Open Access

ARTICLE

Association between microsatellites and resistance to Mal de Río Cuarto in maize by discriminant analysis

Bonamico¹ NC, MG Balzarini², AT Arroyo², MA Ibañez¹, DG Díaz³, JC Salerno³, MA Di Renzo¹

1 Facultad de Agronomía y Veterinaria, Universidad Nacional de Río Cuarto, Agencia n°3, 5800 Río Cuarto, Argentina.
2 Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, cc 509, 5000 Córdoba, and CONICET (National Council of Scientific and Technological Research), Argentina.
3 Instituto de Genética “Ewald A. Favret ”, Instituto Nacional de Tecnología Agropecuaria, cc 25, 1712 Castelar, Argentina.

* Corresponding Author:Address Correspondence to: Natalia C. Bonamico, e-mail: ; Phone: 054-0358-4676145.

Phyton-International Journal of Experimental Botany 2010, 79(all), 31-38. https://doi.org/10.32604/phyton.2010.79.031

Abstract

Resistance to Mal de Río Cuarto (MRC) disease in maize (Zea mays L.) is important in Argentina because the crop area involves a wide region where the disease is endemic. Molecular marker-assisted selection could be used as an additional selection tool to enhance precision of the genotype selection for resistance. It demands the identification of informative markers. Microsatellite (SSR) markers linked to gene(s) associated with resistance to MRC have been reported from previous QTL analyses. These analyses have been made on linkage maps derived from a relatively early mapping population. In advanced populations, where highly distinct genotypes are easily classified, discriminant analysis (DA) represents a complementary strategy to marker identification; this method does not require a priori genetic map. The objectives of this study were (1) to identify SSR markers associated with MRC resistance by using DA, and (2) to assess DA-selected SSR markers consistency across environments. The recombinant inbred lines (RILs) were evaluated for disease severity and traits related to symptoms of MRC disease at five environments located in the endemic area. The DNA profiles were obtained using 60 SSR. For discriminant analysis, the RILs were assigned to one of two groups defined to represent low and high values for each trait. A molecular analysis of variance (AMOVA) from marker data found significant molecular differences between the two extreme groups formed for each trait before DA. There was an array of markers associated with the MRC disease severity and with traits related to symptoms of disease. The lack of consistency in the several DA-selected SSR markers across environments indicated that genotype-environment interaction effects were significant. Selected markers can be used to allocate new individuals to predefined groups as well as to infer putative localization of genes with small individual effects on resistance to MRC.

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