What question did this study set out to answer?

The aim is to utilize multi-omics data to understand genetic and metabolic factors influencing terpene and phenolic compound production in Eucalyptus globulus.

March 25, 2026BMC Plant Biology0 citationsOpen Access

Multi-omics prediction of terpene constituents and phenolic traits in Eucalyptus globulus using Bayesian models and tree-based machine learning

DMDaniel Mieres-CastroUniversity of La Serena CMCarlos MaldonadoUniversidad Mayor FMFreddy Mora-PobleteUniversity of Talca

Key Points

The aim is to utilize multi-omics data to understand genetic and metabolic factors influencing terpene and phenolic compound production in Eucalyptus globulus.
Implemented a multi-omics framework integrating SNP data and phenomic datasets.
Used GC–MS and UHPLC–QqTOF–MS2 for compound analysis.
Applied Bayesian and tree-based machine learning models for predictive accuracy.
Conducted feature selection to identify markers associated with metabolic traits.
1,8-cineole and α-pinene were the most abundant terpenes identified.
High heritability estimates for methanol extract yield and total phenolics indicated strong genetic control.
Bayesian models achieved the highest prediction accuracy for α-terpineol (0.717).
Identified pleiotropic markers linked to traits such as photosynthesis and metabolic regulation.

Abstract

Terpenes and phenolic compounds are multifunctional plant metabolites that contribute to defense, signaling, and ecological interactions, while supporting industrial applications. Here, we implemented an integrative multi-omics framework to dissect the genetic and metabolic architecture of biobased compound production in a Eucalyptus globulus breeding population, a tree species of global economic importance. The dataset comprised 14,442 high-confidence SNPs genotyped using the EUChip60K array and 3,279 haplotype blocks, complemented by phenomic datasets derived from near-infrared (NIR) spectral absorbance (~ 900–2,500 nm) and pigment-related indices (chlorophyll, anthocyanins, flavonols, and nitrogen balance index). Gas chromatography-mass spectrometry (GC–MS) and ultra-high-performance liquid chromatography-quadrupole-time-of-flight tandem MS (UHPLC–QqTOF–MS2) revealed substantial inter-individual variation: essential oils were dominated by 1,8-cineole (42.70% ± 7.19%) and α-pinene (38.02% ± 5.20%), while phenolic extracts were enriched in phloroglucinol derivatives (92.67% ± 1.59%), notably the macrocarpal C isomer (12.98% ± 1.48%). Total additive heritability integrating SNP- and pedigree-based effects was high for MeOH extract yield (h2 = 0.721), total phenolics (h2 = 0.646), α-terpineol (h2 = 0.718), and 1,8-cineole (h2 = 0.658), indicating strong genetic control. Predictive modeling revealed that haplotype-based Bayesian approaches outperformed SNP- and phenomic-based models, with the highest accuracies for α-terpineol (prediction accuracy = 0.717), α-pinene (prediction accuracy = 0.554), and methanolic extract yield (prediction accuracy = 0.624). Feature selection identified pleiotropic markers (e.g., SNP12778, HAP205, HAP272, NIR2429) co-localized with genes involved in photosynthesis, signaling, and metabolic regulation. These findings lay the foundation for omics-assisted E. globulus breeding aimed at generating elite genotypes for large-scale production of high-value bioactive compounds with practical applications in health, agriculture, and bioenergy.

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