Bayesian B-spline mapping for dynamic quantitative traits.
Identifieur interne : 002B88 ( Main/Exploration ); précédent : 002B87; suivant : 002B89Bayesian B-spline mapping for dynamic quantitative traits.
Auteurs : Jun Xing [République populaire de Chine] ; Jiahan Li ; Runqing Yang ; Xiaojing Zhou ; Shizhong XuSource :
- Genetics research [ 1469-5073 ] ; 2012.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Analyse de régression (MeSH), Cartographie chromosomique (méthodes), Chaines de Markov (MeSH), Locus de caractère quantitatif (génétique), Modèles génétiques (MeSH), Méthode de Monte Carlo (MeSH), Phénotype (MeSH), Populus (croissance et développement), Reproductibilité des résultats (MeSH), Simulation numérique (MeSH), Théorème de Bayes (MeSH), Tiges de plante (croissance et développement), Tiges de plante (génétique).
- MESH :
- croissance et développement : Populus, Tiges de plante.
- génétique : Locus de caractère quantitatif, Tiges de plante.
- méthodes : Cartographie chromosomique.
- Algorithmes, Analyse de régression, Chaines de Markov, Modèles génétiques, Méthode de Monte Carlo, Phénotype, Reproductibilité des résultats, Simulation numérique, Théorème de Bayes.
English descriptors
- KwdEn :
- Algorithms (MeSH), Bayes Theorem (MeSH), Chromosome Mapping (methods), Computer Simulation (MeSH), Markov Chains (MeSH), Models, Genetic (MeSH), Monte Carlo Method (MeSH), Phenotype (MeSH), Plant Stems (genetics), Plant Stems (growth & development), Populus (growth & development), Quantitative Trait Loci (genetics), Regression Analysis (MeSH), Reproducibility of Results (MeSH).
- MESH :
- genetics : Plant Stems, Quantitative Trait Loci.
- growth & development : Plant Stems, Populus.
- methods : Chromosome Mapping.
- Algorithms, Bayes Theorem, Computer Simulation, Markov Chains, Models, Genetic, Monte Carlo Method, Phenotype, Regression Analysis, Reproducibility of Results.
Abstract
Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.
DOI: 10.1017/S0016672312000249
PubMed: 22624568
Affiliations:
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uniqKey="Zhou X" first="Xiaojing" last="Zhou">Xiaojing Zhou</name></author><author><name sortKey="Xu, Shizhong" sort="Xu, Shizhong" uniqKey="Xu S" first="Shizhong" last="Xu">Shizhong Xu</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:22624568</idno><idno type="pmid">22624568</idno><idno type="doi">10.1017/S0016672312000249</idno><idno type="wicri:Area/Main/Corpus">002A27</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">002A27</idno><idno type="wicri:Area/Main/Curation">002A27</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">002A27</idno><idno type="wicri:Area/Main/Exploration">002A27</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Bayesian B-spline mapping for dynamic quantitative traits.</title><author><name sortKey="Xing, Jun" sort="Xing, Jun" uniqKey="Xing J" first="Jun" last="Xing">Jun Xing</name><affiliation wicri:level="1"><nlm:affiliation>Department of Gastroenterology, Tumor Hospital of Harbin Medical University, Harbin 150086, People's Republic of China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Gastroenterology, Tumor Hospital of Harbin Medical University, Harbin 150086</wicri:regionArea><wicri:noRegion>Harbin 150086</wicri:noRegion></affiliation></author><author><name sortKey="Li, Jiahan" sort="Li, Jiahan" uniqKey="Li J" first="Jiahan" last="Li">Jiahan Li</name></author><author><name sortKey="Yang, Runqing" sort="Yang, Runqing" uniqKey="Yang R" first="Runqing" last="Yang">Runqing Yang</name></author><author><name sortKey="Zhou, Xiaojing" sort="Zhou, Xiaojing" uniqKey="Zhou X" first="Xiaojing" last="Zhou">Xiaojing Zhou</name></author><author><name sortKey="Xu, Shizhong" sort="Xu, Shizhong" uniqKey="Xu S" first="Shizhong" last="Xu">Shizhong Xu</name></author></analytic><series><title level="j">Genetics research</title><idno type="eISSN">1469-5073</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Bayes Theorem (MeSH)</term><term>Chromosome Mapping (methods)</term><term>Computer Simulation (MeSH)</term><term>Markov Chains (MeSH)</term><term>Models, Genetic (MeSH)</term><term>Monte Carlo Method (MeSH)</term><term>Phenotype (MeSH)</term><term>Plant Stems (genetics)</term><term>Plant Stems (growth & development)</term><term>Populus (growth & development)</term><term>Quantitative Trait Loci (genetics)</term><term>Regression Analysis (MeSH)</term><term>Reproducibility of Results (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Analyse de régression (MeSH)</term><term>Cartographie chromosomique (méthodes)</term><term>Chaines de Markov (MeSH)</term><term>Locus de caractère quantitatif (génétique)</term><term>Modèles génétiques (MeSH)</term><term>Méthode de Monte Carlo (MeSH)</term><term>Phénotype (MeSH)</term><term>Populus (croissance et développement)</term><term>Reproductibilité des résultats (MeSH)</term><term>Simulation numérique (MeSH)</term><term>Théorème de Bayes (MeSH)</term><term>Tiges de plante (croissance et développement)</term><term>Tiges de plante (génétique)</term></keywords><keywords scheme="MESH" qualifier="croissance et développement" xml:lang="fr"><term>Populus</term><term>Tiges de plante</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Plant Stems</term><term>Quantitative Trait Loci</term></keywords><keywords scheme="MESH" qualifier="growth & development" xml:lang="en"><term>Plant Stems</term><term>Populus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Locus de caractère quantitatif</term><term>Tiges de plante</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Chromosome Mapping</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Cartographie chromosomique</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Bayes Theorem</term><term>Computer Simulation</term><term>Markov Chains</term><term>Models, Genetic</term><term>Monte Carlo Method</term><term>Phenotype</term><term>Regression Analysis</term><term>Reproducibility of Results</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de régression</term><term>Chaines de Markov</term><term>Modèles génétiques</term><term>Méthode de Monte Carlo</term><term>Phénotype</term><term>Reproductibilité des résultats</term><term>Simulation numérique</term><term>Théorème de Bayes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22624568</PMID><DateCompleted><Year>2012</Year><Month>09</Month><Day>28</Day></DateCompleted><DateRevised><Year>2016</Year><Month>05</Month><Day>19</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1469-5073</ISSN><JournalIssue CitedMedium="Internet"><Volume>94</Volume><Issue>2</Issue><PubDate><Year>2012</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Genetics research</Title><ISOAbbreviation>Genet Res (Camb)</ISOAbbreviation></Journal><ArticleTitle>Bayesian B-spline mapping for dynamic quantitative traits.</ArticleTitle><Pagination><MedlinePgn>85-95</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1017/S0016672312000249</ELocationID><Abstract><AbstractText>Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Xing</LastName><ForeName>Jun</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Gastroenterology, Tumor Hospital of Harbin Medical University, Harbin 150086, People's Republic of China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Jiahan</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Runqing</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Zhou</LastName><ForeName>Xiaojing</ForeName><Initials>X</Initials></Author><Author ValidYN="Y"><LastName>Xu</LastName><ForeName>Shizhong</ForeName><Initials>S</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></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Genet Res (Camb)</MedlineTA><NlmUniqueID>101550220</NlmUniqueID><ISSNLinking>0016-6723</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001499" MajorTopicYN="Y">Bayes Theorem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002874" MajorTopicYN="N">Chromosome Mapping</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008390" MajorTopicYN="N">Markov Chains</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008957" MajorTopicYN="Y">Models, Genetic</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="D018547" MajorTopicYN="N">Plant Stems</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000254" MajorTopicYN="N">growth & development</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000254" MajorTopicYN="N">growth & development</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D040641" MajorTopicYN="N">Quantitative Trait Loci</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012044" MajorTopicYN="N">Regression Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>5</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>5</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2012</Year><Month>9</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">22624568</ArticleId><ArticleId IdType="pii">S0016672312000249</ArticleId><ArticleId IdType="doi">10.1017/S0016672312000249</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><noCountry><name sortKey="Li, Jiahan" sort="Li, Jiahan" uniqKey="Li J" first="Jiahan" last="Li">Jiahan Li</name><name sortKey="Xu, Shizhong" sort="Xu, Shizhong" uniqKey="Xu S" first="Shizhong" last="Xu">Shizhong Xu</name><name sortKey="Yang, Runqing" sort="Yang, Runqing" uniqKey="Yang R" first="Runqing" last="Yang">Runqing Yang</name><name sortKey="Zhou, Xiaojing" sort="Zhou, Xiaojing" uniqKey="Zhou X" first="Xiaojing" last="Zhou">Xiaojing Zhou</name></noCountry><country name="République populaire de Chine"><noRegion><name sortKey="Xing, Jun" sort="Xing, Jun" uniqKey="Xing J" first="Jun" last="Xing">Jun Xing</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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