Heritable variation of foliar spectral reflectance enhances genomic prediction of hydrogen cyanide in a genetically structured population of eucalyptus

dc.contributor.authorBallesta, Paulina
dc.contributor.authorAhmar, Sunny
dc.contributor.authorLobos, Gustavo A.
dc.contributor.authorMieres-Castro, Daniel
dc.contributor.authorJiménez-Aspee, Felipe
dc.contributor.authorMora-Poblete, Freddy
dc.date.accessioned2024-10-23T12:25:50Z
dc.date.available2024-10-23T12:25:50Z
dc.date.issued2022de
dc.description.abstractPlants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability (hg2=0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 (hg2=0.58), followed by the anthocyanin reflectance index and the Browning reflectance index (hg2=0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.en
dc.identifier.swb1807291863
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16809
dc.identifier.urihttps://doi.org/10.3389/fpls.2022.871943
dc.language.isoengde
dc.rights.licensecc_byde
dc.source1664-462Xde
dc.sourceFrontiers in plant science; Vol. 13 (2022) 871943de
dc.subjectGenomic and phenomic prediction
dc.subjectGenomic heritability
dc.subjectDefense chemistry
dc.subjectSpectral reflectance indexes
dc.subjectSpectroradiometer
dc.subjectSpecialized metabolite
dc.subject.ddc580
dc.titleHeritable variation of foliar spectral reflectance enhances genomic prediction of hydrogen cyanide in a genetically structured population of eucalyptusen
dc.type.diniArticle
dcterms.bibliographicCitationFrontiers in plant science, 13 (2022), 871943. https://doi.org/10.3389/fpls.2022.871943. ISSN: 1664-462X
dcterms.bibliographicCitation.issn1664-462X
dcterms.bibliographicCitation.journaltitleFrontiers in plant science
dcterms.bibliographicCitation.volume13
local.export.bibtex@article{Ballesta2022, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16809}, doi = {10.3389/fpls.2022.871943}, author = {Ballesta, Paulina and Ahmar, Sunny and Lobos, Gustavo A. et al.}, title = {Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus}, journal = {Frontiers in plant science}, year = {2022}, volume = {13}, }
local.export.bibtexAuthorBallesta, Paulina and Ahmar, Sunny and Lobos, Gustavo A. et al.
local.export.bibtexKeyBallesta2022
local.export.bibtexType@article

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