INVESTIGADORES
MADDONNI Gustavo Angel
congresos y reuniones científicas
Título:
Búsqueda de Marcadores moleculares asociados a la variabilidad fenotípica entre plantas de isohíbridos de maíz
Autor/es:
LASERNA, M.P.; AULICINO, M.; LÓPEZ, C.G.; MADDONNI, G.A
Lugar:
Rosario
Reunión:
Congreso; X Congreso Nacional de Maíz; 2014
Institución organizadora:
AIANBA
Resumen:
Our first hypothesis stated that transgene introduction could increase the phenotypic variation of maize hybrids. However, for certain traits the populations of two transgenic versions (DK747MG and DK747MGRR) showed less inter-plant variability than that of the non-transgenic version (DK747). In the same way, the study of genetic variation of these populations using molecular markers (SNPs) showed that the DK747 had the highest genetic diversity (2.74). The aim of this study was to associate the variable SNPs (i.e. genotypic variation) with phenotypic variation of different traits. Experiments were conducted at the experimental field of FA-UBA during 2008-2009 and 2009-2010. Ten plants per plot were labeled to quantify several phenotypic traits. Among them, plant biomass at physiological maturity was used to select contrasting plants (dominant, mean and dominated plants) types for the molecular markers study (SNPs). Two matrices were built, one with 4554 variable SNPs and another one with seven phenotypic traits. The correlation analysis of both matrices was not significant. However, Fisher exact test used to compare the alleles of the dominated and the dominant plants of each version, allowed us to find SNPs whose segregation was associated with plant biomass of the individuals. The significant variable SNPs among types of plants, however, did not agree among versions (33 SNPs in DK747, 267 SNPs in DK747MG and 44 SNPs DK747RR) but were grouped defining a region on chromosome 1, two regions on chromosome 3, and three regions on chromosome 5. Finally, a group of variable SNPs was selected from a correspondence analysis and were subsequently used in a cluster analysis together with phenotypic traits. This analysis allowed us to detect several groups of molecular markers associated with these traits.