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Resumen
Background: In breeding programs, the selection of cultivars with the highest yield potential consisted in the selection of the yield per se, which resulted in cultivars with higher grains per spike (GN) and occasionally increased grain weight (GW) (main numerical components of the yield). In this study, quantitative trait loci (QTL) for GW, GN and spike fertility traits related to GN determination were mapped using two doubled haploid (DH) populations [ver mas...]
dc.contributor.authorPretini, Nicole
dc.contributor.authorVanzetti, Leonardo Sebastian
dc.contributor.authorTerrile, Ignacio Ismael
dc.contributor.authorDonaire, Guillermo Manuel
dc.contributor.authorGonzalez, Fernanda Gabriela
dc.date.accessioned2021-07-28T11:34:01Z
dc.date.available2021-07-28T11:34:01Z
dc.date.issued2021-07
dc.identifier.issn1471-2229
dc.identifier.otherhttps://doi.org/10.1186/s12870-021-03061-y
dc.identifier.urihttp://hdl.handle.net/20.500.12123/9903
dc.identifier.urihttps://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-021-03061-y
dc.description.abstractBackground: In breeding programs, the selection of cultivars with the highest yield potential consisted in the selection of the yield per se, which resulted in cultivars with higher grains per spike (GN) and occasionally increased grain weight (GW) (main numerical components of the yield). In this study, quantitative trait loci (QTL) for GW, GN and spike fertility traits related to GN determination were mapped using two doubled haploid (DH) populations (Baguette Premium 11 × BioINTA 2002 and Baguette 19 × BioINTA 2002). Results: In total 305 QTL were identified for 14 traits, out of which 12 QTL were identified in more than three environments and explained more than 10% of the phenotypic variation in at least one environment. Eight hotspot regions were detected on chromosomes 1A, 2B, 3A, 5A, 5B, 7A and 7B in which at least two major and stable QTL sheared confidence intervals. QTL on two of these regions (R5A.1 and R5A.2) have previously been described, but the other six regions are novel. Conclusions: Based on the pleiotropic analysis within a robust physiological model we conclude that two hotspot genomic regions (R5A.1 and R5A.2) together with the QGW.perg-6B are of high relevance to be used in marker assisted selection in order to improve the spike yield potential. All the QTL identified for the spike related traits are the first step to search for their candidate genes, which will allow their better manipulation in the future.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherBMCes_AR
dc.relationinfo:eu-repograntAgreement/INTA/PNCYO-1127042/AR./Bases ecofisiológicas para el mejoramiento genético y la calidad diferenciada de cereales y oleaginosas.es_AR
dc.relationinfo:eu-repograntAgreement/INTA/2019-PE-E6-I126-001/2019-PE-E6-I126-001/AR./Mejoramiento genético de trigo pan (Triticum aestivum L.) y trigo candeal (Triticum turgidum ssp. durum Desf.)es_AR
dc.relationinfo:eu-repograntAgreement/INTA/2019-PE-E6-I114-001/2019-PE-E6-I114-001/AR./Caracterización de la diversidad genética de plantas, animales y microorganismos mediante herramientas de genómica aplicada.es_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceBMC Plant Biology 21 : Article number: 353 (2021)es_AR
dc.subjectTrigoes_AR
dc.subjectWheateng
dc.subjectTriticum aestivumes_AR
dc.subjectLoci de Rasgos Cuantitativoses_AR
dc.subjectQuantitative Trait Locieng
dc.subjectHaploidiaes_AR
dc.subjectHaploidyeng
dc.subject.otherLRC (loci de rasgos cuantitativos)es_AR
dc.subject.otherQTL (quantitative trait loci)eng
dc.titleMapping QTL for spike fertility and related traits in two doubled haploid wheat (Triticum aestivum L.) populationses_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.description.origenEEA Marcos Juárezes_AR
dc.description.filFil: Pretini, Nicole. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; Argentinaes_AR
dc.description.filFil: Pretini, Nicole. Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA). Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; Argentina.es_AR
dc.description.filFil: Vanzetti, Leonardo Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; Argentinaes_AR
dc.description.filFil: Vanzetti, Leonardo Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentinaes_AR
dc.description.filFil: Terrile, Ignacio Ismael. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Departamento de Trigo; Argentinaes_AR
dc.description.filFil: Donaire, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juarez; Argentina.es_AR
dc.description.filFil: González, Fernanda Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; Argentina.es_AR
dc.description.filFil: González, Fernanda Gabriela. Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA). Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; Argentinaes_AR
dc.description.filFil: González, Fernanda Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina.es_AR
dc.subtypecientifico


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