Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits.
The 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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0236853 |
| _version_ | 1818719448943558656 |
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| author | Mateus Teles Vital Gonçalves Gota Morota Paulo Mafra de Almeida Costa Pedro Marcus Pereira Vidigal Marcio Henrique Pereira Barbosa Luiz Alexandre Peternelli |
| author_facet | Mateus Teles Vital Gonçalves Gota Morota Paulo Mafra de Almeida Costa Pedro Marcus Pereira Vidigal Marcio Henrique Pereira Barbosa Luiz Alexandre Peternelli |
| author_sort | Mateus Teles Vital Gonçalves |
| collection | DOAJ |
| description | The 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. |
| first_indexed | 2024-12-17T20:07:07Z |
| format | Article |
| id | doaj.art-0218be5f7d36438b86a52f13e6a060a0 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T20:07:07Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0218be5f7d36438b86a52f13e6a060a02022-12-21T21:34:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e023685310.1371/journal.pone.0236853Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits.Mateus Teles Vital GonçalvesGota MorotaPaulo Mafra de Almeida CostaPedro Marcus Pereira VidigalMarcio Henrique Pereira BarbosaLuiz Alexandre PeternelliThe 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.https://doi.org/10.1371/journal.pone.0236853 |
| spellingShingle | Mateus Teles Vital Gonçalves Gota Morota Paulo Mafra de Almeida Costa Pedro Marcus Pereira Vidigal Marcio Henrique Pereira Barbosa Luiz Alexandre Peternelli Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. PLoS ONE |
| title | Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. |
| title_full | Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. |
| title_fullStr | Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. |
| title_full_unstemmed | Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. |
| title_short | Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. |
| title_sort | near infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits |
| url | https://doi.org/10.1371/journal.pone.0236853 |
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