PoplarV1, Main, Corpus, bibRecord, 000B96

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

Identifieur interne : 000B96 ( Main/Corpus ); précédent : 000B95; suivant : 000B97

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

Auteurs : Renaud Rincent ; Jean-Paul Charpentier ; Patricia Faivre-Rampant ; Etienne Paux ; Jacques Le Gouis ; Catherine Bastien ; Vincent Segura

Source :

G3 (Bethesda, Md.) [ 2160-1836 ] ; 2018.

RBID : pubmed:30373914

English descriptors

KwdEn :
- Genotype (MeSH), Plant Breeding (MeSH), Populus (genetics), Proof of Concept Study (MeSH), Quantitative Trait, Heritable (MeSH), Triticum (genetics).
MESH :
- genetics : Populus, Triticum.
- Genotype, Plant Breeding, Proof of Concept Study, Quantitative Trait, Heritable.

Abstract

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.

DOI: 10.1534/g3.118.200760
PubMed: 30373914
PubMed Central: PMC6288839

Links to Exploration step

pubmed:30373914

Le document en format XML

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<front><div type="abstract" xml:lang="en">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 (<i>Triticum aestivum</i>
 L. and <i>Populus nigra</i>
 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.</div>
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<Abstract><AbstractText>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 (<i>Triticum aestivum</i>
 L. and <i>Populus nigra</i>
 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.</AbstractText>
<CopyrightInformation>Copyright © 2018 Rincent et al.</CopyrightInformation>
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<Reference><Citation>BMC Genomics. 2018 May 21;19(1):371</Citation>
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   |texte=   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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Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar.

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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