Impact of Bayesian Inference on the Selection of Psidium guajava

Silva, Flávia Alves da; Viana, Alexandre Pío; Corrêa, Caio Cézar Guedes; Carvalho, Beatriz Murizini; Sousa, Carlos Misael Bezerra de; Amaral, Bruno Dias; Ambrósio, Moisés; Glória, Leonardo Siqueira

doi:10.1038/s41598-020-58850-6

Cited by 16 publications

(18 citation statements)

References 21 publications

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“…It is worth mentioning that the REML method also presents hindrances regarding the convergence of the MTM [38], whereas the convergence of the Bayesian MTM it is easier to achieve [11]. Therefore, when compared with the frequentist inference, it seems reasonable that the Bayesian inference presents better results [11,13,14,39,40].…”

Section: Plos Onementioning

confidence: 99%

“…In general, Bayesian inference has outperformed traditional frequentist analyses [12][13][14][15] by: (i) providing additional results to the frequentist approach, such as creditability and highest posterior density intervals (HPD); (ii) estimating the genetic parameters and the genetic values with increased precision, once it is conducted by Gibbs sampler, which is a Markov Chain Monte Carlo (MCMC) sampling algorithm; and, (iii) being a flexible methodology that allows to estimate accurately variance components and genetic values, even from reduced samples.…”

Section: Introductionmentioning

confidence: 99%

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Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

et al. 2021

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Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

et al. 2021

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“…The segregating families were obtained by crossings between the accessions, that were established considering information on genetic diversity obtained by Pessanha, et al 14 . Twelve families were selected and from them we selected the best individuals from each family based on the work of Silva, et al 15 Five explanatory variables were measured for each individual: fruit mass (FM), pulp mass (PM), soluble solids content (SSC), number of fruits per plant (NF), and production per plant (PROD). Five observations of all variables were obtained, except for NF and PROD, for which one observation was carried out per individual.…”

Section: Genetic Materialsmentioning

confidence: 99%

Bayesian Ridge Regression Shows the Best Fit for Ssr Markers in Psidium Guajava Among Bayesian Models

Silva

Viana

Corrêa

et al. 2021

Preprint

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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.

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“…The Bayesian approach is another method for more accurate estimates, especially few observations are available and designs are unbalanced (Silva et al, 2013;Silva et al, 2018;Silva et al, 2020). This approach allows to use of a priori distributions incorporated into the model, which can be an advantage.…”

Section: Introductionmentioning

confidence: 99%

Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit

Souza

Viana

Silva

et al. 2022

Sci. agric. (Piracicaba, Braz.)

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Methods for genetic improvement of semi-perennial species, such as passion fruit, often involve large areas, unbalanced data, and lack of observations. Some strategies can be applied to solve these problems. In this work, different models and approaches were tested to improve the precision of estimates of genetic evaluation models for several characteristics of the passion fruit. A randomized block design (RBD) model was compared to a posteriori correction, adding two factors to the model (post-hoc blocking Row-Col). These models were also combined with the frequentist and Bayesian approaches to identify which combination yields the most accurate results. These approaches are part of a strategic plan in a perennial plant breeding program to select promising genitors of passion to compose the next selection cycle.For Bayesian, we tested two priors, defining different values for the distribution parameters of effect variances of the model. We also performed a cross-validation test to choose a priori values and compare the frequentist and Bayesian approaches using the root mean square error (RMSE) and the correlation between the predicted and observed values, called Predictive capacity of the model (PC). The model with the post-hoc blocking Row-Col design captured the spatial variability for productivity and number of fruits, directly affecting the experimental precision. Both approaches applied to the models showed a similar performance, with predictive capacity and selective efficiency leading to the selection of the same individuals.

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Impact of Bayesian Inference on the Selection of Psidium guajava

Cited by 16 publications

References 21 publications

Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

Bayesian Ridge Regression Shows the Best Fit for Ssr Markers in Psidium Guajava Among Bayesian Models

Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit

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