Heritability of quantitative traits in segregating common bean families using a Bayesian approach |
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Authors: | Maria Celeste Gonçalves-Vidigal Freddy Mora Thaís Souto Bignotto Roxelle Ethienne Ferreira Munhoz Lara Daniela de Souza |
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Institution: | (1) Departamento de Agronomia, Universidade Estadual de Maringá, Av. Colombo 5790, Bloco 05, Maringa, Parana, CEP 87020-900, Brazil |
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Abstract: | Genetic parameters for six quantitative traits in the early generation of segregating populations of common beans (Phaseolus vulgaris L.) were evaluated. A Bayesian approach was used for estimating the variance components, breeding values and broad sense
heritability of the quantitative traits under analysis. The Markov Chain Monte Carlo method was utilized to analyze the contribution
of genes affecting complex traits. Twenty-four F3 families were evaluated in the field during 2005 in Santa Catarina, southern Brazil. With regard to the grain yield and yield
components, the additive variances were relatively similar to the dominance variances. This result is confirmed by the 95%
credible set from the posterior distribution. The mean estimates of broad-sense heritability (H2) varied from 11.5% to 64.2%. The heritability estimates of yield and yield components were higher than the estimates for
the number of days until flowering and reproductive period. However, for grain yield, the 95% heritability credible set included
the heritability estimates from point of crop duration. The predicted genetic gain reached the highest value for the number
of pods per plant (10.95%). Days to flowering and reproductive period had the lowest values of genetic advance. One hundred
seed-weight, grain yield and seeds per pod exhibited a similar predictable level of genetic gain: GA = 5.73%, 5.81% and 4.77%,
respectively. The Bayesian framework provided information that is useful for a breeding program, since it contributes to the
understanding of how quantitative traits are genetically controlled. |
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Keywords: | Bayesian analysis Breeding value Genetic effect Markov Chain Monte Carlo |
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