A Bayesian approach for constructing genetic maps when markers are miscoded
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1186/1297-9686-34-3-353 http://hdl.handle.net/11449/66939 |
Resumo: | The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species, using information on quantitative trait loci (QTL). A QTL analysis relies on accurate genetic marker maps. At present, most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often, however, there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented, and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error, the proposed approach provides more reliable inference on the genetic map. |
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Repositório Institucional da UNESP |
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A Bayesian approach for constructing genetic maps when markers are miscodedBayesian inferenceGenetic map constructionMiscoded genotypesmolecular markerBayesian analysisgeneticsselective breedingBayes theorembiological modelbrassica napuschromosome mapcomputer simulationdata analysisgene mappinggenetic databasegenetic markergenetic recombinationgenotypemethodologymultifactorial inheritancenonhumanphenotypequantitative traitquantitative trait locusreliabilitystatistical analysisrapeseedBrassicaBrassica napusBayes TheoremChromosome MappingComputer SimulationDatabases, GeneticGenetic MarkersGenotypeModels, GeneticQuantitative Trait, HeritableRecombination, GeneticThe advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species, using information on quantitative trait loci (QTL). A QTL analysis relies on accurate genetic marker maps. At present, most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often, however, there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented, and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error, the proposed approach provides more reliable inference on the genetic map.Department of Biostatistics UNESP, Botucatu, SPDepartments of Statistics and of Horticulture University of Winconsin, Madison, WIDepartments of Animal Science and of Biostatistics and Medical Informatics University of Wisconsin, Madison, WIDepartments of Animal Science and of Fisheries and Wildlife Michigan State University, East Lansing, MI 48824Department of Biostatistics UNESP, Botucatu, SPUniversidade Estadual Paulista (Unesp)University of WinconsinUniversity of WisconsinMichigan State UniversityRosa, Guilherme J. M. [UNESP]Yandell, Brian S.Gianola, Daniel2014-05-27T11:20:29Z2014-05-27T11:20:29Z2002-07-25info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article353-369application/pdfhttp://dx.doi.org/10.1186/1297-9686-34-3-353Genetics Selection Evolution, v. 34, n. 3, p. 353-369, 2002.0999-193Xhttp://hdl.handle.net/11449/6693910.1186/1297-9686-34-3-353WOS:0001765617000042-s2.0-00359831532-s2.0-0035983153.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengGenetics Selection Evolution3.743info:eu-repo/semantics/openAccess2024-01-11T06:33:33Zoai:repositorio.unesp.br:11449/66939Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-11T06:33:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Bayesian approach for constructing genetic maps when markers are miscoded |
title |
A Bayesian approach for constructing genetic maps when markers are miscoded |
spellingShingle |
A Bayesian approach for constructing genetic maps when markers are miscoded Rosa, Guilherme J. M. [UNESP] Bayesian inference Genetic map construction Miscoded genotypes molecular marker Bayesian analysis genetics selective breeding Bayes theorem biological model brassica napus chromosome map computer simulation data analysis gene mapping genetic database genetic marker genetic recombination genotype methodology multifactorial inheritance nonhuman phenotype quantitative trait quantitative trait locus reliability statistical analysis rapeseed Brassica Brassica napus Bayes Theorem Chromosome Mapping Computer Simulation Databases, Genetic Genetic Markers Genotype Models, Genetic Quantitative Trait, Heritable Recombination, Genetic |
title_short |
A Bayesian approach for constructing genetic maps when markers are miscoded |
title_full |
A Bayesian approach for constructing genetic maps when markers are miscoded |
title_fullStr |
A Bayesian approach for constructing genetic maps when markers are miscoded |
title_full_unstemmed |
A Bayesian approach for constructing genetic maps when markers are miscoded |
title_sort |
A Bayesian approach for constructing genetic maps when markers are miscoded |
author |
Rosa, Guilherme J. M. [UNESP] |
author_facet |
Rosa, Guilherme J. M. [UNESP] Yandell, Brian S. Gianola, Daniel |
author_role |
author |
author2 |
Yandell, Brian S. Gianola, Daniel |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) University of Winconsin University of Wisconsin Michigan State University |
dc.contributor.author.fl_str_mv |
Rosa, Guilherme J. M. [UNESP] Yandell, Brian S. Gianola, Daniel |
dc.subject.por.fl_str_mv |
Bayesian inference Genetic map construction Miscoded genotypes molecular marker Bayesian analysis genetics selective breeding Bayes theorem biological model brassica napus chromosome map computer simulation data analysis gene mapping genetic database genetic marker genetic recombination genotype methodology multifactorial inheritance nonhuman phenotype quantitative trait quantitative trait locus reliability statistical analysis rapeseed Brassica Brassica napus Bayes Theorem Chromosome Mapping Computer Simulation Databases, Genetic Genetic Markers Genotype Models, Genetic Quantitative Trait, Heritable Recombination, Genetic |
topic |
Bayesian inference Genetic map construction Miscoded genotypes molecular marker Bayesian analysis genetics selective breeding Bayes theorem biological model brassica napus chromosome map computer simulation data analysis gene mapping genetic database genetic marker genetic recombination genotype methodology multifactorial inheritance nonhuman phenotype quantitative trait quantitative trait locus reliability statistical analysis rapeseed Brassica Brassica napus Bayes Theorem Chromosome Mapping Computer Simulation Databases, Genetic Genetic Markers Genotype Models, Genetic Quantitative Trait, Heritable Recombination, Genetic |
description |
The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species, using information on quantitative trait loci (QTL). A QTL analysis relies on accurate genetic marker maps. At present, most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often, however, there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented, and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error, the proposed approach provides more reliable inference on the genetic map. |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002-07-25 2014-05-27T11:20:29Z 2014-05-27T11:20:29Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1186/1297-9686-34-3-353 Genetics Selection Evolution, v. 34, n. 3, p. 353-369, 2002. 0999-193X http://hdl.handle.net/11449/66939 10.1186/1297-9686-34-3-353 WOS:000176561700004 2-s2.0-0035983153 2-s2.0-0035983153.pdf |
url |
http://dx.doi.org/10.1186/1297-9686-34-3-353 http://hdl.handle.net/11449/66939 |
identifier_str_mv |
Genetics Selection Evolution, v. 34, n. 3, p. 353-369, 2002. 0999-193X 10.1186/1297-9686-34-3-353 WOS:000176561700004 2-s2.0-0035983153 2-s2.0-0035983153.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Genetics Selection Evolution 3.743 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
353-369 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803650232920047616 |