Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model.
Identifieur interne : 003302 ( PubMed/Corpus ); précédent : 003301; suivant : 003303Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model.
Auteurs : Matthieu Bogard ; Catherine Ravel ; Etienne Paux ; Jacques Bordes ; François Balfourier ; Scott C. Chapman ; Jacques Le Gouis ; Vincent AllardSource :
- Journal of experimental botany [ 1460-2431 ] ; 2014.
English descriptors
- KwdEn :
- Adaptation, Physiological, Breeding, Environment, Flowers (genetics), Flowers (physiology), Flowers (radiation effects), Genome, Plant (genetics), Genotype, Models, Biological, Phenotype, Photoperiod, Quantitative Trait Loci (genetics), Time Factors, Triticum (genetics), Triticum (physiology), Triticum (radiation effects).
- MESH :
- genetics : Flowers, Genome, Plant, Quantitative Trait Loci, Triticum.
- physiology : Flowers, Triticum.
- radiation effects : Flowers, Triticum.
- Adaptation, Physiological, Breeding, Environment, Genotype, Models, Biological, Phenotype, Photoperiod, Time Factors.
Abstract
Prediction of wheat phenology facilitates the selection of cultivars with specific adaptations to a particular environment. However, while QTL analysis for heading date can identify major genes controlling phenology, the results are limited to the environments and genotypes tested. Moreover, while ecophysiological models allow accurate predictions in new environments, they may require substantial phenotypic data to parameterize each genotype. Also, the model parameters are rarely related to all underlying genes, and all the possible allelic combinations that could be obtained by breeding cannot be tested with models. In this study, a QTL-based model is proposed to predict heading date in bread wheat (Triticum aestivum L.). Two parameters of an ecophysiological model (V sat and P base , representing genotype vernalization requirements and photoperiod sensitivity, respectively) were optimized for 210 genotypes grown in 10 contrasting location × sowing date combinations. Multiple linear regression models predicting V sat and P base with 11 and 12 associated genetic markers accounted for 71 and 68% of the variance of these parameters, respectively. QTL-based V sat and P base estimates were able to predict heading date of an independent validation data set (88 genotypes in six location × sowing date combinations) with a root mean square error of prediction of 5 to 8.6 days, explaining 48 to 63% of the variation for heading date. The QTL-based model proposed in this study may be used for agronomic purposes and to assist breeders in suggesting locally adapted ideotypes for wheat phenology.
DOI: 10.1093/jxb/eru328
PubMed: 25148833
Links to Exploration step
pubmed:25148833Le document en format XML
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Chapman</name><affiliation><nlm:affiliation>CSIRO, Queensland Bioscience Precinct - St Lucia, 306 Carmody Road, St Lucia QLD 4067, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Le Gouis, Jacques" sort="Le Gouis, Jacques" uniqKey="Le Gouis J" first="Jacques" last="Le Gouis">Jacques Le Gouis</name><affiliation><nlm:affiliation>INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, F-63039 Clermont-Ferrand, France Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, F-63177 Aubière Cedex, France.</nlm:affiliation></affiliation></author><author><name sortKey="Allard, Vincent" sort="Allard, Vincent" uniqKey="Allard V" first="Vincent" last="Allard">Vincent Allard</name><affiliation><nlm:affiliation>INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, F-63039 Clermont-Ferrand, France Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, F-63177 Aubière Cedex, France vincent.allard@clermont.inra.fr.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Journal of experimental botany</title><idno type="eISSN">1460-2431</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adaptation, Physiological</term><term>Breeding</term><term>Environment</term><term>Flowers (genetics)</term><term>Flowers (physiology)</term><term>Flowers (radiation effects)</term><term>Genome, Plant (genetics)</term><term>Genotype</term><term>Models, Biological</term><term>Phenotype</term><term>Photoperiod</term><term>Quantitative Trait Loci (genetics)</term><term>Time Factors</term><term>Triticum (genetics)</term><term>Triticum (physiology)</term><term>Triticum (radiation effects)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Flowers</term><term>Genome, Plant</term><term>Quantitative Trait Loci</term><term>Triticum</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Flowers</term><term>Triticum</term></keywords><keywords scheme="MESH" qualifier="radiation effects" xml:lang="en"><term>Flowers</term><term>Triticum</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adaptation, Physiological</term><term>Breeding</term><term>Environment</term><term>Genotype</term><term>Models, Biological</term><term>Phenotype</term><term>Photoperiod</term><term>Time Factors</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Prediction of wheat phenology facilitates the selection of cultivars with specific adaptations to a particular environment. However, while QTL analysis for heading date can identify major genes controlling phenology, the results are limited to the environments and genotypes tested. Moreover, while ecophysiological models allow accurate predictions in new environments, they may require substantial phenotypic data to parameterize each genotype. Also, the model parameters are rarely related to all underlying genes, and all the possible allelic combinations that could be obtained by breeding cannot be tested with models. In this study, a QTL-based model is proposed to predict heading date in bread wheat (Triticum aestivum L.). Two parameters of an ecophysiological model (V sat and P base , representing genotype vernalization requirements and photoperiod sensitivity, respectively) were optimized for 210 genotypes grown in 10 contrasting location × sowing date combinations. Multiple linear regression models predicting V sat and P base with 11 and 12 associated genetic markers accounted for 71 and 68% of the variance of these parameters, respectively. QTL-based V sat and P base estimates were able to predict heading date of an independent validation data set (88 genotypes in six location × sowing date combinations) with a root mean square error of prediction of 5 to 8.6 days, explaining 48 to 63% of the variation for heading date. The QTL-based model proposed in this study may be used for agronomic purposes and to assist breeders in suggesting locally adapted ideotypes for wheat phenology.