Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar.
Identifieur interne : 000D49 ( Main/Exploration ); précédent : 000D48; suivant : 000D50Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar.
Auteurs : Renaud Rincent [France] ; Jean-Paul Charpentier [France] ; Patricia Faivre-Rampant [France] ; Etienne Paux [France] ; Jacques Le Gouis [France] ; Catherine Bastien [France] ; Vincent Segura [France]Source :
- G3 (Bethesda, Md.) [ 2160-1836 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- 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
Affiliations:
- France
- Auvergne (région administrative), Auvergne-Rhône-Alpes, Centre-Val de Loire, Région Centre, Île-de-France
- Clermont-Ferrand, Orléans, Évry (Essonne)
Links toward previous steps (curation, corpus...)
Le document en format XML
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administrative)</region><settlement type="city">Clermont-Ferrand</settlement></placeName></affiliation></author><author><name sortKey="Bastien, Catherine" sort="Bastien, Catherine" uniqKey="Bastien C" first="Catherine" last="Bastien">Catherine Bastien</name><affiliation wicri:level="3"><nlm:affiliation>BioForA, INRA, ONF, 45075 Orléans, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>BioForA, INRA, ONF, 45075 Orléans</wicri:regionArea><placeName><region type="region" nuts="2">Centre-Val de Loire</region><region type="old region" nuts="2">Région Centre</region><settlement type="city">Orléans</settlement></placeName></affiliation></author><author><name sortKey="Segura, Vincent" sort="Segura, Vincent" uniqKey="Segura V" first="Vincent" last="Segura">Vincent Segura</name><affiliation wicri:level="3"><nlm:affiliation>BioForA, INRA, ONF, 45075 Orléans, France vincent.segura@inra.fr.</nlm:affiliation><country wicri:rule="url">France</country><wicri:regionArea>BioForA, INRA, ONF, 45075 Orléans</wicri:regionArea><placeName><region type="region" nuts="2">Centre-Val de Loire</region><region type="old region" nuts="2">Région Centre</region><settlement type="city">Orléans</settlement></placeName></affiliation></author></analytic><series><title level="j">G3 (Bethesda, Md.)</title><idno type="eISSN">2160-1836</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Genotype (MeSH)</term><term>Plant Breeding (MeSH)</term><term>Populus (genetics)</term><term>Proof of Concept Study (MeSH)</term><term>Quantitative Trait, Heritable (MeSH)</term><term>Triticum (genetics)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Amélioration des plantes (MeSH)</term><term>Caractère quantitatif héréditaire (MeSH)</term><term>Génotype (MeSH)</term><term>Populus (génétique)</term><term>Triticum (génétique)</term><term>Étude de validation de principe (MeSH)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Populus</term><term>Triticum</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Populus</term><term>Triticum</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Genotype</term><term>Plant Breeding</term><term>Proof of Concept Study</term><term>Quantitative Trait, Heritable</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Amélioration des plantes</term><term>Caractère quantitatif héréditaire</term><term>Génotype</term><term>Étude de validation de principe</term></keywords></textClass></profileDesc></teiHeader><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></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30373914</PMID><DateCompleted><Year>2019</Year><Month>03</Month><Day>12</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2160-1836</ISSN><JournalIssue CitedMedium="Internet"><Volume>8</Volume><Issue>12</Issue><PubDate><Year>2018</Year><Month>12</Month><Day>10</Day></PubDate></JournalIssue><Title>G3 (Bethesda, Md.)</Title><ISOAbbreviation>G3 (Bethesda)</ISOAbbreviation></Journal><ArticleTitle>Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar.