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Modeling covariance structures and optimizing Jatropha curcas breeding

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Abstract

Jatropha curcas has become a prominent source of biofuel, especially because of the high oil content in its fruit. The aim of this study was to test different statistic models and compare the best-fitted model with the compound symmetry model and study the grain yield persistence of J. curcas progenies. A total of 730 individuals from 73 half-sib families were evaluated for the fruit yield trait over six crop years. Repeated measures models with different covariance structures for the genetic and non-genetic effects were tested. Results show an increase up to in accuracy upon modeling the genetic and non-genetic effects when compared to the compound symmetry model. The selection gain obtained via the best-fit model for 10, 15, 20, and 25 selected best progenies was around 3 to 2% higher than gain obtained via the standard statistical model used by breeders (compound symmetry model). The harvests evaluated exhibited accuracies of high magnitude. The ten progenies that stood out with the best genetic performance are also those with the greatest persistence and greatest accumulated yield. Combining modeling of covariance structures for grain yield and selecting for persistence of production can sustain a successful long-term J. curcas breeding program.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The code and data are available at https://github.com/JenifferSPCE/Jatropha_cov.

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Acknowledgements

We express our appreciation for financial support from the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)—financing code 001.

Funding

This research was supported by the National Institute of Science and Technology of Coffee (INCT Café), Minas Gerais State Agency for Research and Development (FAPEMIG), Brazilian National Council for Scientific and Technological Development (CNPq), and Coordination for the Improvement of Higher Education Personnel (CAPES)—Finance Code 001.

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Authors and Affiliations

  1. Departamento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, Minas Gerais, CEP: 36570-900, Brazil

    Jeniffer Santana Pinto Coelho Evangelista, Marco Antônio Peixoto, Igor Ferreira Coelho, Filipe Manoel Ferreira, Saulo Fabricio da Silva Chaves, Kaio Olimpio das Graças Dias & Leonardo Lopes Bhering

  2. Departamento de Biologia, Universidade Federal de Lavras, Lavras, Minas Gerais, CEP: 37200-900, Brazil

    Tiago de Souza Marçal

  3. Departamento de Estatística, Instituto Nacional de Ciência E Tecnologia Do Café / Universidade Federal de Viçosa, CEP: 36570-900, Viçosa Minas Gerais, Brazil

    Rodrigo Silva Alves

  4. Faculdade UnB Planaltina - Ciências da Vida E da Terra, Universidade de Brasília, Brasília (DF), Brazil

    Erina Vitório Rodrigues

  5. Embrapa Agroenergia, Parque Estação Biológica, Brasília, Distrito Federal, CEP: 70770-901, Brazil

    Bruno Gâlveas Laviola

  6. Departamento de Estatística, Embrapa Café / Universidade Federal de Viçosa, Viçosa, CEPMinas Gerais, 36570-900, Brazil

    Marcos Deon Vilela de Resende

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  1. Jeniffer Santana Pinto Coelho Evangelista
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  2. Marco Antônio Peixoto
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All authors contributed to the study and conception and design.

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Correspondence to Leonardo Lopes Bhering.

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Communicated by F.P. Guerra

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Evangelista, J.S.P.C., Peixoto, M.A., Coelho, I.F. et al. Modeling covariance structures and optimizing Jatropha curcas breeding. Tree Genetics & Genomes 19, 21 (2023). https://doi.org/10.1007/s11295-023-01596-9

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