Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation
Summary 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...
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| Veröffentlicht in: | Plant biotechnology journal 2020-06, Vol.18 (6), p.1361-1375 |
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| container_title | Plant biotechnology journal |
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| creator | Champigny, Marc J. Unda, Faride Skyba, Oleksandr Soolanayakanahally, Raju Y. Mansfield, Shawn D. Campbell, Malcolm M. |
| description | Summary
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_str_mv | 10.1111/pbi.13299 |
| format | Article |
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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.</description><identifier>ISSN: 1467-7644</identifier><identifier>EISSN: 1467-7652</identifier><identifier>DOI: 10.1111/pbi.13299</identifier><identifier>PMID: 31742813</identifier><language>eng</language><publisher>England: John Wiley & Sons, Inc</publisher><subject>Agriculture ; Agroforestry ; authentication ; Biomass ; Carbohydrates ; Cell walls ; Deep learning ; Deoxyribonucleic acid ; DNA ; DNA methylation ; Epigenetics ; epigenomics ; Gardens & gardening ; Genomes ; Genomics ; Genotype & phenotype ; Genotypes ; Hardwoods ; Lignin ; Machine learning ; Mannose ; Natural environment ; Neural networks ; Phenotypes ; Phenotypic variations ; Plant diseases ; Poplar ; Population genetics ; Population structure ; Populus balsamifera ; Provenance ; Statistical analysis ; Trees ; Wood</subject><ispartof>Plant biotechnology journal, 2020-06, Vol.18 (6), p.1361-1375</ispartof><rights>2019 The Authors. published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.</rights><rights>2019 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.</rights><rights>2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4439-76d319f3edefbd93f22cec9a34720a7f377396fa75ff0844301ac29d82dd4ce53</citedby><cites>FETCH-LOGICAL-c4439-76d319f3edefbd93f22cec9a34720a7f377396fa75ff0844301ac29d82dd4ce53</cites><orcidid>0000-0002-0175-554X ; 0000-0002-1396-6651</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fpbi.13299$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fpbi.13299$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,860,881,1411,11542,27903,27904,45553,45554,46030,46454</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31742813$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Champigny, Marc J.</creatorcontrib><creatorcontrib>Unda, Faride</creatorcontrib><creatorcontrib>Skyba, Oleksandr</creatorcontrib><creatorcontrib>Soolanayakanahally, Raju Y.</creatorcontrib><creatorcontrib>Mansfield, Shawn D.</creatorcontrib><creatorcontrib>Campbell, Malcolm M.</creatorcontrib><title>Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation</title><title>Plant biotechnology journal</title><addtitle>Plant Biotechnol J</addtitle><description>Summary
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.</description><subject>Agriculture</subject><subject>Agroforestry</subject><subject>authentication</subject><subject>Biomass</subject><subject>Carbohydrates</subject><subject>Cell walls</subject><subject>Deep learning</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA methylation</subject><subject>Epigenetics</subject><subject>epigenomics</subject><subject>Gardens & gardening</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Genotype & phenotype</subject><subject>Genotypes</subject><subject>Hardwoods</subject><subject>Lignin</subject><subject>Machine learning</subject><subject>Mannose</subject><subject>Natural environment</subject><subject>Neural networks</subject><subject>Phenotypes</subject><subject>Phenotypic variations</subject><subject>Plant diseases</subject><subject>Poplar</subject><subject>Population genetics</subject><subject>Population structure</subject><subject>Populus balsamifera</subject><subject>Provenance</subject><subject>Statistical analysis</subject><subject>Trees</subject><subject>Wood</subject><issn>1467-7644</issn><issn>1467-7652</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kc9uVCEUh2-Mja3VhS9gSNzoYlr-3cvgwqRWbZtMtAtdEwYOUxou3MK9mnkBn1umM51YE9lA4Dsfv5PTNK8IPiF1nQ5Lf0IYlfJJc0R4J2aia-nT_Znzw-Z5KbcYU9K13bPmkBHB6Zywo-b3AnSOPq6Qy6lHPYw365B6KO8RDH4FMfXeIJNyhqBHKCg5dJ2GKUwFLXUouvcOskZj1n7cXBWwKEVkAQYUHtx9shDua6Mep6wD-vT1bPeZHn2KL5oDV23wcrcfNz--fP5-fjlbfLu4Oj9bzAznTNZeLCPSMbDgllYyR6kBIzXjgmItHBOCyc5p0TqH57UEE22otHNqLTfQsuPmw9Y7TMserIFYgwc1ZN_rvFZJe_X4JfobtUo_VfULjHEVvN0JcrqboIyq98VACDpCmoqijLRScExIRd_8g96mKcfanqIcd1xILDeJ3m0pk1MpGdw-DMFqM11Vp6vup1vZ13-n35MP46zA6Rb45QOs_29S1x-vtso_a_Sx1A</recordid><startdate>202006</startdate><enddate>202006</enddate><creator>Champigny, Marc J.