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dc.contributor.authorVital Goncalves, Mateus Telesen
dc.contributor.authorMorota, Gotaen
dc.contributor.authorde Almeida Costa, Paulo Mafraen
dc.contributor.authorPereira Vidigal, Pedro Marcusen
dc.contributor.authorPereira Barbosa, Marcio Henriqueen
dc.contributor.authorPeternelli, Luiz Alexandreen
dc.date.accessioned2021-04-27T14:36:52Zen
dc.date.available2021-04-27T14:36:52Zen
dc.date.issued2021-03-04en
dc.identifier.issn1932-6203en
dc.identifier.othere0236853en
dc.identifier.urihttp://hdl.handle.net/10919/103149en
dc.description.abstractThe main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.en
dc.description.sponsorshipConselho Nacional de Desenvolvimento Cienti'fico e Tecnologico (CNPq)National Council for Scientific and Technological Development (CNPq) [154611/2017-4]; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES)CAPES [001]; Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG)Minas Gerais State Research Foundation (FAPEMIG); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)National Council for Scientific and Technological Development (CNPq) [310503/2015-9]en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleNear-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traitsen
dc.typeArticle - Refereeden
dc.contributor.departmentAnimal and Poultry Sciencesen
dc.description.notesMTVG received a masters degree scholarship (154611/2017-4) from the Conselho Nacional de Desenvolvimento Cienti ' fico e Tecnologico (CNPq). This work was supported by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001, the Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG), and the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) - Grant Number 310503/2015-9 to LAP. We are also thankful for the Inter-University Network for the Development of Sugarcane Industry (RIDESA) for all the field experiment support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.title.serialPlos Oneen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0236853en
dc.identifier.volume16en
dc.identifier.issue3en
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen
dc.identifier.pmid33661948en


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Creative Commons Attribution 4.0 International
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