A statistical model for testing the pleiotropic control of phenotypic plasticity for a count trait.
Identifieur interne : 003A03 ( Main/Exploration ); précédent : 003A02; suivant : 003A04A statistical model for testing the pleiotropic control of phenotypic plasticity for a count trait.
Auteurs : Chang-Xing Ma [États-Unis] ; Qibin Yu ; Arthur Berg ; Derek Drost ; Evandro Novaes ; Guifang Fu ; John Stephen Yap ; Aixin Tan ; Matias Kirst ; Yuehua Cui ; Rongling WuSource :
- Genetics [ 0016-6731 ] ; 2008.
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Abstract
The differences of a phenotypic trait produced by a genotype in response to changes in the environment are referred to as phenotypic plasticity. Despite its importance in the maintenance of genetic diversity via genotype-by-environment interactions, little is known about the detailed genetic architecture of this phenomenon, thus limiting our ability to predict the pattern and process of microevolutionary responses to changing environments. In this article, we develop a statistical model for mapping quantitative trait loci (QTL) that control the phenotypic plasticity of a complex trait through differentiated expressions of pleiotropic QTL in different environments. In particular, our model focuses on count traits that represent an important aspect of biological systems, controlled by a network of multiple genes and environmental factors. The model was derived within a multivariate mixture model framework in which QTL genotype-specific mixture components are modeled by a multivariate Poisson distribution for a count trait expressed in multiple clonal replicates. A two-stage hierarchic EM algorithm is implemented to obtain the maximum-likelihood estimates of the Poisson parameters that specify environment-specific genetic effects of a QTL and residual errors. By approximating the number of sylleptic branches on the main stems of poplar hybrids by a Poisson distribution, the new model was applied to map QTL that contribute to the phenotypic plasticity of a count trait. The statistical behavior of the model and its utilization were investigated through simulation studies that mimic the poplar example used. This model will provide insights into how genomes and environments interact to determine the phenotypes of complex count traits.
DOI: 10.1534/genetics.107.081794
PubMed: 18493077
PubMed Central: PMC2390639
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last="Ma">Chang-Xing Ma</name><affiliation wicri:level="4"><nlm:affiliation>Department of Biostatistics, University at Buffalo, SUNY, Buffalo, NY 14214, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, University at Buffalo, SUNY, Buffalo, NY 14214</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">Buffalo (New York)</settlement></placeName><orgName type="university">Université d'État de New York à Buffalo</orgName></affiliation></author><author><name sortKey="Yu, Qibin" sort="Yu, Qibin" uniqKey="Yu Q" first="Qibin" last="Yu">Qibin Yu</name></author><author><name sortKey="Berg, Arthur" sort="Berg, Arthur" uniqKey="Berg A" first="Arthur" last="Berg">Arthur Berg</name></author><author><name sortKey="Drost, Derek" sort="Drost, Derek" uniqKey="Drost D" first="Derek" last="Drost">Derek Drost</name></author><author><name sortKey="Novaes, Evandro" sort="Novaes, Evandro" 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Simulation</term><term>Environment</term><term>Genotype</term><term>Likelihood Functions</term><term>Models, Genetic</term><term>Monte Carlo Method</term><term>Phenotype</term><term>Quantitative Trait Loci</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Environnement</term><term>Fonctions de vraisemblance</term><term>Génotype</term><term>Locus de caractère quantitatif</term><term>Modèles génétiques</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">The differences of a phenotypic trait produced by a genotype in response to changes in the environment are referred to as phenotypic plasticity. Despite its importance in the maintenance of genetic diversity via genotype-by-environment interactions, little is known about the detailed genetic architecture of this phenomenon, thus limiting our ability to predict the pattern and process of microevolutionary responses to changing environments. In this article, we develop a statistical model for mapping quantitative trait loci (QTL) that control the phenotypic plasticity of a complex trait through differentiated expressions of pleiotropic QTL in different environments. In particular, our model focuses on count traits that represent an important aspect of biological systems, controlled by a network of multiple genes and environmental factors. The model was derived within a multivariate mixture model framework in which QTL genotype-specific mixture components are modeled by a multivariate Poisson distribution for a count trait expressed in multiple clonal replicates. A two-stage hierarchic EM algorithm is implemented to obtain the maximum-likelihood estimates of the Poisson parameters that specify environment-specific genetic effects of a QTL and residual errors. By approximating the number of sylleptic branches on the main stems of poplar hybrids by a Poisson distribution, the new model was applied to map QTL that contribute to the phenotypic plasticity of a count trait. The statistical behavior of the model and its utilization were investigated through simulation studies that mimic the poplar example used. This model will provide insights into how genomes and environments interact to determine the phenotypes of complex count traits.