Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.
Identifieur interne : 000071 ( PubMed/Corpus ); précédent : 000070; suivant : 000072Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.
Auteurs : Osval A. Montesinos-L Pez ; Abelardo Montesinos-L Pez ; José Crossa ; Juan Burgue O ; Kent EskridgeSource :
- G3 (Bethesda, Md.) [ 2160-1836 ] ; 2015.
English descriptors
- KwdEn :
- MESH :
- genetics : Plant Diseases, Triticum, Zea mays.
- methods : Genomics.
- Algorithms, Bayes Theorem, Computer Simulation, Datasets as Topic, Logistic Models, Models, Genetic.
Abstract
Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link.
DOI: 10.1534/g3.115.021154
PubMed: 26290569
Links to Exploration step
pubmed:26290569Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.</title><author><name sortKey="Montesinos L Pez, Osval A" sort="Montesinos L Pez, Osval A" uniqKey="Montesinos L Pez O" first="Osval A" last="Montesinos-L Pez">Osval A. Montesinos-L Pez</name><affiliation><nlm:affiliation>Facultad de Telemática, Universidad de Colima, C.P. 28040 Colima, Colima, México.</nlm:affiliation></affiliation></author><author><name sortKey="Montesinos L Pez, Abelardo" sort="Montesinos L Pez, Abelardo" uniqKey="Montesinos L Pez A" first="Abelardo" last="Montesinos-L Pez">Abelardo Montesinos-L Pez</name><affiliation><nlm:affiliation>Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), 36240 Guanajuato, México.</nlm:affiliation></affiliation></author><author><name sortKey="Crossa, Jose" sort="Crossa, Jose" uniqKey="Crossa J" first="José" last="Crossa">José Crossa</name><affiliation><nlm:affiliation>Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México, Distrito Federal, México j.crossa@cgiar.org.</nlm:affiliation></affiliation></author><author><name sortKey="Burgue O, Juan" sort="Burgue O, Juan" uniqKey="Burgue O J" first="Juan" last="Burgue O">Juan Burgue O</name><affiliation><nlm:affiliation>Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México, Distrito Federal, México.</nlm:affiliation></affiliation></author><author><name sortKey="Eskridge, Kent" sort="Eskridge, Kent" uniqKey="Eskridge K" first="Kent" last="Eskridge">Kent Eskridge</name><affiliation><nlm:affiliation>University of Nebraska, Statistics Department, Lincoln, Nebraska 68583-0963.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2015">2015</date><idno type="RBID">pubmed:26290569</idno><idno type="pmid">26290569</idno><idno type="doi">10.1534/g3.115.021154</idno><idno type="wicri:Area/PubMed/Corpus">000071</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000071</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.</title><author><name sortKey="Montesinos L Pez, Osval A" sort="Montesinos L Pez, Osval A" uniqKey="Montesinos L Pez O" first="Osval A" last="Montesinos-L Pez">Osval A. Montesinos-L Pez</name><affiliation><nlm:affiliation>Facultad de Telemática, Universidad de Colima, C.P. 28040 Colima, Colima, México.</nlm:affiliation></affiliation></author><author><name sortKey="Montesinos L Pez, Abelardo" sort="Montesinos L Pez, Abelardo" uniqKey="Montesinos L Pez A" first="Abelardo" last="Montesinos-L Pez">Abelardo Montesinos-L Pez</name><affiliation><nlm:affiliation>Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), 36240 Guanajuato, México.</nlm:affiliation></affiliation></author><author><name sortKey="Crossa, Jose" sort="Crossa, Jose" uniqKey="Crossa J" first="José" last="Crossa">José Crossa</name><affiliation><nlm:affiliation>Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México, Distrito Federal, México j.crossa@cgiar.org.</nlm:affiliation></affiliation></author><author><name sortKey="Burgue O, Juan" sort="Burgue O, Juan" uniqKey="Burgue O J" first="Juan" last="Burgue O">Juan Burgue O</name><affiliation><nlm:affiliation>Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México, Distrito Federal, México.</nlm:affiliation></affiliation></author><author><name sortKey="Eskridge, Kent" sort="Eskridge, Kent" uniqKey="Eskridge K" first="Kent" last="Eskridge">Kent Eskridge</name><affiliation><nlm:affiliation>University of Nebraska, Statistics Department, Lincoln, Nebraska 68583-0963.</nlm:affiliation></affiliation></author></analytic><series><title level="j">G3 (Bethesda, Md.)</title><idno type="eISSN">2160-1836</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Bayes Theorem</term><term>Computer Simulation</term><term>Datasets as Topic</term><term>Genomics (methods)</term><term>Logistic Models</term><term>Models, Genetic</term><term>Plant Diseases (genetics)</term><term>Triticum (genetics)</term><term>Zea mays (genetics)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Plant Diseases</term><term>Triticum</term><term>Zea mays</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Genomics</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Bayes Theorem</term><term>Computer Simulation</term><term>Datasets as Topic</term><term>Logistic Models</term><term>Models, Genetic</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">26290569</PMID><DateCreated><Year>2015</Year><Month>10</Month><Day>06</Day></DateCreated><DateCompleted><Year>2016</Year><Month>04</Month><Day>06</Day></DateCompleted><DateRevised><Year>2015</Year><Month>10</Month><Day>16</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2160-1836</ISSN><JournalIssue CitedMedium="Internet"><Volume>5</Volume><Issue>10</Issue><PubDate><Year>2015</Year><Month>Oct</Month></PubDate></JournalIssue><Title>G3 (Bethesda, Md.)