Dissecting the genetic basis of physiological processes determining maize kernel weight using a RIL population.

• Autores: Alvarez Prado, Santiago; Lopez, Cesar Gabriel; Gambin, Brenda Laura; Abertondo, Victor; Borras, Lucas
• Año de publicación: 2013
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Candidate Genes; Grain-Filling Duration; Kernel Desiccation Rate; Kernel Growth Rate; Maximum Water Content; Moisture Concentration at Physiological Maturity
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Knowledge on the genetic bases of physiological processes determining maize kernel weight (KW) is relevant for maize yield improvement. However, little is known about the genetic control of KW and its component traits: kernel growth rate (KGR) and grain-filling duration (GFD). We phenotyped several grain-filling traits in 245 RILs from the IBM Syn4 population (B73×Mo17) under two environments, and a multi-trait multi-environment quantitative trait loci (QTL) analysis was conducted. We were specifically interested inseeking genetic links of knowncorrelated traits atthe phenotypic level, like kernelmaximum water content(MWC) and KGR. Our specific objectives were (i)to conduct a QTL analysis over grain-filling traits to determine their genetic complexity,(ii)to study the relationships between kernel developmental traits at phenotypic and genetic levels, and (iii) to suggest possible candidate genes for each specific trait using detected QTL and B73 sequence data. All traits showed significant genotype × environment interactions (p < 0.001) and large phenotypic variability. KW variability was positively associated (p < 0.01) with variations in KGR (r = 0.79) and GFD (r = 0.32). As expected, KGR was positively correlated to MWC, while GFD was negatively correlated to the kernel moisture concentration at physiological maturity (MCPM). A total of 10 joint QTL were detected under both environments, located on chromosomes 1, 2, 4, 5, 6, 7, 9 and 10. Most QTL showed inconsistent effects underlying genotype × environment interactions. However, the multi-trait multi-environment approach helped understand genetic correlations between traits, where positive and consistent genetic correlations were observed between KW, KGR and MWC on chromosomes 2, 6, 9 and 10. Only one consistent QTL for KW, GFD and kernel desiccation rate (KDR) was detected. KGR and GFD showed no common consistent QTL, supporting previous observations on independent physiological control. Several detected QTL co-localized with previous mapping studies. With the use of B73 sequence data we described genes within QTL marker intervals, and discussed relevant candidate ones for future dissection. Results showing the co-localization of consistent QTL for KW, KGR and MWC suggest a common genetic basis for these critical secondary traits measured under field conditions.
Fil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentina
Fil: Lopez, Cesar Gabriel. Universidad Nacional de Lomas de Zamora. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gambin, Brenda Laura. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Departamento de Biologia. Cátedra de Fisiología Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina
Fil: Abertondo, Victor. Advanta Semillas S.A.I.C.;
Fil: Borras, Lucas. Universidad Nacional de Rosario. Facultad de Cs.agrarias; . Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Cultivo Extensivos Cereales y Oleaginosas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina