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Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm

Sudden death syndrome (SDS), caused by Fusarium virguliforme, has spread to northern soybean growing regions in the US causing significant yield losses. The objectives of this study were to identify loci underlying variation in plant responses to SDS through association mapping (AM) and to assess pr...

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Autores principales: Bao, Yong, Kurle, James E., Anderson, Grace, Young, Nevin D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4434860/
https://www.ncbi.nlm.nih.gov/pubmed/25999779
http://dx.doi.org/10.1007/s11032-015-0324-3
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author Bao, Yong
Kurle, James E.
Anderson, Grace
Young, Nevin D.
author_facet Bao, Yong
Kurle, James E.
Anderson, Grace
Young, Nevin D.
author_sort Bao, Yong
collection PubMed
description Sudden death syndrome (SDS), caused by Fusarium virguliforme, has spread to northern soybean growing regions in the US causing significant yield losses. The objectives of this study were to identify loci underlying variation in plant responses to SDS through association mapping (AM) and to assess prediction accuracy of genomic selection (GS) in a panel of early maturing soybean germplasm. A set of 282 soybean breeding lines was selected from the University of Minnesota soybean breeding program and then genotyped using a genome-wide panel of 1536 single-nucleotide polymorphism markers. Four resistance traits, root lesion severity (RLS), foliar symptom severity (FSS), root retention (RR), and dry matter reduction (DMR), were evaluated using soil inoculation in the greenhouse. AM identified significant peaks in genomic regions of known SDS resistance quantitative trait loci cqSDS001, cqRfs4, and SDS11-2. Additionally, two novel loci, one on chromosome 3 and another on chromosome 18, were tentatively identified. A ninefold cross-validation scheme was used to assess the prediction accuracy of GS for SDS resistance. The prediction accuracy of single-trait GS (ST-GS) was 0.64 for RLS, but less than 0.30 for RR, DMR, and FSS. Compared to ST-GS, none of multi-trait GS (MT-GS) models significantly improved the prediction accuracy due to weak correlations between the four traits. This study suggests both AM and GS hold promise for implementation in genetic improvement of SDS resistance in existing soybean breeding programs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11032-015-0324-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-44348602015-05-19 Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm Bao, Yong Kurle, James E. Anderson, Grace Young, Nevin D. Mol Breed Article Sudden death syndrome (SDS), caused by Fusarium virguliforme, has spread to northern soybean growing regions in the US causing significant yield losses. The objectives of this study were to identify loci underlying variation in plant responses to SDS through association mapping (AM) and to assess prediction accuracy of genomic selection (GS) in a panel of early maturing soybean germplasm. A set of 282 soybean breeding lines was selected from the University of Minnesota soybean breeding program and then genotyped using a genome-wide panel of 1536 single-nucleotide polymorphism markers. Four resistance traits, root lesion severity (RLS), foliar symptom severity (FSS), root retention (RR), and dry matter reduction (DMR), were evaluated using soil inoculation in the greenhouse. AM identified significant peaks in genomic regions of known SDS resistance quantitative trait loci cqSDS001, cqRfs4, and SDS11-2. Additionally, two novel loci, one on chromosome 3 and another on chromosome 18, were tentatively identified. A ninefold cross-validation scheme was used to assess the prediction accuracy of GS for SDS resistance. The prediction accuracy of single-trait GS (ST-GS) was 0.64 for RLS, but less than 0.30 for RR, DMR, and FSS. Compared to ST-GS, none of multi-trait GS (MT-GS) models significantly improved the prediction accuracy due to weak correlations between the four traits. This study suggests both AM and GS hold promise for implementation in genetic improvement of SDS resistance in existing soybean breeding programs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11032-015-0324-3) contains supplementary material, which is available to authorized users. Springer Netherlands 2015-05-17 2015 /pmc/articles/PMC4434860/ /pubmed/25999779 http://dx.doi.org/10.1007/s11032-015-0324-3 Text en © The Author(s) 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.
spellingShingle Article
Bao, Yong
Kurle, James E.
Anderson, Grace
Young, Nevin D.
Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm
title Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm
title_full Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm
title_fullStr Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm
title_full_unstemmed Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm
title_short Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm
title_sort association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4434860/
https://www.ncbi.nlm.nih.gov/pubmed/25999779
http://dx.doi.org/10.1007/s11032-015-0324-3
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