Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation.
Identifieur interne : 000291 ( Main/Exploration ); précédent : 000290; suivant : 000292Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation.
Auteurs : Marc J. Champigny [Canada] ; Faride Unda [Canada] ; Oleksandr Skyba [Canada] ; Raju Y. Soolanayakanahally [Canada] ; Shawn D. Mansfield [Canada] ; Malcolm M. Campbell [Canada]Source :
- Plant biotechnology journal [ 1467-7652 ] ; 2020.
Descripteurs français
- KwdFr :
- MESH :
- génétique : Méthylation de l'ADN, Populus.
- Apprentissage profond, Phénotype, Épigénomique.
English descriptors
- KwdEn :
- MESH :
- genetics : DNA Methylation, Populus.
- Deep Learning, Epigenome, Epigenomics, Phenotype.
Abstract
Epigenomes have remarkable potential for the estimation of plant traits. This study tested the hypothesis that natural variation in DNA methylation can be used to estimate industrially important traits in a genetically diverse population of Populus balsamifera L. (balsam poplar) trees grown at two common garden sites. Statistical learning experiments enabled by deep learning models revealed that plant traits in novel genotypes can be modelled transparently using small numbers of methylated DNA predictors. Using this approach, tissue type, a nonheritable attribute, from which DNA methylomes were derived was assigned, and provenance, a purely heritable trait and an element of population structure, was determined. Significant proportions of phenotypic variance in quantitative wood traits, including total biomass (57.5%), wood density (40.9%), soluble lignin (25.3%) and cell wall carbohydrate (mannose: 44.8%) contents, were also explained from natural variation in DNA methylation. Modelling plant traits using DNA methylation can capture tissue-specific epigenetic mechanisms underlying plant phenotypes in natural environments. DNA methylation-based models offer new insight into natural epigenetic influence on plants and can be used as a strategy to validate the identity, provenance or quality of agroforestry products.
DOI: 10.1111/pbi.13299
PubMed: 31742813
PubMed Central: PMC7207000
Affiliations:
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Mansfield</name><affiliation wicri:level="4"><nlm:affiliation>Department of Wood Science, University of British Columbia, Vancouver, BC, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Wood Science, University of British Columbia, Vancouver, BC</wicri:regionArea><orgName type="university">Université de la Colombie-Britannique</orgName><placeName><settlement type="city">Vancouver</settlement><region type="state">Colombie-Britannique </region></placeName></affiliation></author><author><name sortKey="Campbell, Malcolm M" sort="Campbell, Malcolm M" uniqKey="Campbell M" first="Malcolm M" last="Campbell">Malcolm M. Campbell</name><affiliation wicri:level="1"><nlm:affiliation>Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON</wicri:regionArea><wicri:noRegion>ON</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON</wicri:regionArea><wicri:noRegion>ON</wicri:noRegion></affiliation></author></analytic><series><title level="j">Plant biotechnology journal</title><idno type="eISSN">1467-7652</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>DNA Methylation (genetics)</term><term>Deep Learning (MeSH)</term><term>Epigenome (MeSH)</term><term>Epigenomics (MeSH)</term><term>Phenotype (MeSH)</term><term>Populus (genetics)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Apprentissage profond (MeSH)</term><term>Méthylation de l'ADN (génétique)</term><term>Phénotype (MeSH)</term><term>Populus (génétique)</term><term>Épigénomique (MeSH)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>DNA Methylation</term><term>Populus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Méthylation de l'ADN</term><term>Populus</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Deep Learning</term><term>Epigenome</term><term>Epigenomics</term><term>Phenotype</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Apprentissage profond</term><term>Phénotype</term><term>Épigénomique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Epigenomes have remarkable potential for the estimation of plant traits. This study tested the hypothesis that natural variation in DNA methylation can be used to estimate industrially important traits in a genetically diverse population of Populus balsamifera L. (balsam poplar) trees grown at two common garden sites. Statistical learning experiments enabled by deep learning models revealed that plant traits in novel genotypes can be modelled transparently using small numbers of methylated DNA predictors. Using this approach, tissue type, a nonheritable attribute, from which DNA methylomes were derived was assigned, and provenance, a purely heritable trait and an element of population structure, was determined. Significant proportions of phenotypic variance in quantitative wood traits, including total biomass (57.5%), wood density (40.9%), soluble lignin (25.3%) and cell wall carbohydrate (mannose: 44.8%) contents, were also explained from natural variation in DNA methylation. Modelling plant traits using DNA methylation can capture tissue-specific epigenetic mechanisms underlying plant phenotypes in natural environments. DNA methylation-based models offer new insight into natural epigenetic influence on plants and can be used as a strategy to validate the identity, provenance or quality of agroforestry products.