Enhancing prediction accuracy of foliar essential oil content, growth, and stem quality in Eucalyptus globulus using multi-trait deep learning models.

Autor:	Mieres-Castro D; Laboratory of Genomics and Forestry Biotechnology, Institute of Biological Sciences, University of Talca, Talca, Chile., Maldonado C; Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile., Mora-Poblete F; Laboratory of Genomics and Forestry Biotechnology, Institute of Biological Sciences, University of Talca, Talca, Chile.
Jazyk:	angličtina
Zdroj:	Frontiers in plant science [Front Plant Sci] 2024 Oct 10; Vol. 15, pp. 1451784. Date of Electronic Publication: 2024 Oct 10 (Print Publication: 2024).
DOI:	10.3389/fpls.2024.1451784
Abstrakt:	Eucalyptus globulus Labill., is a recognized multipurpose tree, which stands out not only for the valuable qualities of its wood but also for the medicinal applications of the essential oil extracted from its leaves. In this study, we implemented an integrated strategy comprising genomic and phenomic approaches to predict foliar essential oil content, stem quality, and growth-related traits within a 9-year-old breeding population of E. globulus . The strategy involved evaluating Uni/Multi-trait deep learning (DL) models by incorporating genomic data related to single nucleotide polymorphisms (SNPs) and haplotypes, as well as the phenomic data from leaf near-infrared (NIR) spectroscopy. Our results showed that essential oil content (oil yield) ranged from 0.01 to 1.69% v/fw and had no significant correlation with any growth-related traits. This suggests that selection solely based on growth-related traits did n The emphases (colored text) from revisions were removed throughout the article. Confirm that this change is fine. ot influence the essential oil content. Genomic heritability estimates ranged from 0.25 (diameter at breast height (DBH) and oil yield) to 0.71 (DBH and stem straightness (ST)), while pedigree-based heritability exhibited a broader range, from 0.05 to 0.88. Notably, oil yield was found to be moderate to highly heritable, with genomic values ranging from 0.25 to 0.60, alongside a pedigree-based estimate of 0.48. The DL prediction models consistently achieved higher prediction accuracy (PA) values with a Multi-trait approach for most traits analyzed, including oil yield (0.699), tree height (0.772), DBH (0.745), slenderness coefficient (0.616), stem volume (0.757), and ST (0.764). The Uni-trait approach achieved superior PA values solely for branching quality (0.861). NIR spectral absorbance was the best omics data for CNN or MLP models with a Multi-trait approach. These results highlight considerable genetic variation within the Eucalyptus progeny trial, particularly regarding oil production. Our results contribute significantly to understanding omics-assisted deep learning models as a breeding strategy to improve growth-related traits and optimize essential oil production in this species. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Mieres-Castro, Maldonado and Mora-Poblete.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu