Strategies for genotype imputation in composite beef cattle.
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , , , , , , , , |
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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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 |
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Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
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EMBRAPA |
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EMBRAPA |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
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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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1822721606770753536 |