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Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

Disease resistance in plants is mostly quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs that are needed to select both major and minor genes for resistance. In this study, stripe rust (Puc...

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Autores principales: Merrick, Lance F., Burke, Adrienne B., Chen, Xianming, Carter, Arron H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377761/
https://www.ncbi.nlm.nih.gov/pubmed/34421966
http://dx.doi.org/10.3389/fpls.2021.713667
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author Merrick, Lance F.
Burke, Adrienne B.
Chen, Xianming
Carter, Arron H.
author_facet Merrick, Lance F.
Burke, Adrienne B.
Chen, Xianming
Carter, Arron H.
author_sort Merrick, Lance F.
collection PubMed
description Disease resistance in plants is mostly quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs that are needed to select both major and minor genes for resistance. In this study, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type (IT) and disease severity (SEV). We compared two types of training populations composed of 2,630 breeding lines (BLs) phenotyped in single-plot trials from 4 years (2016–2020) and 475 diversity panel (DP) lines from 4 years (2013–2016), both across two locations. We also compared the accuracy of models using four different major gene markers and genome-wide association study (GWAS) markers as fixed effects. The prediction models used 31,975 markers that are replicated 50 times using a 5-fold cross-validation. We then compared GS models using a marker-assisted selection (MAS) to compare the prediction accuracy of the markers alone and in combination. GS models had higher accuracies than MAS and reached an accuracy of 0.72 for disease SEV. The major gene and GWAS markers had only a small to nil increase in the prediction accuracy more than the base GS model, with the highest accuracy increase of 0.03 for the major markers and 0.06 for the GWAS markers. There was a statistical increase in the accuracy using the disease SEV trait, BLs, population type, and combining years. There was also a statistical increase in the accuracy using the major markers in the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased the accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.
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spelling pubmed-83777612021-08-21 Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance Merrick, Lance F. Burke, Adrienne B. Chen, Xianming Carter, Arron H. Front Plant Sci Plant Science Disease resistance in plants is mostly quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs that are needed to select both major and minor genes for resistance. In this study, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type (IT) and disease severity (SEV). We compared two types of training populations composed of 2,630 breeding lines (BLs) phenotyped in single-plot trials from 4 years (2016–2020) and 475 diversity panel (DP) lines from 4 years (2013–2016), both across two locations. We also compared the accuracy of models using four different major gene markers and genome-wide association study (GWAS) markers as fixed effects. The prediction models used 31,975 markers that are replicated 50 times using a 5-fold cross-validation. We then compared GS models using a marker-assisted selection (MAS) to compare the prediction accuracy of the markers alone and in combination. GS models had higher accuracies than MAS and reached an accuracy of 0.72 for disease SEV. The major gene and GWAS markers had only a small to nil increase in the prediction accuracy more than the base GS model, with the highest accuracy increase of 0.03 for the major markers and 0.06 for the GWAS markers. There was a statistical increase in the accuracy using the disease SEV trait, BLs, population type, and combining years. There was also a statistical increase in the accuracy using the major markers in the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased the accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes. Frontiers Media S.A. 2021-08-06 /pmc/articles/PMC8377761/ /pubmed/34421966 http://dx.doi.org/10.3389/fpls.2021.713667 Text en Copyright © 2021 Merrick, Burke, Chen and Carter. 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
Merrick, Lance F.
Burke, Adrienne B.
Chen, Xianming
Carter, Arron H.
Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance
title Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance
title_full Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance
title_fullStr Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance
title_full_unstemmed Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance
title_short Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance
title_sort breeding with major and minor genes: genomic selection for quantitative disease resistance
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377761/
https://www.ncbi.nlm.nih.gov/pubmed/34421966
http://dx.doi.org/10.3389/fpls.2021.713667
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