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BAG. Journal of basic and applied genetics

On-line version ISSN 1852-6233

Abstract

ROSSI, E.A; RUIZ, M; DI RENZO, M  and  BONAMICO, N.C. Genotypic diversity in 291 maize lines from CIMMYT and phenotypic characterization in southern Córdoba, Argentina. BAG, J. basic appl. genet. [online]. 2019, vol.30, n.1, pp.25-33. ISSN 1852-6233.

CIMMYT maize inbred lines (CMLs) are freely distributed to breeding programs around the world. Better information on phenotypic and genotypic diversity may provide guidance to breeders on how to use more efficiently the CMLs in their breeding programs. In this study a group of 291 CIMMYT maize inbred lines, was phenotyped by nine agro-morphological traits in south Córdoba, Argentina and genotyped using 18,082 SNPs. Based on the geographic information and the environmental adaptation, 291 CMLs were classified into eight subgroups. Anthesis-silking interval (IAE) was the trait with higher phenotypic diversity. A 40% of maize inbred lines, with IAE less than five days, show a good adaptation to growing conditions in south Córdoba, Argentina. The low phenotypic variation explained by environmental adaptation subgroups indicates that population structure is only a minor factor contributing to phenotypic diversity in this panel. Principal component analysis (ACP) allowed us to obtain phenotypic and genotypic orderings. Generalized procrustes analysis (APG) indicated a 60% consensus between both data type from the total panel of maize lines. In each environmental adaptation subgroup, the APG consensus was higher. This result, which might indicate linkage disequilibrium between SNPs markers and the genes controlling these agro-morphological traits, is promising and could be used as an initial tool in the identification of Quantitative Trait Loci (QTL). Information on genetic diversity, population structure and phenotypic diversity in local environments will help maize breeders to better understand how to use the current CIMMYT maize inbred lines group.

Keywords : Broad-sense heritability; Multivariate analysis; SNPs; Agro-morphological traits.

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