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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Plant Breeding
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1441683

Training set optimization is a feasible alternative for perennial orphan crop domestication and germplasm management: An Acrocomia aculeata example

Provisionally accepted
  • 1 Universidade Federal de Viçosa, Viçosa, Brazil
  • 2 State University of Campinas, Campinas, São Paulo, Brazil
  • 3 Instituto Agronômico de Campinas (IAC), Campinas, São Paulo, Brazil
  • 4 Instituto de Pesca, Agência de Agronegócio e Tecnologia de São Paulo (APTA), São Paulo, São Paulo, Brazil

The final, formatted version of the article will be published soon.

    Orphan perennial native species are gaining importance as sustainability in agriculture becomes crucial to mitigate climate change. Nevertheless, issues related to the undomesticated status and lack of improved germplasm impede the evolution of formal agricultural initiatives. Acrocomia aculeata -a neotropical palm with potential for oil production -is an example. Breeding efforts can aid the species to reach its full potential and increase market competitiveness. Here, we present genomic information and training set optimization as alternatives to boost orphan perennial native species breeding using Acrocomia aculeata as an example. Furthermore, we compared three SNP calling methods and, for the first time, presented the prediction accuracies of three yield-related traits. We collected data for two years from 201 wild individuals. These trees were genotyped, and three references were used for SNP calling: the oil palm genome, de novo sequencing, and the A. aculeata transcriptome. The traits analyzed were fruit dry mass (FDM), pulp dry mass (PDM), and pulp oil content (OC). We compared the predictive ability of GBLUP and BayesB models in cross-and real validation procedures. Afterwards, we tested several optimization criteria regarding consistency and the ability to provide the optimized training set that yielded less risk in both targeted and untargeted scenarios. Using the oil palm genome as a reference and GBLUP models had better results for the genomic prediction of FDM, OC, and 1Training set optimization in macauba PDM (prediction accuracies of 0.46, 0.45, and 0.39, respectively). Using the criteria PEV, r-score and core collection methodology provides risk-averse decisions. Training set optimization is an alternative to improve decision-making while leveraging genomic information as a cost-saving tool to accelerate plant domestication and breeding. The optimized training set can be used as a reference for the characterization of native species populations, aiding in decisions involving germplasm collection and construction of breeding populations

    Keywords: Genomic prediction, Macauba, perennial native species, Risk-averse decisions, GBLUP, Bayes B

    Received: 31 May 2024; Accepted: 14 Aug 2024.

    Copyright: © 2024 Oliveira Couto, Chaves, Dias, Morales Marroquín, Alves-Pereira, Motoike, Colombo and Zucchi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Evellyn Giselly De Oliveira Couto, Universidade Federal de Viçosa, Viçosa, Brazil
    Maria I. Zucchi, Instituto de Pesca, Agência de Agronegócio e Tecnologia de São Paulo (APTA), São Paulo, 11045-401, São Paulo, Brazil

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.