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">18493077</PMID><DateCompleted><Year>2008</Year><Month>09</Month><Day>22</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>27</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0016-6731</ISSN><JournalIssue CitedMedium="Print"><Volume>179</Volume><Issue>1</Issue><PubDate><Year>2008</Year><Month>May</Month></PubDate></JournalIssue><Title>Genetics</Title><ISOAbbreviation>Genetics</ISOAbbreviation></Journal><ArticleTitle>A statistical model for testing the pleiotropic control of phenotypic plasticity for a count trait.</ArticleTitle><Pagination><MedlinePgn>627-36</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1534/genetics.107.081794</ELocationID><Abstract><AbstractText>The differences of a phenotypic trait produced by a genotype in response to changes in the environment are referred to as phenotypic plasticity. Despite its importance in the maintenance of genetic diversity via genotype-by-environment interactions, little is known about the detailed genetic architecture of this phenomenon, thus limiting our ability to predict the pattern and process of microevolutionary responses to changing environments. In this article, we develop a statistical model for mapping quantitative trait loci (QTL) that control the phenotypic plasticity of a complex trait through differentiated expressions of pleiotropic QTL in different environments. In particular, our model focuses on count traits that represent an important aspect of biological systems, controlled by a network of multiple genes and environmental factors. The model was derived within a multivariate mixture model framework in which QTL genotype-specific mixture components are modeled by a multivariate Poisson distribution for a count trait expressed in multiple clonal replicates. A two-stage hierarchic EM algorithm is implemented to obtain the maximum-likelihood estimates of the Poisson parameters that specify environment-specific genetic effects of a QTL and residual errors. By approximating the number of sylleptic branches on the main stems of poplar hybrids by a Poisson distribution, the new model was applied to map QTL that contribute to the phenotypic plasticity of a count trait. The statistical behavior of the model and its utilization were investigated through simulation studies that mimic the poplar example used. This model will provide insights into how genomes and environments interact to determine the phenotypes of complex count traits.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ma</LastName><ForeName>Chang-Xing</ForeName><Initials>CX</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, University at Buffalo, SUNY, Buffalo, NY 14214, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yu</LastName><ForeName>Qibin</ForeName><Initials>Q</Initials></Author><Author ValidYN="Y"><LastName>Berg</LastName><ForeName>Arthur</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Drost</LastName><ForeName>Derek</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Novaes</LastName><ForeName>Evandro</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Fu</LastName><ForeName>Guifang</ForeName><Initials>G</Initials></Author><Author 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IdType="pubmed">9584103</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>État de New York</li></region><settlement><li>Buffalo (New York)</li></settlement><orgName><li>Université d'État de New York à Buffalo</li></orgName></list><tree><noCountry><name sortKey="Berg, Arthur" sort="Berg, Arthur" uniqKey="Berg A" first="Arthur" last="Berg">Arthur Berg</name><name sortKey="Cui, Yuehua" sort="Cui, Yuehua" uniqKey="Cui Y" first="Yuehua" last="Cui">Yuehua Cui</name><name sortKey="Drost, Derek" sort="Drost, Derek" uniqKey="Drost D" first="Derek" last="Drost">Derek Drost</name><name sortKey="Fu, Guifang" sort="Fu, Guifang" uniqKey="Fu G" first="Guifang" last="Fu">Guifang Fu</name><name sortKey="Kirst, Matias" sort="Kirst, Matias" uniqKey="Kirst M" first="Matias" last="Kirst">Matias Kirst</name><name sortKey="Novaes, Evandro" sort="Novaes, Evandro" uniqKey="Novaes E" first="Evandro" last="Novaes">Evandro Novaes</name><name sortKey="Tan, Aixin" sort="Tan, Aixin" uniqKey="Tan A" first="Aixin" last="Tan">Aixin Tan</name><name sortKey="Wu, Rongling" sort="Wu, Rongling" uniqKey="Wu R" first="Rongling" last="Wu">Rongling Wu</name><name sortKey="Yap, John Stephen" sort="Yap, John Stephen" uniqKey="Yap J" first="John Stephen" last="Yap">John Stephen Yap</name><name sortKey="Yu, Qibin" sort="Yu, Qibin" uniqKey="Yu Q" first="Qibin" last="Yu">Qibin Yu</name></noCountry><country name="États-Unis"><region name="État de New York"><name sortKey="Ma, Chang Xing" sort="Ma, Chang Xing" uniqKey="Ma C" first="Chang-Xing" last="Ma">Chang-Xing Ma</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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