Application of the Bayesian multi-trait model to estimate genetic covariance and heritability in the evaluation of Coffea canephora in an agroecological-based system
DOI:
https://doi.org/10.4025/actasciagron.v48.i1.77337Keywords:
genetic parameter; conilon; biometrics; inverse wishart; bayesian inferenceAbstract
The purpose of this study is to analyze the genetic and residual variability of the grain yield trait in Coffea canephora using the Bayesian multi-trait model. Thirty-six varieties of conilon coffee were used. The design was randomized blocks, with three replications, nine plants per plot. Data was analyzed using multi-trait Bayesian models with an arbitrary number of random effects, employing a Gibbs sampler. The covariance matrix of the random effects is assigned as a prior Inverse Wishart distribution. A total of 1,800,000 samples were generated, with a burn-in of 5,000 and a thin of 5 interactions, resulting in 1,795,000 samples. The broad-sense heritability, residual and genetic variations were calculated from the posterior distribution. The variance-covariance matrix for the genetic factor shows significant variability among the traits. The 95% credibility intervals for the variances and covariances are narrow, indicating accurate estimates. The data is adequate to provide reliable estimates, indicating significant genetic effects on most traits. The residual variance-covariance matrix reveals heteroscedasticity among the traits. According to the results, accurate estimates of broad-sense heritability obtained by the Bayesian methods can guide breeding programs, in addition to identifying traits with high genetic variability and potential for response to selection. The Bayesian approach provided robust and detailed estimates, aligning with previous studies and offering valuable insights for breeding programs.
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Copyright (c) 2026 Antônio Carlos Silva Junior, Waldênia de Melo Moura, Sirlene Viana de Faria , Luciana Gomes Soares , Hugo Sebastião Sant’Anna Andrade , Carlos Victor Vieira Queiroz, Isabella Pinto de Oliveira, Cosme Damião Cruz (Autor)
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