</Title><ISOAbbreviation>G3 (Bethesda)</ISOAbbreviation></Journal><ArticleTitle>Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.</ArticleTitle><Pagination><MedlinePgn>2113-26</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1534/g3.115.021154</ELocationID><Abstract><AbstractText>Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link.</AbstractText><CopyrightInformation>Copyright © 2015 Montesinos-López et al.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Montesinos-López</LastName><ForeName>Osval A</ForeName><Initials>OA</Initials><AffiliationInfo><Affiliation>Facultad de Telemática, Universidad de Colima, C.P. 28040 Colima, Colima, México.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Montesinos-López</LastName><ForeName>Abelardo</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), 36240 Guanajuato, México.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Crossa</LastName><ForeName>José</ForeName><Initials>J</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-9429-5855</Identifier><AffiliationInfo><Affiliation>Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México, Distrito Federal, México j.crossa@cgiar.org.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Burgueño</LastName><ForeName>Juan</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México, Distrito Federal, México.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Eskridge</LastName><ForeName>Kent</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>University of Nebraska, Statistics Department, Lincoln, Nebraska 68583-0963.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>08</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>G3 (Bethesda)</MedlineTA><NlmUniqueID>101566598</NlmUniqueID><ISSNLinking>2160-1836</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>J Anim Sci. 1980 Dec;51(6):1266-71</RefSource><PMID Version="1">7204270</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Biometrics. 1947 Mar;3(1):39-52</RefSource><PMID Version="1">20240416</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Genet Sel Evol. 2011;43:7</RefSource><PMID Version="1">21329522</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Anim Sci. 2011 Nov;89(11):3433-42</RefSource><PMID Version="1">21742941</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Genetics. 2012 Apr;190(4):1491-501</RefSource><PMID Version="1">22135352</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Theor Appl Genet. 2012 Aug;125(4):759-71</RefSource><PMID Version="1">22566067</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Heredity (Edinb). 2013 Mar;110(3):213-9</RefSource><PMID Version="1">23149458</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Genetics. 2013 Jul;194(3):573-96</RefSource><PMID Version="1">23636739</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Genetics. 2014 Oct;198(2):483-95</RefSource><PMID Version="1">25009151</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>G3 (Bethesda). 2015 Feb;5(2):291-300</RefSource><PMID Version="1">25538102</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D001499">Bayes Theorem</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003198">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D066264">Datasets as Topic</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D023281">Genomics</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D016015">Logistic Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D008957">Models, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010935">Plant Diseases</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000235">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014908">Triticum</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000235">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003313">Zea mays</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000235">genetics</QualifierName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4592994</OtherID><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Bayesian ordinal regression</Keyword><Keyword MajorTopicYN="N">GenPred</Keyword><Keyword MajorTopicYN="N">Gibbs sampler</Keyword><Keyword MajorTopicYN="N">genomic selection</Keyword><Keyword MajorTopicYN="N">logit</Keyword><Keyword MajorTopicYN="N">probit</Keyword><Keyword MajorTopicYN="N">shared data resource</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>8</Month><Day>21</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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