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

Marco Antônio Peixoto,Igor Ferreira Coelho,Jeniffer Santana Pinto Coelho Evangelista,Marcos Deon Vilela De Resende,Leonardo Lopes Bhering,Bruno Gâlveas Laviola,Rodrigo Silva Alves,Fabyano Fonseca E Silva,Mehdi Rahimi

doi:10.1371/journal.pone.0247775

Marco Antônio Peixoto, Igor Ferreira Coelho + Show 7 more

Open Access

https://doi.org/10.1371/journal.pone.0247775

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Abstract

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.

Highlights

The increasing energy demand and environmental awareness about pollution caused by fossil fuels have raised the interest of private companies, governments, and agencies of many countries in the development and production of alternative and sustainable energy sources
The posterior mean estimates were obtained for the variance components, which suggested density with qui square and normal shape appearances (Fig 1)
The High posterior density (HPD) intervals indicate the significance of the genetic and plot effects

Summary

Introduction

The increasing energy demand and environmental awareness about pollution caused by fossil fuels have raised the interest of private companies, governments, and agencies of many countries in the development and production of alternative and sustainable energy sources. The use of bioenergy has been pointed out in the last decades as one of many alternatives to reduce the need for fossil fuels. Bioenergy should be interpreted as a renewable source of biomass, i.e. sugar/lignocellulosic tissues and vegetable oils [1]. Many crops have been identified as potential feedstock to obtain biofuels, mainly corn, sugar cane, soybean, castor bean oil, forest. Multiple-trait Bayesian model biomass, livestock manure, oil palm, peanut, canola and physic nut (Jatropha curcas L.) [2]. Biofuel may be used in engines either directly as fuel or as a blend mixed with fossil fuels [3, 4]

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