Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the value from very low to $$\pi$$ π = $${10}^{-5}$$ 10 - 5 ), BayesC and Bayesian Lasso (LASSO) for predictions of the genomic genetic values of productivity and quality traits of a guava population. The models were fitted for traits fruit mass, pulp mass, soluble solids content, fruit number, and production per plant in the genomic prediction with SSR markers, obtained through the CTAB extraction method with 200 primers. The Bayesian ridge regression model showed the best results for all traits and was chosen to predict the individual’s genomic values according to the cross-validation data. A good stabilization of the Markov and Monte Carlo chains was observed with the mean values close to the observed phenotypic means. Heritabilities showed good predictive accuracy. The model showed strong correlations between some traits, allowing indirect selection.

Brazil is one of the world's largest producers of guava (Psidium guajava L.), a very promising fruit in the northern region of the state of Rio de Janeiro. Despite this, no guava cultivar has been developed for the region. Thus, this study proposed to examine a population of guava full sibs using microsatellite markers and to identify which genotypes are the most divergent for future crosses, to select cultivars better adapted to the soil and climatic conditions of northern Rio Janeiro. Ninety-six superior genotypes were selected according to their agronomic traits, which were characterized using 45 microsatellite markers. The genetic distance between the analyzed genotypes, their clustering pattern and the genetic structure of the population were estimated. Hierarchical cluster analysis by the neighbor joining method indicated the formation of three distinct groups. The use of molecular information revealed the existence of moderate genetic variability between the genotypes of the full-sib families. Bayesian analysis separated the genotypes into only two groups, as the individuals shared most of the analyzed genomic regions. The most genetically divergent guava genotypes, that is, those allocated to different groups, such as genotypes 5 and 85, should be recommended for future crosses to obtain segregating populations, thus giving continuity to the guava breeding program.

Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the value from very low to Π = 10-5 ) and BayesC and Bayesian Lasso (LASSO) for predictions of the genomic genetic values of productivity and quality traits of a guava population. A randomized block design with two replications was used. Seventeen full-sib families were evaluated. Fruit mass, pulp mass, soluble solids content, number of fruits, and production per plant were measured. These variables were used in the genomic prediction with SSR markers, obtained through the CTAB extraction method with 200 primers.The Bayesian ridge regression model showed the best results for all variables and was chosen to predict the individuals' genomic values according to the cross-validation data.A good stabilization of the Markov and Monte Carlo chains was observed with the mean values corresponding to the observed phenotypic means. Heritabilities showed good predictive accuracy. The model showed strong correlations between some variables, allowing indirect selection.