Identifying Hybridisation in <i>Pinus</i> Species Using near Infrared Spectroscopy of Foliage

Meder, Roger; Kain, Dominic; Ebdon, Nicholas; Brawner, Jeremy

doi:10.1255/jnirs.1127

Cited by 18 publications

(17 citation statements)

References 29 publications

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“…We thus propose to evaluate the efficiency of NIRS to make predictions with G-BLUP (or equivalently, RR-BLUP) using these traits instead of molecular markers. Numerous studies have demonstrated the usefulness of NIRS for barcoding samples and discriminating species or varieties (Bertrand et al 1985;Adedipe et al 2008;Espinoza et al 2012;Fischnaller et al 2012;Abasolo et al 2013;O'Reilly-Wapstra et al 2013;Meder et al 2014;Lang et al 2017) and have thus suggested that NIRS could be considered as a genetic marker (Cruickshank and Munck 2011) Moreover, some studies have shown that NIRS can capture some genetic variability by estimating the heritability of the spectrum and even mapping corresponding quantitative trait loci (QTL, O'Reilly-Wapstra et al 2013;Posada et al 2008;Diepeveen et al 2012;Hein and Chaix 2014). However, to the best of our knowledge, no studies have proposed using NIRS to perform "phenomic selection" (PS), which we define as the use of high-throughput phenotyping to obtain numerous variables which can be used as regressors or to estimate kinship in the statistical models classically used in GS.…”

Section: Introductionmentioning

confidence: 99%

Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar

Rincent

Charpentier

Faivre-Rampant

et al. 2018

Preprint

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Genomic selection -the prediction of breeding values using DNA polymorphisms -is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and nondestructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits and coined this new approach "phenomic selection" (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information. ARTICLE SUMMARY:Despite its widely adopted interest in breeding, genomic selection -the prediction of breeding values using DNA polymorphisms -remains difficult to implement for many species because of genotyping costs. As an alternative or complement depending on the context, we propose "phenomic selection" (PS) as the use of low-cost and high-throughput phenotypic RUNNING TITLE:The concept of phenomic selection

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Section: Introductionmentioning

confidence: 99%

Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar

Rincent

Charpentier

Faivre-Rampant

et al. 2018

Preprint

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“…Visible and near infrared (Vis-NIR) spectroscopy has been widely used in many sectors, including the food, material, and life sciences [7][8][9]. In the field of forestry, many studies have demonstrated its potential to determine components, such as moisture, density, lignin content, and so on; detect wood preservation; and classify species [10][11][12][13][14][15][16]. Sandak et al applied cluster analysis (CA) and principal component analysis (PCA) to classify powdered samples (fraction < 0.5 mm) and wood samples, respectively.…”

Section: Introductionmentioning

confidence: 99%

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Via

Young

et al. 2019

Forests

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This study aimed to rapidly and accurately identify geographical origin, tree species, and model wood density using visible and near infrared (Vis-NIR) spectroscopy coupled with chemometric methods. A total of 280 samples with two origins (Jilin and Heilongjiang province, China), and three species, Dahurian larch (Larix gmelinii (Rupr.) Rupr.), Japanese elm (Ulmus davidiana Planch. var. japonica Nakai), and Chinese white poplar (Populus tomentosa carriere), were collected for classification and prediction analysis. The spectral data were de-noised using lifting wavelet transform (LWT) and linear and nonlinear models were built from the de-noised spectra using partial least squares (PLS) and particle swarm optimization (PSO)-support vector machine (SVM) methods, respectively. The response surface methodology (RSM) was applied to analyze the best combined parameters of PSO-SVM. The PSO-SVM model was employed for discrimination of origin and species. The identification accuracy for tree species using wavelet coefficients were better than models developed using raw spectra, and the accuracy of geographical origin and species was greater than 98% for the prediction dataset. The prediction accuracy of density using wavelet coefficients was better than that of constructed spectra. The PSO-SVM models optimized by RSM obtained the best results with coefficients of determination of the calibration set of 0.953, 0.974, 0.959, and 0.837 for Dahurian larch, Japanese elm, Chinese white poplar (Jilin), and Chinese white poplar (Heilongjiang), respectively. The results showed the feasibility of Vis-NIR spectroscopy coupled with chemometric methods for determining wood property and geographical origin with simple, rapid, and non-destructive advantages.

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“…We thus propose to evaluate the efficiency of NIRS to make predictions with G-BLUP (or equivalently, RR-BLUP) using these traits instead of molecular markers. Numerous studies have demonstrated the usefulness of NIRS for barcoding samples and discriminating species or varieties (Bertrand et al 1985; Adedipe et al 2008; Espinoza et al 2012; Fischnaller et al 2012; Abasolo et al 2013; O’Reilly-Wapstra et al 2013; Meder et al 2014; Lang et al 2017) and have thus suggested that NIRS could be considered as a genetic marker (Cruickshank and Munck 2011). Moreover, some studies have shown that NIRS can capture some genetic variability by estimating the heritability of absorbances along the spectrum and even mapping corresponding quantitative trait loci (QTL, (Posada et al 2009; Diepeveen et al 2012; O’Reilly-Wapstra et al 2013; Hein and Chaix 2014)).…”

mentioning

confidence: 99%

Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar

Rincent

Charpentier²,

Faivre-Rampant

et al. 2018

G3 Genes|Genomes|Genetics

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176

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Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach ”phenomic selection” (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.

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Identifying Hybridisation in Pinus Species Using near Infrared Spectroscopy of Foliage

Cited by 18 publications

References 29 publications

Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar

Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar

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