Divergência genética e predição de valores genotípicos em soja
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Data
2014-05-07
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Universidade Federal de Goiás
Resumo
Soybean breeding programs practice selection of high genetic value genotypes
with two main objectives: a) to use them as parents in the hybridization process (first stage
of the program), and b) to indicate them as new cultivars (final stage of the program). In
this context, a first study used microsatellite markers (SSR) to assess the genetic diversity
of soybean germplasm adapted to the Brazilian conditions. The experimental material
consisted of 192 accessions, which included both introductions and Brazilian germplasm.
The genetic divergence was assessed by descriptive analysis and the Rogers-W genetic
distance. A total of 222 alleles were identified in the 37 genotyped loci, with an average of
six alleles and a range of 2 to 14 alleles per locus. The genotypes were clustered according
to the origin of the germplasm, and resulted in two groups: one group formed by
introductions and other by Brazilian genotypes. Eighty five percent of the genetic distances
estimates were above 0.70, suggesting that the assessed germplasm has good potential for
hybridization in soybean breeding programs. It was concluded that the SSR markers are
useful to identify divergent genotypic groups, as well as genotypic combinations with high
genetic variability. It also became clear that the use of introduced germplasm ensures the
incorporation of alleles necessary to increase the genetic base of soybeans and,
consequently, the variability needed for the selective process. In a second study, the mixed
model approach was used to assess some strategies of estimation and prediction of
genotypic values for grain yield in the soybean regional yield trials. A total of 111
genotypes classified into three maturity groups were sown in up to 23 experiments in
Central Brazil. The experiments were carried out in randomized complete block designs,
with three replications. The biometrical analyses followed the fixed model and mixed
model approaches, in the latter case assuming the genotypic effects as random. In the
mixed model approach, analyses were made with or without information from the
relationship estimates obtained either by genealogy or SSR markers, arranged in a
genotypic covariance matrix (G). Also, in a context of spatial analysis, different structures
were used in the residual covariance matrix (R) for each mixed model adjusted. The
following conclusions were obtained: i) the fixed model analysis is adequate to estimate
genotypic values in soybean trials with balanced data and orthogonal design; ii) under such
conditions and intermediate to low heritability, the inclusion of relationship information
associated to G matrix, although does not ensure the best fit models, improves the
precision in predicting genotypic values; iii) the use of spatial structures associated to R
matrix, in presence of the residual autocorrelation, improves the goodness of model fit to
the data; and, iv) the choice of model for the analysis does not change the ranking of the
genotypes in high heritability situations and, therefore, does not impact significantly on the
selection of superior genotypes.
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GODOI, Cláudio Roberto Cardoso de. Divergência genética e predição de valores genotípicos em soja. 2014. 108 f. Tese (Doutorado em Genética e Melhoramento de Plantas) - Universidade Federal de Goiás, Goiânia, 2014.