Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat

Handle URI:
http://hdl.handle.net/10754/617507
Title:
Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat
Authors:
Liu, Guozheng; Zhao, Yusheng; Gowda, Manje; Longin, C. Friedrich H.; Reif, Jochen C.; Mette, Michael F.
Abstract:
Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.
KAUST Department:
Center for Desert Agriculture
Citation:
Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat 2016, 11 (7):e0158635 PLOS ONE
Publisher:
Public Library of Science (PLoS)
Journal:
PLoS ONE
Issue Date:
6-Jul-2016
DOI:
10.1371/journal.pone.0158635
Type:
Article
ISSN:
1932-6203
Sponsors:
The wheat data set for this research was generated within the HYWHEAT project funded by Bundesministerium für Bildung und Forschung (Grant ID: FKZ0315945D). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Additional Links:
http://dx.plos.org/10.1371/journal.pone.0158635
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorLiu, Guozhengen
dc.contributor.authorZhao, Yushengen
dc.contributor.authorGowda, Manjeen
dc.contributor.authorLongin, C. Friedrich H.en
dc.contributor.authorReif, Jochen C.en
dc.contributor.authorMette, Michael F.en
dc.date.accessioned2016-07-26T09:14:55Z-
dc.date.available2016-07-26T09:14:55Z-
dc.date.issued2016-07-06-
dc.identifier.citationPredicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat 2016, 11 (7):e0158635 PLOS ONEen
dc.identifier.issn1932-6203-
dc.identifier.doi10.1371/journal.pone.0158635-
dc.identifier.urihttp://hdl.handle.net/10754/617507-
dc.description.abstractBread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.en
dc.description.sponsorshipThe wheat data set for this research was generated within the HYWHEAT project funded by Bundesministerium für Bildung und Forschung (Grant ID: FKZ0315945D). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.language.isoenen
dc.publisherPublic Library of Science (PLoS)en
dc.relation.urlhttp://dx.plos.org/10.1371/journal.pone.0158635en
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/4.0/en
dc.titlePredicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheaten
dc.typeArticleen
dc.contributor.departmentCenter for Desert Agricultureen
dc.identifier.journalPLoS ONEen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Stadt Seeland, Germanyen
dc.contributor.institutionState Plant Breeding Institute, University of Hohenheim, Stuttgart, Germanyen
dc.contributor.institutionInternational Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenyaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorMette, Michael F.en
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