Association mapping in Populus reveals the interaction between Pto-miR530a and its target Pto-KNAT1.
Identifieur interne : 001E83 ( Main/Exploration ); précédent : 001E82; suivant : 001E84Association mapping in Populus reveals the interaction between Pto-miR530a and its target Pto-KNAT1.
Auteurs : Xiaohui Yang [Oman] ; Qingzhang Du ; Jinhui Chen ; Bowen Wang ; Deqiang ZhangSource :
- Planta [ 1432-2048 ] ; 2015.
Descripteurs français
- KwdFr :
- ARN messager (génétique), ARN messager (métabolisme), Caractère quantitatif héréditaire (MeSH), Cartographie chromosomique (MeSH), Déséquilibre de liaison (génétique), Gènes dominants (MeSH), Liaison aux protéines (MeSH), Locus génétiques (MeSH), Nucléotides (génétique), Phylogenèse (MeSH), Phénotype (MeSH), Polymorphisme de nucléotide simple (génétique), Populus (croissance et développement), Populus (génétique), Protéines végétales (génétique), Protéines végétales (métabolisme), Régulation de l'expression des gènes végétaux (MeSH), Spécificité d'organe (génétique), microARN (génétique), microARN (métabolisme), Épistasie (MeSH).
- MESH :
- croissance et développement : Populus.
- génétique : ARN messager, Déséquilibre de liaison, Nucléotides, Polymorphisme de nucléotide simple, Populus, Protéines végétales, Spécificité d'organe, microARN.
- métabolisme : ARN messager, Protéines végétales, microARN.
- Caractère quantitatif héréditaire, Cartographie chromosomique, Gènes dominants, Liaison aux protéines, Locus génétiques, Phylogenèse, Phénotype, Régulation de l'expression des gènes végétaux, Épistasie.
English descriptors
- KwdEn :
- Chromosome Mapping (MeSH), Epistasis, Genetic (MeSH), Gene Expression Regulation, Plant (MeSH), Genes, Dominant (MeSH), Genetic Loci (MeSH), Linkage Disequilibrium (genetics), MicroRNAs (genetics), MicroRNAs (metabolism), Nucleotides (genetics), Organ Specificity (genetics), Phenotype (MeSH), Phylogeny (MeSH), Plant Proteins (genetics), Plant Proteins (metabolism), Polymorphism, Single Nucleotide (genetics), Populus (genetics), Populus (growth & development), Protein Binding (MeSH), Quantitative Trait, Heritable (MeSH), RNA, Messenger (genetics), RNA, Messenger (metabolism).
- MESH :
- chemical , genetics : MicroRNAs, Nucleotides, Plant Proteins, RNA, Messenger.
- genetics : Linkage Disequilibrium, Organ Specificity, Polymorphism, Single Nucleotide, Populus.
- growth & development : Populus.
- chemical , metabolism : MicroRNAs, Plant Proteins, RNA, Messenger.
- Chromosome Mapping, Epistasis, Genetic, Gene Expression Regulation, Plant, Genes, Dominant, Genetic Loci, Phenotype, Phylogeny, Protein Binding, Quantitative Trait, Heritable.
Abstract
MAIN CONCLUSION
We used transcript profiling and multi-SNP association to investigate the genetic regulatory relationship between miRNA Pto-miR530a and its target Pto-KNAT1, identifying additive, dominant, and epistatic effects. MicroRNAs (miRNAs) play crucial roles in the post-transcriptional regulation of plant growth and development; indeed, many studies have described the importance of miRNA-target interactions in herbaceous species. However, elucidation of the miRNA-target interactions in trees may require novel strategies. In the present study, we describe a strategy combining expression profiling by reverse transcription quantitative PCR (RT-qPCR) and association mapping with multiple single nucleotide polymorphisms (SNPs) to evaluate the interaction between Pto-miR530a and its target Pto-KNAT1 in Populus tomentosa. RT-qPCR analysis showed a negative correlation (r = -0.62, P < 0.05) between expression levels of Pto-miR530a and Pto-KNAT1 in eight tissues. We used a Bayesian hierarchical model to identify allelic variants of Pto-miR530a and Pto-KNAT1 that associated with eight traits related to growth and wood properties, in a population of 460 unrelated individuals of P. tomentosa. This analysis identified 27 associations, with the proportions of phenotypic variance (R (2)) contributed by each SNP ranging of 0.82-15.81 %, the additive effects of each SNP ranging of 0.16-18.09, and the dominant effects ranging from -14.09 to 19.00. Epistatic interaction models showed a strong interaction among SNPs in the miRNA target with R (2) of 0.1-3.56 %, and information gain of significant SNP pairs of -3.09 to 0.93 %, representing the regulatory interactions between the miRNA and the mRNA. Thus, we used a new strategy that combines association genetics and expression profiling based on SNPs to study the regulatory relationship between this miRNA and its target mRNA, thereby providing novel advances in our understanding of the genetic architecture of important traits.