</creator><creator>Unda, Faride</creator><creator>Skyba, Oleksandr</creator><creator>Soolanayakanahally, Raju Y.</creator><creator>Mansfield, Shawn D.</creator><creator>Campbell, Malcolm M.</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>LK8</scope><scope>M7P</scope><scope>M7S</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0175-554X</orcidid><orcidid>https://orcid.org/0000-0002-1396-6651</orcidid></search><sort><creationdate>202006</creationdate><title>Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation</title><author>Champigny, Marc J. ; Unda, Faride ; Skyba, Oleksandr ; Soolanayakanahally, Raju Y. ; Mansfield, Shawn D. ; Campbell, Malcolm M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4439-76d319f3edefbd93f22cec9a34720a7f377396fa75ff0844301ac29d82dd4ce53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Agriculture</topic><topic>Agroforestry</topic><topic>authentication</topic><topic>Biomass</topic><topic>Carbohydrates</topic><topic>Cell walls</topic><topic>Deep learning</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA methylation</topic><topic>Epigenetics</topic><topic>epigenomics</topic><topic>Gardens & gardening</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Genotype & phenotype</topic><topic>Genotypes</topic><topic>Hardwoods</topic><topic>Lignin</topic><topic>Machine learning</topic><topic>Mannose</topic><topic>Natural environment</topic><topic>Neural networks</topic><topic>Phenotypes</topic><topic>Phenotypic variations</topic><topic>Plant diseases</topic><topic>Poplar</topic><topic>Population genetics</topic><topic>Population structure</topic><topic>Populus balsamifera</topic><topic>Provenance</topic><topic>Statistical analysis</topic><topic>Trees</topic><topic>Wood</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Champigny, Marc J.</creatorcontrib><creatorcontrib>Unda, Faride</creatorcontrib><creatorcontrib>Skyba, Oleksandr</creatorcontrib><creatorcontrib>Soolanayakanahally, Raju Y.</creatorcontrib><creatorcontrib>Mansfield, Shawn D.</creatorcontrib><creatorcontrib>Campbell, Malcolm M.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Free Content</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Plant biotechnology journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Champigny, Marc J.</au><au>Unda, Faride</au><au>Skyba, Oleksandr</au><au>Soolanayakanahally, Raju Y.</au><au>Mansfield, Shawn D.</au><au>Campbell, Malcolm M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation</atitle><jtitle>Plant biotechnology journal</jtitle><addtitle>Plant Biotechnol J</addtitle><date>2020-06</date><risdate>2020</risdate><volume>18</volume><issue>6</issue><spage>1361</spage><epage>1375</epage><pages>1361-1375</pages><issn>1467-7644</issn><eissn>1467-7652</eissn><abstract>Summary
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.</abstract><cop>England</cop><pub>John Wiley & Sons, Inc</pub><pmid>31742813</pmid><doi>10.1111/pbi.13299</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-0175-554X</orcidid><orcidid>https://orcid.org/0000-0002-1396-6651</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Agriculture Agroforestry authentication Biomass Carbohydrates Cell walls Deep learning Deoxyribonucleic acid DNA DNA methylation Epigenetics epigenomics Gardens & gardening Genomes Genomics Genotype & phenotype Genotypes Hardwoods Lignin Machine learning Mannose Natural environment Neural networks Phenotypes Phenotypic variations Plant diseases Poplar Population genetics Population structure Populus balsamifera Provenance Statistical analysis Trees Wood |
| title | Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation |
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