Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

Autores
Gualdron Duarte, Jose Luis; Bates, Ronald O.; Ernst, Catherine W.; Raney, Nancy E.; Cantet, Rodolfo Juan Carlos; Steibel, Juan P.
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: F2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios. Results: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90. Conclusions: Combining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.
Fil: Gualdron Duarte, Jose Luis. Michigan State University; Estados Unidos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bates, Ronald O.. Michigan State University; Estados Unidos
Fil: Ernst, Catherine W.. Michigan State University; Estados Unidos
Fil: Raney, Nancy E.. Michigan State University; Estados Unidos
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Steibel, Juan P.. Michigan State University; Estados Unidos
Materia
Genotype imputation
Tag SNPs
Pigs
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/26192
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/26192
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Genotype imputation accuracy in a F2 pig population using high density and low density SNP panelsGualdron Duarte, Jose LuisBates, Ronald O.Ernst, Catherine W.Raney, Nancy E.Cantet, Rodolfo Juan CarlosSteibel, Juan P.Genotype imputationTag SNPsPigshttps://purl.org/becyt/ford/4.2https://purl.org/becyt/ford/4Background: F2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios. Results: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90. Conclusions: Combining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.Fil: Gualdron Duarte, Jose Luis. Michigan State University; Estados Unidos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bates, Ronald O.. Michigan State University; Estados UnidosFil: Ernst, Catherine W.. Michigan State University; Estados UnidosFil: Raney, Nancy E.. Michigan State University; Estados UnidosFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Steibel, Juan P.. Michigan State University; Estados UnidosBioMed Central2013-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/26192Gualdron Duarte, Jose Luis; Bates, Ronald O.; Ernst, Catherine W.; Raney, Nancy E.; Cantet, Rodolfo Juan Carlos; et al.; Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels; BioMed Central; BMC Genetics; 14; 5-2013; 38-511471-2156CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1186/1471-2156-14-38info:eu-repo/semantics/altIdentifier/url/https://bmcgenet.biomedcentral.com/articles/10.1186/1471-2156-14-38info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T10:40:48Zoai:ri.conicet.gov.ar:11336/26192instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-17 10:40:48.757CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels
title Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels
spellingShingle Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels
Gualdron Duarte, Jose Luis
Genotype imputation
Tag SNPs
Pigs
title_short Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels
title_full Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels
title_fullStr Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels
title_full_unstemmed Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels
title_sort Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels
dc.creator.none.fl_str_mv Gualdron Duarte, Jose Luis
Bates, Ronald O.
Ernst, Catherine W.
Raney, Nancy E.
Cantet, Rodolfo Juan Carlos
Steibel, Juan P.
author Gualdron Duarte, Jose Luis
author_facet Gualdron Duarte, Jose Luis
Bates, Ronald O.
Ernst, Catherine W.
Raney, Nancy E.
Cantet, Rodolfo Juan Carlos
Steibel, Juan P.
author_role author
author2 Bates, Ronald O.
Ernst, Catherine W.
Raney, Nancy E.
Cantet, Rodolfo Juan Carlos
Steibel, Juan P.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Genotype imputation
Tag SNPs
Pigs
topic Genotype imputation
Tag SNPs
Pigs
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.2
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Background: F2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios. Results: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90. Conclusions: Combining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.
Fil: Gualdron Duarte, Jose Luis. Michigan State University; Estados Unidos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bates, Ronald O.. Michigan State University; Estados Unidos
Fil: Ernst, Catherine W.. Michigan State University; Estados Unidos
Fil: Raney, Nancy E.. Michigan State University; Estados Unidos
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Steibel, Juan P.. Michigan State University; Estados Unidos
description Background: F2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios. Results: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90. Conclusions: Combining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.
publishDate 2013
dc.date.none.fl_str_mv 2013-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/26192
Gualdron Duarte, Jose Luis; Bates, Ronald O.; Ernst, Catherine W.; Raney, Nancy E.; Cantet, Rodolfo Juan Carlos; et al.; Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels; BioMed Central; BMC Genetics; 14; 5-2013; 38-51
1471-2156
CONICET Digital
CONICET
url http://hdl.handle.net/11336/26192
identifier_str_mv Gualdron Duarte, Jose Luis; Bates, Ronald O.; Ernst, Catherine W.; Raney, Nancy E.; Cantet, Rodolfo Juan Carlos; et al.; Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels; BioMed Central; BMC Genetics; 14; 5-2013; 38-51
1471-2156
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1186/1471-2156-14-38
info:eu-repo/semantics/altIdentifier/url/https://bmcgenet.biomedcentral.com/articles/10.1186/1471-2156-14-38
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.990902

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