Mapping shape quantitative trait loci using a radius-centroid-contour model.
Identifieur interne : 002596 ( Main/Exploration ); précédent : 002595; suivant : 002597Mapping shape quantitative trait loci using a radius-centroid-contour model.
Auteurs : G. Fu [République populaire de Chine] ; W. Bo ; X. Pang ; Z. Wang ; L. Chen ; Y. Song ; Z. Zhang ; J. Li ; R. WuSource :
- Heredity [ 1365-2540 ] ; 2013.
Descripteurs français
- KwdFr :
- Analyse en composantes principales (MeSH), Animaux (MeSH), Cartographie chromosomique (MeSH), Croisements génétiques (MeSH), Environnement (MeSH), Feuilles de plante (génétique), Génotype (MeSH), Humains (MeSH), Locus de caractère quantitatif (génétique), Modèles statistiques (MeSH), Phénotype (MeSH).
- MESH :
English descriptors
- KwdEn :
- MESH :
Abstract
As the consequence of complex interactions between different parts of an organ, shape can be used as a predictor of structural-functional relationships implicated in changing environments. Despite such importance, however, it is no surprise that little is known about the genetic detail involved in shape variation, because no approach is currently available for mapping quantitative trait loci (QTLs) that control shape. Here, we address this problem by developing a statistical model that integrates the principle of shape analysis into a mixture-model-based likelihood formulated for QTL mapping. One state-of-the-art approach for shape analysis is to identify and analyze the polar coordinates of anatomical landmarks on a shape measured in terms of radii from the centroid to the contour at regular intervals. A procrustes analysis is used to align shapes to filter out position, scale and rotation effects on shape variation. To the end, the accurate and quantitative representation of a shape is produced with aligned radius-centroid-contour (RCC) curves, that is, a function of radial angle at the centroid. The high dimensionality of the RCC data, crucial for a comprehensive description of the geometric feature of a shape, is reduced by principal component (PC) analysis, and the resulting PC axes are treated as phenotypic traits, allowing specific QTLs for global and local shape variability to be mapped, respectively. The usefulness and utilization of the new model for shape mapping in practice are validated by analyzing a mapping data collected from a natural population of poplar, Populus szechuanica var tibetica, and identifying several QTLs for leaf shape in this species. The model provides a powerful tool to compute which genes determine biological shape in plants, animals and humans.
DOI: 10.1038/hdy.2012.97
PubMed: 23572125
PubMed Central: PMC3656636
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Wang</name></author><author><name sortKey="Chen, L" sort="Chen, L" uniqKey="Chen L" first="L" last="Chen">L. Chen</name></author><author><name sortKey="Song, Y" sort="Song, Y" uniqKey="Song Y" first="Y" last="Song">Y. Song</name></author><author><name sortKey="Zhang, Z" sort="Zhang, Z" uniqKey="Zhang Z" first="Z" last="Zhang">Z. Zhang</name></author><author><name sortKey="Li, J" sort="Li, J" uniqKey="Li J" first="J" last="Li">J. Li</name></author><author><name sortKey="Wu, R" sort="Wu, R" uniqKey="Wu R" first="R" last="Wu">R. Wu</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="RBID">pubmed:23572125</idno><idno type="pmid">23572125</idno><idno type="doi">10.1038/hdy.2012.97</idno><idno type="pmc">PMC3656636</idno><idno type="wicri:Area/Main/Corpus">002640</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">002640</idno><idno type="wicri:Area/Main/Curation">002640</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">002640</idno><idno type="wicri:Area/Main/Exploration">002640</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Mapping shape quantitative trait loci using a radius-centroid-contour model.</title><author><name sortKey="Fu, G" sort="Fu, G" uniqKey="Fu G" first="G" last="Fu">G. Fu</name><affiliation wicri:level="1"><nlm:affiliation>Center for Computational Biology, Beijing Forestry University, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Center for Computational Biology, Beijing Forestry University</wicri:regionArea><wicri:noRegion>Beijing Forestry University</wicri:noRegion></affiliation></author><author><name sortKey="Bo, W" sort="Bo, W" uniqKey="Bo W" first="W" last="Bo">W. Bo</name></author><author><name sortKey="Pang, X" sort="Pang, X" uniqKey="Pang X" first="X" last="Pang">X. Pang</name></author><author><name sortKey="Wang, Z" sort="Wang, Z" uniqKey="Wang Z" first="Z" last="Wang">Z. Wang</name></author><author><name sortKey="Chen, L" sort="Chen, L" uniqKey="Chen L" first="L" last="Chen">L. Chen</name></author><author><name sortKey="Song, Y" sort="Song, Y" uniqKey="Song Y" first="Y" last="Song">Y. Song</name></author><author><name sortKey="Zhang, Z" sort="Zhang, Z" uniqKey="Zhang Z" first="Z" last="Zhang">Z. Zhang</name></author><author><name sortKey="Li, J" sort="Li, J" uniqKey="Li J" first="J" last="Li">J. Li</name></author><author><name sortKey="Wu, R" sort="Wu, R" uniqKey="Wu R" first="R" last="Wu">R. Wu</name></author></analytic><series><title level="j">Heredity</title><idno type="eISSN">1365-2540</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals (MeSH)</term><term>Chromosome Mapping (MeSH)</term><term>Crosses, Genetic (MeSH)</term><term>Environment (MeSH)</term><term>Genotype (MeSH)</term><term>Humans (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Phenotype (MeSH)</term><term>Plant Leaves (genetics)</term><term>Principal Component Analysis (MeSH)</term><term>Quantitative Trait Loci (genetics)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse en composantes principales (MeSH)</term><term>Animaux (MeSH)</term><term>Cartographie chromosomique (MeSH)</term><term>Croisements génétiques (MeSH)</term><term>Environnement (MeSH)</term><term>Feuilles de plante (génétique)</term><term>Génotype (MeSH)</term><term>Humains (MeSH)</term><term>Locus de caractère quantitatif (génétique)</term><term>Modèles statistiques (MeSH)</term><term>Phénotype (MeSH)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Plant