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25148833</PMID><DateCreated><Year>2014</Year><Month>10</Month><Day>21</Day></DateCreated><DateCompleted><Year>2015</Year><Month>07</Month><Day>08</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1460-2431</ISSN><JournalIssue CitedMedium="Internet"><Volume>65</Volume><Issue>20</Issue><PubDate><Year>2014</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Journal of experimental botany</Title><ISOAbbreviation>J. Exp. Bot.</ISOAbbreviation></Journal><ArticleTitle>Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model.</ArticleTitle><Pagination><MedlinePgn>5849-65</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/jxb/eru328</ELocationID><Abstract><AbstractText>Prediction of wheat phenology facilitates the selection of cultivars with specific adaptations to a particular environment. However, while QTL analysis for heading date can identify major genes controlling phenology, the results are limited to the environments and genotypes tested. Moreover, while ecophysiological models allow accurate predictions in new environments, they may require substantial phenotypic data to parameterize each genotype. Also, the model parameters are rarely related to all underlying genes, and all the possible allelic combinations that could be obtained by breeding cannot be tested with models. In this study, a QTL-based model is proposed to predict heading date in bread wheat (Triticum aestivum L.). Two parameters of an ecophysiological model (V sat and P base , representing genotype vernalization requirements and photoperiod sensitivity, respectively) were optimized for 210 genotypes grown in 10 contrasting location × sowing date combinations. Multiple linear regression models predicting V sat and P base with 11 and 12 associated genetic markers accounted for 71 and 68% of the variance of these parameters, respectively. QTL-based V sat and P base estimates were able to predict heading date of an independent validation data set (88 genotypes in six location × sowing date combinations) with a root mean square error of prediction of 5 to 8.6 days, explaining 48 to 63% of the variation for heading date. The QTL-based model proposed in this study may be used for agronomic purposes and to assist breeders in suggesting locally adapted ideotypes for wheat phenology.</AbstractText><CopyrightInformation>© The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Bogard</LastName><ForeName>Matthieu</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, F-63039 Clermont-Ferrand, France Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, F-63177 Aubière Cedex, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ravel</LastName><ForeName>Catherine</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, F-63039 Clermont-Ferrand, France Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, F-63177 Aubière Cedex, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Paux</LastName><ForeName>Etienne</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, F-63039 Clermont-Ferrand, France Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, F-63177 Aubière Cedex, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bordes</LastName><ForeName>Jacques</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, F-63039 Clermont-Ferrand, France Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, F-63177 Aubière Cedex, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Balfourier</LastName><ForeName>François</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, F-63039 Clermont-Ferrand, France Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, F-63177 Aubière Cedex, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chapman</LastName><ForeName>Scott C</ForeName><Initials>SC</Initials><AffiliationInfo><Affiliation>CSIRO, Queensland Bioscience Precinct - St Lucia, 306 Carmody Road, St Lucia QLD 4067, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Le Gouis</LastName><ForeName>Jacques</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, F-63039 Clermont-Ferrand, France Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, F-63177 Aubière Cedex, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Allard</LastName><ForeName>Vincent</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, F-63039 Clermont-Ferrand, France Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, F-63177 Aubière Cedex, France vincent.allard@clermont.inra.fr.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>08</Month><Day>22</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Exp 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effects</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018745" MajorTopicYN="N">Genome, Plant</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005838" MajorTopicYN="N">Genotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008954" MajorTopicYN="N">Models, Biological</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010641" MajorTopicYN="N">Phenotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017440" MajorTopicYN="N">Photoperiod</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D040641" MajorTopicYN="N">Quantitative Trait Loci</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013997" MajorTopicYN="N">Time Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014908" MajorTopicYN="N">Triticum</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName><QualifierName UI="Q000528" MajorTopicYN="N">radiation effects</QualifierName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4203124</OtherID><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Association genetics</Keyword><Keyword MajorTopicYN="N">ecophysiological model</Keyword><Keyword MajorTopicYN="N">gene-based modelling</Keyword><Keyword MajorTopicYN="N">heading date</Keyword><Keyword MajorTopicYN="N">phenology</Keyword><Keyword MajorTopicYN="N">wheat.</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>8</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>8</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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