Maize nitrogen use efficiency : QTL mapping in a U.S. Dent x Argentine - Caribbean Flint RILs population

• Autores: Mandolino, Cecilia I.; D'Andrea, Karina Elizabeth; Olmos, Sofía; Otegui, María Elena; Eyhérabide, Guillermo Hugo
• Año de publicación: 2018
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Maydica; Vol.63, no.1; m2, 17 p.; https://journals-crea.4science.it
• Materia: CANDIDATE GENES; MAIZE; NITROGEN USE EFFICIENCY (NUE); QUANITATIVE TRAIT LOCI (QTL)
• Institución: Universidad de Buenos Aires. Facultad de Agronomía

Fil: Mandolino, Cecilia I. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino (EEA Pergamino). Buenos Aires, Argentina.
Fil: D'Andrea, Karina Elizabeth. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina.
Fil: Olmos, Sofía. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino (EEA Pergamino). Buenos Aires, Argentina.
Fil: Otegui, María Elena. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino (EEA Pergamino). Buenos Aires, Argentina.
Fil: Eyhérabide, Guillermo Hugo. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino (EEA Pergamino). Buenos Aires, Argentina.
This study was aimed to identify quantitative trait loci (QTL) for nitrogen use efficiency (NUE) and related traits in a maize population derived from a cross between two lines with different genetic background (B100 and LP2). Recombinant inbred lines (181) from this population were evaluated under field conditions during two growing seasons, and significant (P minor sign 0.01) phenotypic and genotypic variability was detected for most evaluated traits. Two different mapping methods were applied for detecting QTLs. Firstly, a trait by trait approach was performed on across environments, and 19 QTLs were identified. Secondly, a multi - trait multi - environment analysis detected seven joint QTLs. Almost all joint QTLs had inconsistent additive effects from one environment to another, which would reflect presence of QTL × Environment interaction. Most joint QTLs co - localized with QTLs detected by individual mapping. We detected consistent additive effects for grain yield per plant and NUE, as well as for biomass and nitrogen harvest index in some joint QTLs, especially QTL-1 and QTL-6. These QTLs had positive and stable effects across environments, and presence of some genes within these QTL intervals could be relevant for selecting for both NUE and grain yield simultaneously. Up today, this is a first report on the co - localization of QTLs for enhanced allocation of biomass allocation to grains with NUE, and NUE candidate gene identification. Fine mapping of these regions could allow to detect additional markers more closely linked to these QTLs that could be used for marker assisted selection for NUE.
tbls.