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dc.rights.licenseReconocimiento 4.0 Internacional. (CC BY)-
dc.contributor.authorRiella, Venancioes
dc.contributor.authorLado, Bettinaes
dc.contributor.authorCondón, Federicoes
dc.contributor.authorPritsch, Claraes
dc.contributor.authorQuincke, Martínes
dc.contributor.authorKavanová, Monikaes
dc.contributor.authorGarcía, Richardes
dc.contributor.authorPereira, Fernandoes
dc.contributor.authorPérez, Noeliaes
dc.contributor.authorCastro, Arieles
dc.contributor.authorGutiérrez, Lucíaes
dc.contributor.authorGermán, Silviaes
dc.contributor.authorSilva, Paulaes
dc.date.accessioned2025-05-30T17:32:28Z-
dc.date.available2025-05-30T17:32:28Z-
dc.date.issued2025-05-22-
dc.identifier.urihttps://hdl.handle.net/20.500.12381/4048-
dc.description.abstractWheat yellow rust (YR), caused by Puccinia striiformis f. sp. tritici (Pst), is among the most devastating diseases affecting wheat worldwide. Since 2000, YR has expanded into regions where it was previously not considered an economically important disease. The deployment of YR-resistant cultivars remains the most effective and sustainable control strategy. We assembled a diverse mapping panel to (i) identify genomic regions associated with YR resistance using genome-wide association studies (GWAS), and (ii) assess the prediction accuracy of genomic prediction (GP) models for YR resistance. The panel of 366 wheat lines, including germplasm from INIA-Uruguay and other breeding programs, was phenotyped under artificial field inoculations in 2021 and 2022, and at the seedling stage using the same two Pst races used for field inoculations. GWAS identified eight genomic regions associated with field resistance, located on chromosomes 1B, 2B (three regions), 5B (two regions), 5D, and 6B, explaining 4.9 to 21.2% of the phenotypic variability. None of these regions were identified with seedling resistance to race Triticale2015b, the more virulent race, indicating that they confer adult-plant resistance. Moreover, these regions did not correspond to previously reported Yr genes. Two QTL on 2D and 3A were identified at the seedling stage to race Triticale2015a but did not contribute to field resistance. GP models achieved an average prediction ability of 0.64, highlighting their potential for accelerating the selection of resistant lines. These findings provide valuable insights into the genetic basis of YR and offer robust tools for enhancing YR resistance breeding efforts in wheat.es
dc.description.sponsorshipAgencia Nacional de Investigación e Innovación (ANII)es
dc.description.sponsorshipInstituto Nacional de Investigación Agropecuaria (INIA)es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.urihttps://hdl.handle.net/20.500.12381/4040es
dc.relation.urihttps://hdl.handle.net/20.500.12381/4029es
dc.relation.urihttps://hdl.handle.net/20.500.12381/4034es
dc.relation.urihttps://hdl.handle.net/20.500.12381/4033es
dc.relation.urihttps://hdl.handle.net/20.500.12381/4030es
dc.rightsAcceso abierto*
dc.sourceTheoretical and Applied Geneticses
dc.subjectPuccinia striiformises
dc.subjectGenome-wide association study (GWAS)es
dc.subjectQuantitative disease resistancees
dc.subjectGenomic prediction (GP)es
dc.titleWheat yellow rust in Uuruguay: understanding the genetic resistance in a panel of breeding and commercial germplasmes
dc.typeArtículoes
dc.subject.aniiCiencias Agrícolas-
dc.subject.aniiBiotecnología Agropecuaria-
dc.identifier.aniiFSA_1_2018_1_152918es
dc.type.versionAceptadoes
dc.anii.institucionresponsableInstituto Nacional de Investigación Agropecuaria (INIA)es
dc.anii.subjectcompleto//Ciencias Agrícolas/Biotecnología Agropecuaria/Biotecnología Agropecuariaes
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