Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

Gonçalves, Mateus Teles Vital; Morota, Gota; Costa, Paulo Mafra de Almeida; Vidigal, Pedro Marcus Pereira; Barbosa, Márcio Henrique Pereira; Peternelli, Luiz Alexandre

doi:10.1371/journal.pone.0236853

Cited by 12 publications

(8 citation statements)

References 114 publications

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“…However, the model combining spectral reflectance, pedigree, and haplotype-based genomic data (Model 13) was not superior to Model 7 (in terms of its prediction ability). Gonçalves et al (2021) reported that the prediction ability based on the spectral reflectance of the fiber and sucrose content of sugarcane stems was superior to that of the model based on genomic data alone. However, the combined use of spectral reflectance and genomic data did not significantly increase the prediction ability of the studied traits.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies have found that Bayesian regression models, such as Bayes B, have been more accurate than the PLS method ( Solberg et al, 2009 ; Ferragina et al, 2015 ). Moreover, Gonçalves et al (2021) , reported that the Bayes B method was up to approximately two times more accurate than PLS in predicting fiber and sucrose con-tent in sugarcane stems. According to several studies ( Kainer et al, 2018 ; Thistlethwaite et al, 2019 ; Rio et al, 2021 ), the Bayesian methods, such as Bayes B, can improve the predictive ability in genome -based evaluations.…”

Section: Discussionmentioning

confidence: 99%

“…where,

is the incidence matrix associated to

vector, which corresponds to the vector of wavelength effects ( Gonçalves et al, 2021 ). The prediction Model 8 comprised the genomic and wavelength effects:…”

Section: Methodsmentioning

confidence: 99%

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Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus

Ballesta¹,

Ahmar²,

Lobos

et al. 2022

Front. Plant Sci.

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Plants 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus

Ballesta¹,

Ahmar²,

Lobos

et al. 2022

Front. Plant Sci.

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“…Near-infrared reflectance spectroscopy (NIRS) data composed of phenomics information have been used as predictors to compare its predictive ability with marker data [ 155 ]. The phenomics study via NIRS has been shown to achieve promising predictive abilities in crops, including soybean [ 156 ], maize [ 157 ] and sugarcane [ 158 ]. HTP, on the other hand, relies on automated trait analysis in producing phenotypic data, such as imaging techniques.…”

Section: The Omics-platformmentioning

confidence: 99%

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

Abdullah‐Zawawi

Govender

Harun

et al. 2022

Plants

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In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

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“…Thus, the use of spectral data as predictors could drastically increase the efficiency of selection at greatly reduced costs. Phenomic prediction based on NIRS or field-based hyperspectral data was reported for different crops and traits and shown to achieve promising predictive abilities, for example, in soybean (Parmley et al 2019 ), maize (Lane et al 2020 ), wheat (Rincent et al 2018 ; Krause et al 2019 ), rye (Galán et al 2020 , 2021 ), and sugarcane (Gonçalves et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

The performance of phenomic selection depends on the genetic architecture of the target trait

et al. 2021

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Key message The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Abstract Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.

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Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

Cited by 12 publications

References 114 publications

Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus

Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

The performance of phenomic selection depends on the genetic architecture of the target trait

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