Leaves</term><term>Quantitative Trait Loci</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Feuilles de plante</term><term>Locus de caractère quantitatif</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Chromosome Mapping</term><term>Crosses, Genetic</term><term>Environment</term><term>Genotype</term><term>Humans</term><term>Models, Statistical</term><term>Phenotype</term><term>Principal Component Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse en composantes principales</term><term>Animaux</term><term>Cartographie chromosomique</term><term>Croisements génétiques</term><term>Environnement</term><term>Génotype</term><term>Humains</term><term>Modèles statistiques</term><term>Phénotype</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">As the consequence of complex interactions between different parts of an organ, shape can be used as a predictor of structural-functional relationships implicated in changing environments. Despite such importance, however, it is no surprise that little is known about the genetic detail involved in shape variation, because no approach is currently available for mapping quantitative trait loci (QTLs) that control shape. Here, we address this problem by developing a statistical model that integrates the principle of shape analysis into a mixture-model-based likelihood formulated for QTL mapping. One state-of-the-art approach for shape analysis is to identify and analyze the polar coordinates of anatomical landmarks on a shape measured in terms of radii from the centroid to the contour at regular intervals. A procrustes analysis is used to align shapes to filter out position, scale and rotation effects on shape variation. To the end, the accurate and quantitative representation of a shape is produced with aligned radius-centroid-contour (RCC) curves, that is, a function of radial angle at the centroid. The high dimensionality of the RCC data, crucial for a comprehensive description of the geometric feature of a shape, is reduced by principal component (PC) analysis, and the resulting PC axes are treated as phenotypic traits, allowing specific QTLs for global and local shape variability to be mapped, respectively. The usefulness and utilization of the new model for shape mapping in practice are validated by analyzing a mapping data collected from a natural population of poplar, Populus szechuanica var tibetica, and identifying several QTLs for leaf shape in this species. The model provides a powerful tool to compute which genes determine biological shape in plants, animals and humans.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23572125</PMID><DateCompleted><Year>2013</Year><Month>12</Month><Day>31</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1365-2540</ISSN><JournalIssue CitedMedium="Internet"><Volume>110</Volume><Issue>6</Issue><PubDate><Year>2013</Year><Month>Jun</Month></PubDate></JournalIssue><Title>Heredity</Title><ISOAbbreviation>Heredity (Edinb)</ISOAbbreviation></Journal><ArticleTitle>Mapping shape quantitative trait loci using a radius-centroid-contour model.</ArticleTitle><Pagination><MedlinePgn>511-9</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1038/hdy.2012.97</ELocationID><Abstract><AbstractText>As the consequence of complex interactions between different parts of an organ, shape can be used as a predictor of structural-functional relationships implicated in changing environments. Despite such importance, however, it is no surprise that little is known about the genetic detail involved in shape variation, because no approach is currently available for mapping quantitative trait loci (QTLs) that control shape. Here, we address this problem by developing a statistical model that integrates the principle of shape analysis into a mixture-model-based likelihood formulated for QTL mapping. One state-of-the-art approach for shape analysis is to identify and analyze the polar coordinates of anatomical landmarks on a shape measured in terms of radii from the centroid to the contour at regular intervals. A procrustes analysis is used to align shapes to filter out position, scale and rotation effects on shape variation. To the end, the accurate and quantitative representation of a shape is produced with aligned radius-centroid-contour (RCC) curves, that is, a function of radial angle at the centroid. The high dimensionality of the RCC data, crucial for a comprehensive description of the geometric feature of a shape, is reduced by principal component (PC) analysis, and the resulting PC axes are treated as phenotypic traits, allowing specific QTLs for global and local shape variability to be mapped, respectively. The usefulness and utilization of the new model for shape mapping in practice are validated by analyzing a mapping data collected from a natural population of poplar, Populus szechuanica var tibetica, and identifying several QTLs for leaf shape in this species. The model provides a powerful tool to compute which genes determine biological shape in plants, animals and humans.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Fu</LastName><ForeName>G</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Center for Computational Biology, Beijing Forestry University, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bo</LastName><ForeName>W</ForeName><Initials>W</Initials></Author><Author ValidYN="Y"><LastName>Pang</LastName><ForeName>X</ForeName><Initials>X</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Z</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>L</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Song</LastName><ForeName>Y</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Z</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>J</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>R</ForeName><Initials>R</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>04</Month><Day>10</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Heredity (Edinb)</MedlineTA><NlmUniqueID>0373007</NlmUniqueID><ISSNLinking>0018-067X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002874" MajorTopicYN="Y">Chromosome 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