QTL mapping in sweet corn testecrosses with different testers and environments / Mapeamento de QTL em testecrosses de milho doce com diferentes testadores e ambientes

AUTOR(ES)
DATA DE PUBLICAÇÃO

2010

RESUMO

One of the main challenges in sweet corn breeding is to improve the efficiency of selection for grain yield and quality traits. The use of molecular markers would be a way to increase the selection efficiency in breeding programs. QTL mapping is an important tool for understanding the genetic basis of the traits and to generate information that can be used in marker assisted selection. This study aimed to map QTL in sweet corn testecrosses for grain yield, its components and quality traits, and evaluate the effect of different testers and environments in QTL mapping. For this study a population was obtained by crossing lines B532 and B605, from the same heterotic group and contrasting for different traits. Two hundred and fifty-six F4:5 progenies were genotyped with SNP markers for the construction of the genetic map. Subsequently, these progenies were crossed with the testers A36 and A17 from a different heterotic group than the population. The obtained testecrosses were evaluated in two environments, Uberlândia, MG, e Itatiba, SP, in a simple lattice design 16 x 16. The traits evaluated were: grain yield (PG); number of rows (NF); ear length (CE); ear diameter (DE); kernel depth (CG), kernel color (CL); kernel tenderness (MC) and kernel sweetness (DÇ). The composite interval mapping extended to multiple environments (mCIM) was used to map QTL and to detect the QTL x environment interaction. One hundred and sixteen QTL were mapped; with 21 for PG, 17 for NF, 22 for CE, 14 for DE, 12 for CG, 11 for CL, 11 for MC and 8 for DÇ. With the exception of 2 QTL for NF which explained 12%,19% and 10,03% and by 1 QTL for CE which explained 10,48%, all the others explained less than 10% of the phenotypic variance. Considering all of the traits, 91% of the mapped QTL were specific to each tester, indicating a high QTL x tester interaction. Out of the 116 QTL mapped, only 22 showed significant QTL x environment interaction, indicating that there was a small QTL x environment interaction. Thus, most traits of economic importance in sweet corn seem to be controlled by many QTL with small effects, which showed a large QTL x tester interaction and a small QTL x environment interaction. The large number of QTL controlling the traits and the large QTL x tester interactions demonstrate the complexity of the implementation of marker assisted selection in sweet corn breeding.

ASSUNTO(S)

produtividade. yield. mapeamento genético corn-quality marcador molecular milho - qualidade molecular marker genetic mapping

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