Predicting hybrid performances for quality traits through genomic-assisted approaches in Central European wheat

dc.creatorGuozheng Liu
dc.creatorYusheng Zhao
dc.creatorGowda, M.
dc.creatorLongin, C.F.H.
dc.creatorReif, J.C.
dc.creatorMette, M.F.
dc.date2019-01-09T20:27:57Z
dc.date2019-01-09T20:27:57Z
dc.date2016
dc.date.accessioned2023-07-17T20:03:15Z
dc.date.available2023-07-17T20:03:15Z
dc.identifier1932-6203
dc.identifierhttps://hdl.handle.net/10883/19710
dc.identifier10.1371/journal.pone.0158635
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7511590
dc.descriptionBread-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.
dc.formatPDF
dc.languageEnglish
dc.publisherPublic Library of Science
dc.relationhttps://figshare.com/articles/dataset/Predicting_Hybrid_Performances_for_Quality_Traits_through_Genomic-Assisted_Approaches_in_Central_European_Wheat/3910530
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dc.rightsOpen Access
dc.source7
dc.source11
dc.sourcePLoS ONE
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectInbred Strains
dc.subjectQUANTITATIVE TRAIT LOCI
dc.subjectWHEAT
dc.subjectMARKER-ASSISTED SELECTION
dc.subjectPHENOTYPES
dc.subjectGLUTEN
dc.subjectINBRED LINES
dc.subjectSTARCHES
dc.titlePredicting hybrid performances for quality traits through genomic-assisted approaches in Central European wheat
dc.typeArticle
dc.coverageCentral Europe
dc.coverageUnited States


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