Abstract
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 = {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.
Highlights
Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making
It was identified that Bayesian models (Bayesian LASSO—Bayesian Lasso (BL)) and ridge regression models (BRR) showed superior performance for traits controlled by additive genetic e ffects[5]
Six Bayesian models were applied to detect the effect of the markers along with phenotypic data from a guava population
Summary
Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. Tropical fruits have a great commercial value worldwide because, besides being widely consumed in the countries that produce them, they are highly appreciated and with a great added value around the world[1] One of these perennial fruits is the guava tree (Psidium guajava L.) which is gaining space on the market in parts, due to the increasingly efficient selection methods for improving the species. With a Bayesian approach, the effects of the markers can be estimated together to predict the genomic values for a quantitative trait without making the previous selection in the panel of markers[3] This Bayesian genomic selection has as main advantages the inclusion of a priori information in the model, besides generating more accurate credibility intervals[4]. The double exponential distribution has a high mass density at zero, and heavier priori tails compared to a Scientific Reports | (2021) 11:13639
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