Genetic mapping of developmental instability: design, model and algorithm.
Identifieur interne : 003B79 ( Main/Exploration ); précédent : 003B78; suivant : 003B80Genetic mapping of developmental instability: design, model and algorithm.
Auteurs : Jiasheng Wu [République populaire de Chine] ; Bo Zhang ; Yuehua Cui ; Wei Zhao ; Li'An Xu ; Minren Huang ; Yanru Zeng ; Jun Zhu ; Rongling WuSource :
- Genetics [ 0016-6731 ] ; 2007.
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Abstract
Developmental instability or noise, defined as the phenotypic imprecision of an organism in the face of internal or external stochastic disturbances, has been thought to play an important role in shaping evolutionary processes and patterns. The genetic studies of developmental instability have been based on fluctuating asymmetry (FA) that measures random differences between the left and the right sides of bilateral traits. In this article, we frame an experimental design characterized by a spatial autocorrelation structure for determining the genetic control of developmental instability for those traits that cannot be bilaterally measured. This design allows the residual environmental variance of a quantitative trait to be dissolved into two components due to permanent and random environmental factors. The degree of developmental instability is quantified by the relative proportion of the random residual variance to the total residual variance. We formulate a mixture model to estimate and test the genetic effects of quantitative trait loci (QTL) on the developmental instability of the trait. The genetic parameters including the QTL position, the QTL effects, and spatial autocorrelations are estimated by implementing the EM algorithm within the mixture model framework. Simulation studies were performed to investigate the statistical behavior of the model. A live example for poplar trees was used to map the QTL that control root length growth and its developmental instability from cuttings in water culture.
DOI: 10.1534/genetics.107.072843
PubMed: 17435243
PubMed Central: PMC1894583
Affiliations:
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Zhejiang University, Hangzhou, Zhejiang 310029</wicri:regionArea><orgName type="university">Université de Zhejiang</orgName><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName></affiliation></author><author><name sortKey="Zhang, Bo" sort="Zhang, Bo" uniqKey="Zhang B" first="Bo" last="Zhang">Bo Zhang</name></author><author><name sortKey="Cui, Yuehua" sort="Cui, Yuehua" uniqKey="Cui Y" first="Yuehua" last="Cui">Yuehua Cui</name></author><author><name sortKey="Zhao, Wei" sort="Zhao, Wei" uniqKey="Zhao W" first="Wei" last="Zhao">Wei Zhao</name></author><author><name sortKey="Xu, Li An" sort="Xu, Li An" uniqKey="Xu L" first="Li'An" last="Xu">Li'An Xu</name></author><author><name sortKey="Huang, Minren" sort="Huang, Minren" uniqKey="Huang M" first="Minren" last="Huang">Minren Huang</name></author><author><name sortKey="Zeng, Yanru" sort="Zeng, Yanru" uniqKey="Zeng Y" first="Yanru" last="Zeng">Yanru Zeng</name></author><author><name sortKey="Zhu, Jun" sort="Zhu, Jun" uniqKey="Zhu J" first="Jun" last="Zhu">Jun Zhu</name></author><author><name sortKey="Wu, Rongling" sort="Wu, Rongling" uniqKey="Wu R" first="Rongling" last="Wu">Rongling Wu</name></author></analytic><series><title level="j">Genetics</title><idno type="ISSN">0016-6731</idno><imprint><date when="2007" type="published">2007</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Chromosome Mapping (MeSH)</term><term>Crosses, Genetic (MeSH)</term><term>Genomic Instability (MeSH)</term><term>Models, Genetic (MeSH)</term><term>Plants (genetics)</term><term>Quantitative Trait Loci (MeSH)</term><term>Stochastic Processes (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Cartographie chromosomique (MeSH)</term><term>Croisements génétiques (MeSH)</term><term>Instabilité du génome (MeSH)</term><term>Locus de caractère quantitatif (MeSH)</term><term>Modèles génétiques (MeSH)</term><term>Plantes (génétique)</term><term>Processus stochastiques (MeSH)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Plants</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Plantes</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Chromosome Mapping</term><term>Crosses, Genetic</term><term>Genomic Instability</term><term>Models, Genetic</term><term>Quantitative Trait Loci</term><term>Stochastic Processes</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Cartographie chromosomique</term><term>Croisements génétiques</term><term>Instabilité du génome</term><term>Locus de caractère quantitatif</term><term>Modèles génétiques</term><term>Processus stochastiques</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Developmental instability or noise, defined as the phenotypic imprecision of an organism in the face of internal or external stochastic disturbances, has been thought to play an important role in shaping evolutionary processes and patterns. The genetic studies of developmental instability have been based on fluctuating asymmetry (FA) that measures random differences between the left and the right sides of bilateral traits. In this article, we frame an experimental design characterized by a spatial autocorrelation structure for determining the genetic control of developmental instability for those traits that cannot be bilaterally measured. This design allows the residual environmental variance of a quantitative trait to be dissolved into two components due to permanent and random environmental factors. The degree of developmental instability is quantified by the relative proportion of the random residual variance to the total residual variance. We formulate a mixture model to estimate and test the genetic effects of quantitative trait loci (QTL) on the developmental instability of the trait. The genetic parameters including the QTL position, the QTL effects, and spatial autocorrelations are estimated by implementing the EM algorithm within the mixture model framework. Simulation studies were performed to investigate the statistical behavior of the model. A live example for poplar trees was used to map the QTL that control root length growth and its developmental instability from cuttings in water culture.