</ArticleTitle><Pagination><MedlinePgn>3961-3972</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1534/g3.118.200760</ELocationID><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></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rincent</LastName><ForeName>Renaud</ForeName><Initials>R</Initials><Identifier Source="ORCID">0000-0003-0885-0969</Identifier><AffiliationInfo><Affiliation>GDEC, INRA, UCA, 63000 Clermont-Ferrand, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Charpentier</LastName><ForeName>Jean-Paul</ForeName><Initials>JP</Initials><Identifier Source="ORCID">0000-0002-6029-0498</Identifier><AffiliationInfo><Affiliation>BioForA, INRA, ONF, 45075 Orléans, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>GenoBois analytical platform, INRA, 45075 Orléans, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Faivre-Rampant</LastName><ForeName>Patricia</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>EPGV, INRA, CEA-IG/CNG, 91057 Evry, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Paux</LastName><ForeName>Etienne</ForeName><Initials>E</Initials><Identifier Source="ORCID">0000-0002-3094-7129</Identifier><AffiliationInfo><Affiliation>GDEC, INRA, UCA, 63000 Clermont-Ferrand, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Le Gouis</LastName><ForeName>Jacques</ForeName><Initials>J</Initials><Identifier Source="ORCID">0000-0001-5726-4902</Identifier><AffiliationInfo><Affiliation>GDEC, INRA, UCA, 63000 Clermont-Ferrand, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bastien</LastName><ForeName>Catherine</ForeName><Initials>C</Initials><Identifier Source="ORCID">000-0002-9391-6637</Identifier><AffiliationInfo><Affiliation>BioForA, INRA, ONF, 45075 Orléans, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Segura</LastName><ForeName>Vincent</ForeName><Initials>V</Initials><Identifier Source="ORCID">0000-0003-1860-2256</Identifier><AffiliationInfo><Affiliation>BioForA, INRA, ONF, 45075 Orléans, France vincent.segura@inra.fr.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><DataBankList CompleteYN="Y"><DataBank><DataBankName>figshare</DataBankName><AccessionNumberList><AccessionNumber>10.25387/g3.7243256</AccessionNumber></AccessionNumberList></DataBank></DataBankList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>12</Month><Day>10</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>G3 (Bethesda)</MedlineTA><NlmUniqueID>101566598</NlmUniqueID><ISSNLinking>2160-1836</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D005838" MajorTopicYN="Y">Genotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069600" MajorTopicYN="Y">Plant Breeding</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000075082" MajorTopicYN="N">Proof of Concept Study</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019655" MajorTopicYN="Y">Quantitative Trait, Heritable</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014908" MajorTopicYN="N">Triticum</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">GenPred</Keyword><Keyword MajorTopicYN="Y">Genomic Prediction</Keyword><Keyword MajorTopicYN="Y">Near InfraRed Spectroscopy (NIRS)</Keyword><Keyword MajorTopicYN="Y">Poplar</Keyword><Keyword MajorTopicYN="Y">Shared Data Resources</Keyword><Keyword MajorTopicYN="Y">Wheat</Keyword><Keyword MajorTopicYN="Y">breeding</Keyword><Keyword MajorTopicYN="Y">endophenotypes</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>10</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>3</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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Renaud" uniqKey="Rincent R" first="Renaud" last="Rincent">Renaud Rincent</name></region><name sortKey="Bastien, Catherine" sort="Bastien, Catherine" uniqKey="Bastien C" first="Catherine" last="Bastien">Catherine Bastien</name><name sortKey="Charpentier, Jean Paul" sort="Charpentier, Jean Paul" uniqKey="Charpentier J" first="Jean-Paul" last="Charpentier">Jean-Paul Charpentier</name><name sortKey="Charpentier, Jean Paul" sort="Charpentier, Jean Paul" uniqKey="Charpentier J" first="Jean-Paul" last="Charpentier">Jean-Paul Charpentier</name><name sortKey="Faivre Rampant, Patricia" sort="Faivre Rampant, Patricia" uniqKey="Faivre Rampant P" first="Patricia" last="Faivre-Rampant">Patricia Faivre-Rampant</name><name sortKey="Le Gouis, Jacques" sort="Le Gouis, Jacques" uniqKey="Le Gouis J" first="Jacques" last="Le Gouis">Jacques Le Gouis</name><name sortKey="Paux, Etienne" sort="Paux, Etienne" uniqKey="Paux E" first="Etienne" last="Paux">Etienne Paux</name><name sortKey="Segura, Vincent" sort="Segura, Vincent" uniqKey="Segura V" first="Vincent" last="Segura">Vincent Segura</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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