White mold resistance-associated quantitative trait loci in the Jalo x Small White common bean population.

We aimed to identify simple sequence repeat (SSR) markers linked to quantitative trait loci (QTLs) associated with white mold resistance in a segregating population derived from a cross between common bean cultivars Jalo and Small White, in the Southern State of Minas Gerais. Parents were crossed to obtain the F2 generation of 190 plants. From these, F2:3 and F2:4 progenies were obtained for phenotypic evaluation. DNA was extracted from F2 plants and parents for genotyping with SSR primers. For phenotypic assessment by the straw test, we used 190 F2:3 progenies and six lines in a triple-lattice design of 14 x 14 m. Nine trials were conducted for phenotyping by the oxalic acid method to evaluate 177 F2:4 progenies, in addition to the two parents and two common treatments represented by the Jalo and Corujinha lines. The experimental design for the oxalic acid method was completely randomized with three replicates. Adjusted means of evaluations related to F2:3 and F2:4 were used to identify QTLs by using the moving away method from the marker under Bayesian analysis. The markers GATS91, BM197, and X60000 stood out with high heritability and with effects on disease reduction by the straw test; therefore, these markers are promising for selection. The markers BM183, BM189, and SSR-IAC143 were associated with the QTLs identified by oxalic acid, providing greater effects in white mold resistance with high heritability. Based on the oxalic acid and straw test methods, the most prominent marker was PVBR189.

Factor analysis using mixed models of multi-environment trials with different levels of unbalancing.

This study aimed to analyze the robustness of mixed models for the study of genotype-environment interactions (G x E). Simulated unbalancing of real data was used to determine if the method could predict missing genotypes and select stable genotypes. Data from multi-environment trials containing 55 maize hybrids, collected during the 2005-2006 harvest season, were used in this study. Analyses were performed in two steps: the variance components were estimated by restricted maximum likelihood, using the expectation-maximization (EM) algorithm, and factor analysis (FA) was used to calculate the factor scores and relative position of each genotype in the biplot. Random unbalancing of the data was performed by removing 10, 30, and 50% of the plots; the scores were then re-estimated using the FA model. It was observed that 10, 30, and 50% unbalancing exhibited mean correlation values of 0.7, 0.6, and 0.56, respectively. Overall, the genotypes classified as stable in the biplot had smaller prediction error sum of squares (PRESS) value and prediction amplitude of ellipses. Therefore, our results revealed the applicability of the PRESS statistic to evaluate the performance of stable genotypes in the biplot. This result was confirmed by the sizes of the prediction ellipses, which were smaller for the stable genotypes. Therefore, mixed models can confidently be used to evaluate stability in plant breeding programs, even with highly unbalanced data.

A new method of QTL identification for undersaturated maps.

In many species, low levels of polymorphism prevent the assembly of linkage maps that are used to identify genetic markers related to the expression of quantitative trait loci (QTLs). This study compared two methods of locating QTLs in association studies that do not require a previous estimation of linkage maps. Method I (MI) was a Bayesian multiple marker regression and Method II (MII) combined multiple QTL mapping and "moving away from markers". In this method, markers are not directly regressed to the phenotype, but are used as pivots to search for QTLs along the genome. To compare methods, we simulated 300 individuals from an F2 progeny with two levels of marker loss (20 and 80%). A total of 165 markers and seven QTLs were spread along 11 chromosomes (roughly emulating the genetic structure of the common bean, Phaseolus vulgaris). A real data example with 186 progenies of a F2:4 generation of the species was analyzed using 59 markers (17 simple sequence repeats, 31 amplified fragment length polymorphisms, and 11 sequence-related amplified polymorphisms). MII was more precise than MI for both levels of marker loss. For real data, MII detected 17 candidate positions for QTLs, whereas MI did not detect any. MII is a powerful method that requires further studies with actual data and other designs such as crossover, and genome-wide studies.

Identification of QTLs of resistance to white mold in common bean from multiple markers by using Bayesian analysis.

In this study, we identified simple sequence repeat, ampli-fied fragment length polymorphism, and sequence-related amplified poly-morphism markers linked to quantitative trait loci (QTLs) for resistance to white mold disease in common bean progenies derived from a cross between lines CNFC 9506 and RP-2, evaluated using the oxalic acid test and using Bayesian analysis. DNA was extracted from 186 F2 plants and their parental lines for molecular analysis. Fifteen experiments were car-ried out for phenotypic analysis, which included 186 F2:4 progenies, the F1 generation, the F2 generation, and the lines CNFC 9506, RP-2, and G122 as common treatments. A completely randomized experimental design with 3 replications was used in controlled environments. The adjusted means for the F2:4 generation were to identify QTLs by Bayesian shrink-age analysis. Significant differences were observed among the progenies for the reaction to white mold. The moving away method under the Bayes-ian approach was effective for identifying QTLs when it was not possible to obtain a genetic map because of low marker density. Using the Wald test, 25 markers identified QTLs for resistance to white mold, as well as 16 simple sequence repeats, 7 amplified fragment length polymorphisms, and 2 sequence-related amplified polymorphisms. The markers BM184, BM211, and PV-gaat001 showed low distances from QTLs related white mold resistance. In addition, these markers showed, signal effects with increasing resistance to white mold and high heritability in the analysis with oxalic acid, and thus, are promising for marker-assisted selection.