DOI: 10.1007/s00425-015-2287-3
PubMed: 25833262
Affiliations:
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Dominant</term><term>Genetic Loci</term><term>Phenotype</term><term>Phylogeny</term><term>Protein Binding</term><term>Quantitative Trait, Heritable</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Caractère quantitatif héréditaire</term><term>Cartographie chromosomique</term><term>Gènes dominants</term><term>Liaison aux protéines</term><term>Locus génétiques</term><term>Phylogenèse</term><term>Phénotype</term><term>Régulation de l'expression des gènes végétaux</term><term>Épistasie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>MAIN CONCLUSION</b></p><p>We used transcript profiling and multi-SNP association to investigate the genetic regulatory relationship between miRNA Pto-miR530a and its target Pto-KNAT1, identifying additive, dominant, and epistatic effects. MicroRNAs (miRNAs) play crucial roles in the post-transcriptional regulation of plant growth and development; indeed, many studies have described the importance of miRNA-target interactions in herbaceous species. However, elucidation of the miRNA-target interactions in trees may require novel strategies. In the present study, we describe a strategy combining expression profiling by reverse transcription quantitative PCR (RT-qPCR) and association mapping with multiple single nucleotide polymorphisms (SNPs) to evaluate the interaction between Pto-miR530a and its target Pto-KNAT1 in Populus tomentosa. RT-qPCR analysis showed a negative correlation (r = -0.62, P < 0.05) between expression levels of Pto-miR530a and Pto-KNAT1 in eight tissues. We used a Bayesian hierarchical model to identify allelic variants of Pto-miR530a and Pto-KNAT1 that associated with eight traits related to growth and wood properties, in a population of 460 unrelated individuals of P. tomentosa. This analysis identified 27 associations, with the proportions of phenotypic variance (R (2)) contributed by each SNP ranging of 0.82-15.81 %, the additive effects of each SNP ranging of 0.16-18.09, and the dominant effects ranging from -14.09 to 19.00. Epistatic interaction models showed a strong interaction among SNPs in the miRNA target with R (2) of 0.1-3.56 %, and information gain of significant SNP pairs of -3.09 to 0.93 %, representing the regulatory interactions between the miRNA and the mRNA. Thus, we used a new strategy that combines association genetics and expression profiling based on SNPs to study the regulatory relationship between this miRNA and its target mRNA, thereby providing novel advances in our understanding of the genetic architecture of important traits.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25833262</PMID><DateCompleted><Year>2016</Year><Month>04</Month><Day>14</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1432-2048</ISSN><JournalIssue CitedMedium="Internet"><Volume>242</Volume><Issue>1</Issue><PubDate><Year>2015</Year><Month>Jul</Month></PubDate></JournalIssue><Title>Planta</Title><ISOAbbreviation>Planta</ISOAbbreviation></Journal><ArticleTitle>Association mapping in Populus reveals the interaction between Pto-miR530a and its target Pto-KNAT1.</ArticleTitle><Pagination><MedlinePgn>77-95</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s00425-015-2287-3</ELocationID><Abstract><AbstractText Label="MAIN CONCLUSION" NlmCategory="CONCLUSIONS">We used transcript profiling and multi-SNP association to investigate the genetic regulatory relationship between miRNA Pto-miR530a and its target Pto-KNAT1, identifying additive, dominant, and epistatic effects. MicroRNAs (miRNAs) play crucial roles in the post-transcriptional regulation of plant growth and development; indeed, many studies have described the importance of miRNA-target interactions in herbaceous species. However, elucidation of the miRNA-target interactions in trees may require novel strategies. In the present study, we describe a strategy combining expression profiling by reverse transcription quantitative PCR (RT-qPCR) and association mapping with multiple single nucleotide polymorphisms (SNPs) to evaluate the interaction between Pto-miR530a and its target Pto-KNAT1 in Populus tomentosa. RT-qPCR analysis showed a negative correlation (r = -0.62, P < 0.05) between expression levels of Pto-miR530a and Pto-KNAT1 in eight tissues. We used a Bayesian hierarchical model to identify allelic variants of Pto-miR530a and Pto-KNAT1 that associated with eight traits related to growth and wood properties, in a population of 460 unrelated individuals of P. tomentosa. This analysis identified 27 associations, with the proportions of phenotypic variance (R (2)) contributed by each SNP ranging of 0.82-15.81 %, the additive effects of each SNP ranging of 0.16-18.09, and the dominant effects ranging from -14.09 to 19.00. Epistatic interaction models showed a strong interaction among SNPs in the miRNA target with R (2) of 0.1-3.56 %, and information gain of significant SNP pairs of -3.09 to 0.93 %, representing the regulatory interactions between the miRNA and the mRNA. Thus, we used a new strategy that combines association genetics and expression profiling based on SNPs to study the regulatory relationship between this miRNA and its target mRNA, thereby providing novel advances in our understanding of the genetic architecture of important traits.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Xiaohui</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, People's Republic of China, yangxiaohui82122@163.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Du</LastName><ForeName>Qingzhang</ForeName><Initials>Q</Initials></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Jinhui</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Bowen</ForeName><Initials>B</Initials></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Deqiang</ForeName><Initials>D</Initials></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>2015</Year><Month>04</Month><Day>02</Day></ArticleDate></Article><MedlineJournalInfo><Country>Germany</Country><MedlineTA>Planta</MedlineTA><NlmUniqueID>1250576</NlmUniqueID><ISSNLinking>0032-0935</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D035683">MicroRNAs</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D009711">Nucleotides</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance 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UI="D035683" MajorTopicYN="N">MicroRNAs</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D009711" MajorTopicYN="N">Nucleotides</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D009928" MajorTopicYN="N">Organ Specificity</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010641" MajorTopicYN="N">Phenotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010802" MajorTopicYN="N">Phylogeny</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010940" MajorTopicYN="N">Plant Proteins</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020641" MajorTopicYN="N">Polymorphism, Single Nucleotide</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000254" MajorTopicYN="N">growth & development</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011485" MajorTopicYN="N">Protein Binding</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019655" MajorTopicYN="N">Quantitative Trait, Heritable</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012333" MajorTopicYN="N">RNA, Messenger</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000378" 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sortKey="Yang, Xiaohui" sort="Yang, Xiaohui" uniqKey="Yang X" first="Xiaohui" last="Yang">Xiaohui Yang</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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