A nonlinear mixed-effect mixture model for functional mapping of dynamic traits.
Identifieur interne : 003A07 ( Main/Exploration ); précédent : 003A06; suivant : 003A08A nonlinear mixed-effect mixture model for functional mapping of dynamic traits.
Auteurs : W. Hou [États-Unis] ; H. Li ; B. Zhang ; M. Huang ; R. WuSource :
- Heredity [ 1365-2540 ] ; 2008.
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Abstract
Functional mapping has emerged as a next-generation statistical tool for mapping quantitative trait loci (QTL) that affect complex dynamic traits. In this article, we incorporated the idea of nonlinear mixed-effect (NLME) models into the mixture-based framework of functional mapping, aimed to generalize the spectrum of applications for functional mapping. NLME-based functional mapping, implemented with the linearization algorithm based on the first-order Taylor expansion, can provide reasonable estimates of QTL genotypic-specific curve parameters (fixed effect) and the between-individual variation of these parameters (random effect). Results from simulation studies suggest that the NLME-based model is more general than traditional functional mapping. The new model can be useful for the identification of the ontogenetic patterns of QTL genetic effects during time course.
DOI: 10.1038/hdy.2008.53
PubMed: 18612322
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Wu</name></author></analytic><series><title level="j">Heredity</title><idno type="eISSN">1365-2540</idno><imprint><date when="2008" type="published">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Computer Simulation (MeSH)</term><term>Genotype (MeSH)</term><term>Likelihood Functions (MeSH)</term><term>Models, Genetic (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Monte Carlo Method (MeSH)</term><term>Phenotype (MeSH)</term><term>Populus (genetics)</term><term>Quantitative Trait, Heritable (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Caractère quantitatif héréditaire (MeSH)</term><term>Fonctions de vraisemblance (MeSH)</term><term>Génotype (MeSH)</term><term>Modèles génétiques (MeSH)</term><term>Modèles statistiques (MeSH)</term><term>Méthode de Monte Carlo (MeSH)</term><term>Phénotype (MeSH)</term><term>Populus (génétique)</term><term>Simulation numérique (MeSH)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Populus</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Computer Simulation</term><term>Genotype</term><term>Likelihood Functions</term><term>Models, Genetic</term><term>Models, Statistical</term><term>Monte Carlo Method</term><term>Phenotype</term><term>Quantitative Trait, Heritable</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Caractère quantitatif héréditaire</term><term>Fonctions de vraisemblance</term><term>Génotype</term><term>Modèles génétiques</term><term>Modèles statistiques</term><term>Méthode de Monte Carlo</term><term>Phénotype</term><term>Simulation numérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Functional mapping has emerged as a next-generation statistical tool for mapping quantitative trait loci (QTL) that affect complex dynamic traits. In this article, we incorporated the idea of nonlinear mixed-effect (NLME) models into the mixture-based framework of functional mapping, aimed to generalize the spectrum of applications for functional mapping. NLME-based functional mapping, implemented with the linearization algorithm based on the first-order Taylor expansion, can provide reasonable estimates of QTL genotypic-specific curve parameters (fixed effect) and the between-individual variation of these parameters (random effect). Results from simulation studies suggest that the NLME-based model is more general than traditional functional mapping. The new model can be useful for the identification of the ontogenetic patterns of QTL genetic effects during time course.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">18612322</PMID><DateCompleted><Year>2008</Year><Month>10</Month><Day>23</Day></DateCompleted><DateRevised><Year>2011</Year><Month>12</Month><Day>09</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1365-2540</ISSN><JournalIssue CitedMedium="Internet"><Volume>101</Volume><Issue>4</Issue><PubDate><Year>2008</Year><Month>Oct</Month></PubDate></JournalIssue><Title>Heredity</Title><ISOAbbreviation>Heredity (Edinb)</ISOAbbreviation></Journal><ArticleTitle>A nonlinear mixed-effect mixture model for functional mapping of dynamic traits.</ArticleTitle><Pagination><MedlinePgn>321-8</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1038/hdy.2008.53</ELocationID><Abstract><AbstractText>Functional mapping has emerged as a next-generation statistical tool for mapping quantitative trait loci (QTL) that affect complex dynamic traits. In this article, we incorporated the idea of nonlinear mixed-effect (NLME) models into the mixture-based framework of functional mapping, aimed to generalize the spectrum of applications for functional mapping. NLME-based functional mapping, implemented with the linearization algorithm based on the first-order Taylor expansion, can provide reasonable estimates of QTL genotypic-specific curve parameters (fixed effect) and the between-individual variation of these parameters (random effect). Results from simulation studies suggest that the NLME-based model is more general than traditional functional mapping. The new model can be useful for the identification of the ontogenetic patterns of QTL genetic effects during time course.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hou</LastName><ForeName>W</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>Department of Statistics, University of Florida, Gainesville, FL 32611, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>H</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>B</ForeName><Initials>B</Initials></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>M</ForeName><Initials>M</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>2008</Year><Month>07</Month><Day>09</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="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005838" MajorTopicYN="N">Genotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016013" MajorTopicYN="N">Likelihood Functions</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008957" MajorTopicYN="Y">Models, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="Y">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009010" MajorTopicYN="N">Monte Carlo Method</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010641" MajorTopicYN="N">Phenotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D019655" MajorTopicYN="Y">Quantitative Trait, Heritable</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2008</Year><Month>7</Month><Day>10</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2008</Year><Month>10</Month><Day>24</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2008</Year><Month>7</Month><Day>10</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">18612322</ArticleId><ArticleId IdType="pii">hdy200853</ArticleId><ArticleId IdType="doi">10.1038/hdy.2008.53</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Floride</li></region></list><tree><noCountry><name sortKey="Huang, M" sort="Huang, M" uniqKey="Huang M" first="M" last="Huang">M. Huang</name><name sortKey="Li, H" sort="Li, H" uniqKey="Li H" first="H" last="Li">H. Li</name><name sortKey="Wu, R" sort="Wu, R" uniqKey="Wu R" first="R" last="Wu">R. Wu</name><name sortKey="Zhang, B" sort="Zhang, B" uniqKey="Zhang B" first="B" last="Zhang">B. Zhang</name></noCountry><country name="États-Unis"><region name="Floride"><name sortKey="Hou, W" sort="Hou, W" uniqKey="Hou W" first="W" last="Hou">W. Hou</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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