredictionforsolublesolidscontentinpeachprunuspersica
AT byrnedavid multienvironmentgenomicpredictionforsolublesolidscontentinpeachprunuspersica
AT rawandoozizena multienvironmentgenomicpredictionforsolublesolidscontentinpeachprunuspersica
AT peacecameron multienvironmentgenomicpredictionforsolublesolidscontentinpeachprunuspersica