SNPeffect: identifying functional roles of SNPs using metabolic networks.
Identifieur interne : 000137 ( Main/Exploration ); précédent : 000136; suivant : 000138SNPeffect: identifying functional roles of SNPs using metabolic networks.
Auteurs : Debolina Sarkar [États-Unis] ; Costas D. Maranas [États-Unis]Source :
- The Plant journal : for cell and molecular biology [ 1365-313X ] ; 2020.
Abstract
Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.
DOI: 10.1111/tpj.14746
PubMed: 32167625
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Maranas</name><affiliation wicri:level="4"><nlm:affiliation>Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Chemical Engineering, Pennsylvania State University, University Park, PA</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">University Park (Pennsylvanie)</settlement></placeName><orgName type="university">Université d'État de Pennsylvanie</orgName></affiliation></author></analytic><series><title level="j">The Plant journal : for cell and molecular biology</title><idno type="eISSN">1365-313X</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">32167625</PMID><DateRevised><Year>2020</Year><Month>08</Month><Day>31</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1365-313X</ISSN><JournalIssue CitedMedium="Internet"><Volume>103</Volume><Issue>2</Issue><PubDate><Year>2020</Year><Month>07</Month></PubDate></JournalIssue><Title>The Plant journal : for cell and molecular biology</Title><ISOAbbreviation>Plant J</ISOAbbreviation></Journal><ArticleTitle>SNPeffect: identifying functional roles of SNPs using metabolic networks.</ArticleTitle><Pagination><MedlinePgn>512-531</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/tpj.14746</ELocationID><Abstract><AbstractText>Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.</AbstractText><CopyrightInformation>© 2020 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sarkar</LastName><ForeName>Debolina</ForeName><Initials>D</Initials><Identifier Source="ORCID">0000-0002-4976-5536</Identifier><AffiliationInfo><Affiliation>Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Maranas</LastName><ForeName>Costas D</ForeName><Initials>CD</Initials><Identifier Source="ORCID">0000-0002-1508-1398</Identifier><AffiliationInfo><Affiliation>Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>04</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Plant J</MedlineTA><NlmUniqueID>9207397</NlmUniqueID><ISSNLinking>0960-7412</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Arabidopsis</Keyword><Keyword MajorTopicYN="Y">SNPs</Keyword><Keyword MajorTopicYN="Y">complementary GWAS</Keyword><Keyword MajorTopicYN="Y">flux balance analysis</Keyword><Keyword MajorTopicYN="Y">metabolic networks</Keyword><Keyword MajorTopicYN="Y">plant metabolic modeling</Keyword><Keyword MajorTopicYN="Y">poplar</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year><Month>10</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>02</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32167625</ArticleId><ArticleId IdType="doi">10.1111/tpj.14746</ArticleId></ArticleIdList><ReferenceList><Title>REFERENCES</Title><Reference><Citation>Alonso-Blanco, C., Andrade, J., Becker, C. et al. 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