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">17435243</PMID><DateCompleted><Year>2007</Year><Month>10</Month><Day>11</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>27</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0016-6731</ISSN><JournalIssue CitedMedium="Print"><Volume>176</Volume><Issue>2</Issue><PubDate><Year>2007</Year><Month>Jun</Month></PubDate></JournalIssue><Title>Genetics</Title><ISOAbbreviation>Genetics</ISOAbbreviation></Journal><ArticleTitle>Genetic mapping of developmental instability: design, model and algorithm.</ArticleTitle><Pagination><MedlinePgn>1187-96</MedlinePgn></Pagination><Abstract><AbstractText>Developmental instability or noise, defined as the phenotypic imprecision of an organism in the face of internal or external stochastic disturbances, has been thought to play an important role in shaping evolutionary processes and patterns. The genetic studies of developmental instability have been based on fluctuating asymmetry (FA) that measures random differences between the left and the right sides of bilateral traits. In this article, we frame an experimental design characterized by a spatial autocorrelation structure for determining the genetic control of developmental instability for those traits that cannot be bilaterally measured. This design allows the residual environmental variance of a quantitative trait to be dissolved into two components due to permanent and random environmental factors. The degree of developmental instability is quantified by the relative proportion of the random residual variance to the total residual variance. We formulate a mixture model to estimate and test the genetic effects of quantitative trait loci (QTL) on the developmental instability of the trait. The genetic parameters including the QTL position, the QTL effects, and spatial autocorrelations are estimated by implementing the EM algorithm within the mixture model framework. Simulation studies were performed to investigate the statistical behavior of the model. A live example for poplar trees was used to map the QTL that control root length growth and its developmental instability from cuttings in water culture.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Jiasheng</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, Zhejiang 310029, People's Republic of China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Bo</ForeName><Initials>B</Initials></Author><Author ValidYN="Y"><LastName>Cui</LastName><ForeName>Yuehua</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Wei</ForeName><Initials>W</Initials></Author><Author ValidYN="Y"><LastName>Xu</LastName><ForeName>Li'an</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Minren</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Zeng</LastName><ForeName>Yanru</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Zhu</LastName><ForeName>Jun</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Rongling</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>2007</Year><Month>04</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Genetics</MedlineTA><NlmUniqueID>0374636</NlmUniqueID><ISSNLinking>0016-6731</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002874" MajorTopicYN="Y">Chromosome Mapping</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003433" MajorTopicYN="N">Crosses, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D042822" MajorTopicYN="Y">Genomic Instability</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008957" MajorTopicYN="Y">Models, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010944" MajorTopicYN="N">Plants</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D040641" MajorTopicYN="Y">Quantitative Trait Loci</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013269" MajorTopicYN="N">Stochastic Processes</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2007</Year><Month>4</Month><Day>17</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2007</Year><Month>10</Month><Day>12</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2007</Year><Month>4</Month><Day>17</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">17435243</ArticleId><ArticleId IdType="pii">genetics.107.072843</ArticleId><ArticleId IdType="doi">10.1534/genetics.107.072843</ArticleId><ArticleId IdType="pmc">PMC1894583</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Bioelectromagnetics. 2000 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sortKey="Xu, Li An" sort="Xu, Li An" uniqKey="Xu L" first="Li'An" last="Xu">Li'An Xu</name><name sortKey="Zeng, Yanru" sort="Zeng, Yanru" uniqKey="Zeng Y" first="Yanru" last="Zeng">Yanru Zeng</name><name sortKey="Zhang, Bo" sort="Zhang, Bo" uniqKey="Zhang B" first="Bo" last="Zhang">Bo Zhang</name><name sortKey="Zhao, Wei" sort="Zhao, Wei" uniqKey="Zhao W" first="Wei" last="Zhao">Wei Zhao</name><name sortKey="Zhu, Jun" sort="Zhu, Jun" uniqKey="Zhu J" first="Jun" last="Zhu">Jun Zhu</name></noCountry><country name="République populaire de Chine"><region name="Zhejiang"><name sortKey="Wu, Jiasheng" sort="Wu, Jiasheng" uniqKey="Wu J" first="Jiasheng" last="Wu">Jiasheng Wu</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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