Strategies for genotype imputation in composite beef cattle.

Detalhes bibliográficos
Autor(a) principal: CHUD, T. C. S.
Data de Publicação: 2015
Outros Autores: VENTURA, R. V., SCHENKEL, F. S., CARVALHEIRO, R., BUZANSKAS, M. E., ROSA, J. O., MUDADU, M. de A., SILVA, M. V. G. B., MOKRY, F. B., MARCONDES, C. R., REGITANO, L. C. de A., MUNARI, D. P.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1029694
Resumo: Genotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. In Brazilian beef cattle production, many animals are resulting from crossbreeding and such an event may alter linkage disequilibrium patterns. Thus, the challenge is to obtain accurately imputed genotypes in crossbred animals. The objective of this study was to evaluate the best fitting and most accurate imputation strategy on the MA genetic group (the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows) and Canchim cattle. The data set contained 400 animals (born between 1999 and 2005) genotyped with the Illumina BovineHD panel. Imputation accuracy of genotypes from the Illumina-Bovine3K (3K), Illumina-BovineLD (6K), GeneSeek-Genomic-Profiler (GGP) BeefLD (GGP9K), GGP-IndicusLD (GGP20Ki), Illumina-BovineSNP50 (50K), GGP-IndicusHD (GGP75Ki), and GGP-BeefHD (GGP80K) to Illumina-BovineHD (HD) SNP panels were investigated. Seven scenarios for reference and target populations were tested; the animals were grouped according with birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7). Analyses were performed using FImpute and BEAGLE software and computation run-time was recorded. Genotype imputation accuracy was measured by concordance rate (CR) and allelic R square (R2).
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spelling Strategies for genotype imputation in composite beef cattle.Canchim breedCrossbred cattleGenomic dataLow density panelsingle nucleotide polymorphismGenotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. In Brazilian beef cattle production, many animals are resulting from crossbreeding and such an event may alter linkage disequilibrium patterns. Thus, the challenge is to obtain accurately imputed genotypes in crossbred animals. The objective of this study was to evaluate the best fitting and most accurate imputation strategy on the MA genetic group (the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows) and Canchim cattle. The data set contained 400 animals (born between 1999 and 2005) genotyped with the Illumina BovineHD panel. Imputation accuracy of genotypes from the Illumina-Bovine3K (3K), Illumina-BovineLD (6K), GeneSeek-Genomic-Profiler (GGP) BeefLD (GGP9K), GGP-IndicusLD (GGP20Ki), Illumina-BovineSNP50 (50K), GGP-IndicusHD (GGP75Ki), and GGP-BeefHD (GGP80K) to Illumina-BovineHD (HD) SNP panels were investigated. Seven scenarios for reference and target populations were tested; the animals were grouped according with birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7). Analyses were performed using FImpute and BEAGLE software and computation run-time was recorded. Genotype imputation accuracy was measured by concordance rate (CR) and allelic R square (R2).TATIANE C. S. CHUD, UNESP; RICARDO V. VENTURA, UNESP; FLAVIO S. SCHENKEL, University of Guelph; ROBERTO CARVALHEIRO, UNESP; MARCOS E. BUZANSKAS, UNESP; JAQUELINE O. ROSA, UNESP; MAURICIO DE ALVARENGA MUDADU, CPPSE; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; FABIANA B. MOKRY, Federal University of São Carlos; CINTIA RIGHETTI MARCONDES, CPPSE; LUCIANA CORREIA DE ALMEIDA REGITANO, CPPSE; DANÍSIO P. MUNARI, UNESP.CHUD, T. C. S.VENTURA, R. V.SCHENKEL, F. S.CARVALHEIRO, R.BUZANSKAS, M. E.ROSA, J. O.MUDADU, M. de A.SILVA, M. V. G. B.MOKRY, F. B.MARCONDES, C. R.REGITANO, L. C. de A.MUNARI, D. P.2023-03-15T17:58:32Z2023-03-15T17:58:32Z2015-11-262015info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10 p.BMC Genomics, v. 16, n. 99, 2015.http://www.alice.cnptia.embrapa.br/alice/handle/doc/102969410.1186/s12863-015-0251-7enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2023-03-15T17:58:32Zoai:www.alice.cnptia.embrapa.br:doc/1029694Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542023-03-15T17:58:32Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Strategies for genotype imputation in composite beef cattle.
title Strategies for genotype imputation in composite beef cattle.
spellingShingle Strategies for genotype imputation in composite beef cattle.
