Potential of maize single-cross hybrids for extraction of inbred lines using the mean components and mixed models with microsatellite marker information

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ABSTRACT. We evaluated the potential of the best linear unbiased predictor (BLUP) along with the relationship coefficient for predicting the performance of untested maize single-cross hybrids. Ninety S 0:2 progenies arising from three single-cross hybrids were used. The 90 progenies were genotyped with 25 microsatellite markers, with nine markers linked to quantitative trait loci for grain yield. Based on genetic similarities, 17 partial inbred lines were selected and crossed in a partial diallel design. Similarity and relationship coefficients were used to construct the additive and dominance genetic matrices; along with BLUP, they provided predictions for untested single-crosses. Five degrees of imbalance were simulated (5, 10, 20, 30, and 40 hybrids).The correlation values between the predicted genotypic values and the observed phenotypic means varied from 0.55 to 0.70, depending on the degree of imbalance. A similar result was observed for the specific combining ability predictions; they varied from 0.61 to 0.70. It was also found that the relationship coefficient based on BLUP provided more accurate predictions than similarity-in-state predictions. We conclude Prediction of maize single-cross hybrids that BLUP methodology is a viable alternative for the prediction of untested crosses in early progenies.

Section: Introductionmentioning

confidence: 99%

Prediction of maize single-cross performance by mixed linear models with microsatellite marker information

Balestre

Pinho²,

Souza³

2010

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“…Nevertheless, some authors (Bauer et al, 2006;Balestre et al, 2008c) have suggested the use of similarity-in-state to predict genetic values using the methodology of BLUP. However, observing accuracy values for the predictions of SCA and GCA (Figure 1), it can be noted that the LR coefficient yielded a greater magnitude of accuracy values in comparison with RMD.…”

Section: Discussionmentioning

confidence: 99%

Potential use of molecular markers for prediction of genotypic values in hybrid maize performance

Balestre¹,

Pinho²,

Souza³

et al. 2009

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ABSTRACT. We evaluated the potential of genetic distances estimated by microsatellite markers for the prediction of the performance of single-cross maize hybrids. We also examined the potential of molecular markers for the prediction of genotypic values and the applicability of the Monte Carlo method for a correlation of genetic distances and grain yield. Ninety S 0:2 progenies derived from three single-cross hybrids were analyzed. All 90 progenies were genotyped with 25 microsatellite markers, including nine markers linked to quantitative trait loci for grain yield. The genetic similarity datasets were used for constructing additive genetic and dominance matrices that were subsequently used to obtain the best linear unbiased prediction of specific combining ability and general combining ability. The genetic similarities were also correlated with grain yield, specific combining ability and heterosis of the hybrids. Genetic distances had moderate predictive ability for grain yield (0.546), specific combining ability (0.567) and heterosis (0.661). The Monte Carlo simulation was found to be a viable alternative for a correlation of genetic distances and grain yield. The accuracy of genotypic values using molecular data information was slightly higher than if no such information was incorporated. The estimation of the relationship using molecular markers proved to be a promising method for predicting genetic values using mixed linear models, especially when information about pedigree is unavailable.

“…Each single-cross hybrid was genotyped with 20 microsatellite markers, with nine linked to quantitative trait loci for components of grain yield in tropical maize (Balestre et al, 2008). Starting from the plots dataset, diallel analysis was conducted using the IV method, proposed by Griffing (1956).…”

Section: Methodsmentioning

confidence: 99%

“…The prediction accuracy is dependent on the heritability of traits in the tested hybrids, adequate estimates of variance components, accurate approximation of relationship coefficients by molecular markers (Balestre et al, 2009), and number of predictor parentals in the analysis (Vuylsteke et al, 2000;Schrag et al, 2006). Some authors suggest the use of molecular marker information associated with similarity-by-state coefficients for genotypic value prediction (Bauer et al, 2006;Balestre et al, 2008). However, Balestre et al (2009Balestre et al ( , 2010 observed problems with using similarity-by-state coefficients for prediction of genotypic values, mainly because information concerning unrelated genotypes is included in the relationship matrix, causing bias in the prediction of genotypic values.…”

Section: Introductionmentioning

confidence: 99%

Prediction of maize double-cross hybrids using the best linear unbiased prediction with microsatellite marker information

Balestre¹,

Rg²,

Jc³

2011

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ABSTRACT. We examined the usefulness of the best linear unbiased prediction associated with molecular markers for prediction of untested maize double-cross hybrids. Ten single-cross hybrids from different commercial backgrounds were crossed using a complete diallel design. These 10 single-cross hybrids were genotyped with 20 microsatellite markers. The best linear unbiased prediction associated with microsatellite information gave relatively good prediction ability of the double-cross hybrid performance, with correlations between observed phenotypic values and genotypic prediction values varying from 0.27 to 0.54. Taking into account the predictions of specific combing ability, the correlation between observed and predicted specific combining ability varied from 0.50 to 0.88. Based on these results, we infer that it is feasible to predict maize doublecross hybrids with different unbalance degrees without including any prior information about parental inbreed lines or single-cross hybrid performance.