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Automated" Owner="NLM"><PMID Version="1">31742813</PMID><DateCompleted><Year>2020</Year><Month>11</Month><Day>02</Day></DateCompleted><DateRevised><Year>2020</Year><Month>11</Month><Day>02</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1467-7652</ISSN><JournalIssue CitedMedium="Internet"><Volume>18</Volume><Issue>6</Issue><PubDate><Year>2020</Year><Month>06</Month></PubDate></JournalIssue><Title>Plant biotechnology journal</Title><ISOAbbreviation>Plant Biotechnol J</ISOAbbreviation></Journal><ArticleTitle>Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation.</ArticleTitle><Pagination><MedlinePgn>1361-1375</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/pbi.13299</ELocationID><Abstract><AbstractText>Epigenomes have remarkable potential for the estimation of plant traits. This study tested the hypothesis that natural variation in DNA methylation can be used to estimate industrially important traits in a genetically diverse population of Populus balsamifera L. (balsam poplar) trees grown at two common garden sites. Statistical learning experiments enabled by deep learning models revealed that plant traits in novel genotypes can be modelled transparently using small numbers of methylated DNA predictors. Using this approach, tissue type, a nonheritable attribute, from which DNA methylomes were derived was assigned, and provenance, a purely heritable trait and an element of population structure, was determined. Significant proportions of phenotypic variance in quantitative wood traits, including total biomass (57.5%), wood density (40.9%), soluble lignin (25.3%) and cell wall carbohydrate (mannose: 44.8%) contents, were also explained from natural variation in DNA methylation. Modelling plant traits using DNA methylation can capture tissue-specific epigenetic mechanisms underlying plant phenotypes in natural environments. DNA methylation-based models offer new insight into natural epigenetic influence on plants and can be used as a strategy to validate the identity, provenance or quality of agroforestry products.</AbstractText><CopyrightInformation>© 2019 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Champigny</LastName><ForeName>Marc J</ForeName><Initials>MJ</Initials><AffiliationInfo><Affiliation>Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Unda</LastName><ForeName>Faride</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Department of Wood Science, University of British Columbia, Vancouver, BC, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Skyba</LastName><ForeName>Oleksandr</ForeName><Initials>O</Initials><AffiliationInfo><Affiliation>Department of Wood Science, University of British Columbia, Vancouver, BC, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Soolanayakanahally</LastName><ForeName>Raju Y</ForeName><Initials>RY</Initials><AffiliationInfo><Affiliation>Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, SK, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mansfield</LastName><ForeName>Shawn D</ForeName><Initials>SD</Initials><Identifier Source="ORCID">0000-0002-0175-554X</Identifier><AffiliationInfo><Affiliation>Department of Wood Science, University of British Columbia, Vancouver, BC, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Campbell</LastName><ForeName>Malcolm M</ForeName><Initials>MM</Initials><Identifier Source="ORCID">0000-0002-1396-6651</Identifier><AffiliationInfo><Affiliation>Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><DataBankList CompleteYN="Y"><DataBank><DataBankName>GENBANK</DataBankName><AccessionNumberList><AccessionNumber>J02459.1</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>2019</Year><Month>12</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Plant Biotechnol J</MedlineTA><NlmUniqueID>101201889</NlmUniqueID><ISSNLinking>1467-7644</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D019175" MajorTopicYN="N">DNA Methylation</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000077321" MajorTopicYN="N">Deep Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000081122" MajorTopicYN="N">Epigenome</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D057890" MajorTopicYN="N">Epigenomics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010641" MajorTopicYN="N">Phenotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="Y">Populus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">authentication</Keyword><Keyword MajorTopicYN="Y">deep learning</Keyword><Keyword MajorTopicYN="Y">epigenomics</Keyword><Keyword 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Champigny</name></noRegion><name sortKey="Campbell, Malcolm M" sort="Campbell, Malcolm M" uniqKey="Campbell M" first="Malcolm M" last="Campbell">Malcolm M. Campbell</name><name sortKey="Campbell, Malcolm M" sort="Campbell, Malcolm M" uniqKey="Campbell M" first="Malcolm M" last="Campbell">Malcolm M. Campbell</name><name sortKey="Champigny, Marc J" sort="Champigny, Marc J" uniqKey="Champigny M" first="Marc J" last="Champigny">Marc J. Champigny</name><name sortKey="Mansfield, Shawn D" sort="Mansfield, Shawn D" uniqKey="Mansfield S" first="Shawn D" last="Mansfield">Shawn D. Mansfield</name><name sortKey="Skyba, Oleksandr" sort="Skyba, Oleksandr" uniqKey="Skyba O" first="Oleksandr" last="Skyba">Oleksandr Skyba</name><name sortKey="Soolanayakanahally, Raju Y" sort="Soolanayakanahally, Raju Y" uniqKey="Soolanayakanahally R" first="Raju Y" last="Soolanayakanahally">Raju Y. Soolanayakanahally</name><name sortKey="Unda, Faride" sort="Unda, Faride" uniqKey="Unda F" first="Faride" last="Unda">Faride Unda</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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