CHUD, T. C. S.
Canchim breed
Crossbred cattle
Genomic data
Low density panel
single nucleotide polymorphism
title_short Strategies for genotype imputation in composite beef cattle.
title_full Strategies for genotype imputation in composite beef cattle.
title_fullStr Strategies for genotype imputation in composite beef cattle.
title_full_unstemmed Strategies for genotype imputation in composite beef cattle.
title_sort Strategies for genotype imputation in composite beef cattle.
author CHUD, T. C. S.
author_facet CHUD, T. C. S.
VENTURA, R. V.
SCHENKEL, F. S.
CARVALHEIRO, R.
BUZANSKAS, M. E.
ROSA, J. O.
MUDADU, M. de A.
SILVA, M. V. G. B.
MOKRY, F. B.
MARCONDES, C. R.
REGITANO, L. C. de A.
MUNARI, D. P.
author_role author
author2 VENTURA, R. V.
SCHENKEL, F. S.
CARVALHEIRO, R.
BUZANSKAS, M. E.
ROSA, J. O.
MUDADU, M. de A.
SILVA, M. V. G. B.
MOKRY, F. B.
MARCONDES, C. R.
REGITANO, L. C. de A.
MUNARI, D. P.
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv TATIANE C. S. CHUD, UNESP; RICARDO V. VENTURA, UNESP; FLAVIO S. SCHENKEL, University of Guelph; ROBERTO CARVALHEIRO, UNESP; MARCOS E. BUZANSKAS, UNESP; JAQUELINE O. ROSA, UNESP; MAURICIO DE ALVARENGA MUDADU, CPPSE; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; FABIANA B. MOKRY, Federal University of São Carlos; CINTIA RIGHETTI MARCONDES, CPPSE; LUCIANA CORREIA DE ALMEIDA REGITANO, CPPSE; DANÍSIO P. MUNARI, UNESP.
dc.contributor.author.fl_str_mv CHUD, T. C. S.
VENTURA, R. V.
SCHENKEL, F. S.
CARVALHEIRO, R.
BUZANSKAS, M. E.
ROSA, J. O.
MUDADU, M. de A.
SILVA, M. V. G. B.
MOKRY, F. B.
MARCONDES, C. R.
REGITANO, L. C. de A.
MUNARI, D. P.
dc.subject.por.fl_str_mv Canchim breed
Crossbred cattle
Genomic data
Low density panel
single nucleotide polymorphism
topic Canchim breed
Crossbred cattle
Genomic data
Low density panel
single nucleotide polymorphism
description Genotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. In Brazilian beef cattle production, many animals are resulting from crossbreeding and such an event may alter linkage disequilibrium patterns. Thus, the challenge is to obtain accurately imputed genotypes in crossbred animals. The objective of this study was to evaluate the best fitting and most accurate imputation strategy on the MA genetic group (the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows) and Canchim cattle. The data set contained 400 animals (born between 1999 and 2005) genotyped with the Illumina BovineHD panel. Imputation accuracy of genotypes from the Illumina-Bovine3K (3K), Illumina-BovineLD (6K), GeneSeek-Genomic-Profiler (GGP) BeefLD (GGP9K), GGP-IndicusLD (GGP20Ki), Illumina-BovineSNP50 (50K), GGP-IndicusHD (GGP75Ki), and GGP-BeefHD (GGP80K) to Illumina-BovineHD (HD) SNP panels were investigated. Seven scenarios for reference and target populations were tested; the animals were grouped according with birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7). Analyses were performed using FImpute and BEAGLE software and computation run-time was recorded. Genotype imputation accuracy was measured by concordance rate (CR) and allelic R square (R2).
publishDate 2015
dc.date.none.fl_str_mv 2015-11-26
2015
2023-03-15T17:58:32Z
2023-03-15T17:58:32Z
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 BMC Genomics, v. 16, n. 99, 2015.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1029694
10.1186/s12863-015-0251-7
identifier_str_mv BMC Genomics, v. 16, n. 99, 2015.
10.1186/s12863-015-0251-7
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1029694
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 10 p.
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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