| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Gado de Leite. |
|
Data corrente: |
01/12/2014 |
|
Data da última atualização: |
05/02/2024 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
BOISON, S. A.; NEVES, H. H. R.; O'BRIEN, A. M. P.; UTSUNOMIYA, Y. T.; CARVALHEIRO, R.; SILVA, M. V. G. B.; SÖLKNER, J.; GARCIA, J. F. |
|
Afiliação: |
S.A. BOISON, Univ Nat Resources & Life Sci. BOKU; H.H.R. NEVES, UNESP; A.M. PÉREZ O?BRIEN, Univ Nat Resources & Life Sci. BOKU; Y.T. UTSUNOMIYA, UNESP; R. CARVALHEIRO, UNESP; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; J. SÖLKNER, Univ Nat Resources & Life Sci. BOKU; J. F. GARCIA, UNESP. |
|
Título: |
Imputation of non-genotyped individuals using genotyped progeny in Nellore, a Bos indicus cattle breed. |
|
Ano de publicação: |
2014 |
|
Fonte/Imprenta: |
Livestock Science, v. 166, p. 176-189, 2014. |
|
DOI: |
https://doi.org/10.1016/j.livsci.2014.05.033 |
|
Idioma: |
Inglês |
|
Conteúdo: |
This study aimed at imputing non(un)-genotyped sires using a stepwise imputation approach that combines identity by descent (IBD) detection methods with other imputation algorithms. We also studied the effect of using actual or imputed genotypes of non-genotyped sires in estimating genomic relationships. Simulations and real data were used for the analysis. Fifty sire families were simulated and 23 sire families were derived from 995 Brazilian Nellore cattle genotyped with Illumina® Bovine HD (777,962 SNPs) SNP Chip. Un-genotyped sires were imputed using genotype information from progeny (5 or 10); progeny and grand offspring; a combination of progeny, mates of genotyped progeny and grand offspring; and the entire genotyped population. Stepwise imputation was done with an IBD detection method that uses simple inheritance rules (MERLIN) as a first step and subsequently with FImpute, MaCH or BEAGLE as the second step to infer genotypes that were not imputed unambiguously by MERLIN. The stepwise imputation procedure was compared to an approach that ignores the first step (MERLIN) but uses only prior pedigree information to impute non-genotyped animals. Imputation accuracy was assessed as percent of correctly called genotypes and the correlation between imputed and actual genotypes (in brackets). With real data, imputation accuracy ranged from 81.6% (0.856) to 97.4% (0.981) depending on the amount of genotyped information considered for the first step (MERLIN) and imputation algorithms used for the second step. Greater accuracies of imputing non-genotyped sires were obtained when the stepwise imputation approach was used with 10 genotyped offspring as the first step. The stepwise approach resulted in an increase of 1.2% (5 offsprings) and 4.7% (10 offsprings) in imputation accuracy. MaCH was more accurate in the second step, followed by FImpute then BEAGLE. Similar trends in imputation accuracy were observed for the simulated population. Generally, imputed genotypes were successfully used to estimate genomic relationships among close relatives but considerable bias was observed for true pairwise relationships of zero. In conclusion, high imputation accuracies can be achieved for non-genotyped animals when genotype information of 5 or 10 direct progeny is available for imputation. Performing preliminary IBD analysis and using non-ambiguous genotypes from that analysis in conventional imputation increased the imputation accuracy considerably. MenosThis study aimed at imputing non(un)-genotyped sires using a stepwise imputation approach that combines identity by descent (IBD) detection methods with other imputation algorithms. We also studied the effect of using actual or imputed genotypes of non-genotyped sires in estimating genomic relationships. Simulations and real data were used for the analysis. Fifty sire families were simulated and 23 sire families were derived from 995 Brazilian Nellore cattle genotyped with Illumina® Bovine HD (777,962 SNPs) SNP Chip. Un-genotyped sires were imputed using genotype information from progeny (5 or 10); progeny and grand offspring; a combination of progeny, mates of genotyped progeny and grand offspring; and the entire genotyped population. Stepwise imputation was done with an IBD detection method that uses simple inheritance rules (MERLIN) as a first step and subsequently with FImpute, MaCH or BEAGLE as the second step to infer genotypes that were not imputed unambiguously by MERLIN. The stepwise imputation procedure was compared to an approach that ignores the first step (MERLIN) but uses only prior pedigree information to impute non-genotyped animals. Imputation accuracy was assessed as percent of correctly called genotypes and the correlation between imputed and actual genotypes (in brackets). With real data, imputation accuracy ranged from 81.6% (0.856) to 97.4% (0.981) depending on the amount of genotyped information considered for the first step (MERLIN) and imputation alg... Mostrar Tudo |
|
Palavras-Chave: |
FImpute; Imputation; MaCH; MERLIN; Non(un)-genotyped. |
|
Thesaurus Nal: |
Beagle. |
|
Categoria do assunto: |
G Melhoramento Genético |
|
Marc: |
LEADER 03319naa a2200289 a 4500
001 2001135
005 2024-02-05
008 2014 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.livsci.2014.05.033$2DOI
100 1 $aBOISON, S. A.
245 $aImputation of non-genotyped individuals using genotyped progeny in Nellore, a Bos indicus cattle breed.$h[electronic resource]
260 $c2014
520 $aThis study aimed at imputing non(un)-genotyped sires using a stepwise imputation approach that combines identity by descent (IBD) detection methods with other imputation algorithms. We also studied the effect of using actual or imputed genotypes of non-genotyped sires in estimating genomic relationships. Simulations and real data were used for the analysis. Fifty sire families were simulated and 23 sire families were derived from 995 Brazilian Nellore cattle genotyped with Illumina® Bovine HD (777,962 SNPs) SNP Chip. Un-genotyped sires were imputed using genotype information from progeny (5 or 10); progeny and grand offspring; a combination of progeny, mates of genotyped progeny and grand offspring; and the entire genotyped population. Stepwise imputation was done with an IBD detection method that uses simple inheritance rules (MERLIN) as a first step and subsequently with FImpute, MaCH or BEAGLE as the second step to infer genotypes that were not imputed unambiguously by MERLIN. The stepwise imputation procedure was compared to an approach that ignores the first step (MERLIN) but uses only prior pedigree information to impute non-genotyped animals. Imputation accuracy was assessed as percent of correctly called genotypes and the correlation between imputed and actual genotypes (in brackets). With real data, imputation accuracy ranged from 81.6% (0.856) to 97.4% (0.981) depending on the amount of genotyped information considered for the first step (MERLIN) and imputation algorithms used for the second step. Greater accuracies of imputing non-genotyped sires were obtained when the stepwise imputation approach was used with 10 genotyped offspring as the first step. The stepwise approach resulted in an increase of 1.2% (5 offsprings) and 4.7% (10 offsprings) in imputation accuracy. MaCH was more accurate in the second step, followed by FImpute then BEAGLE. Similar trends in imputation accuracy were observed for the simulated population. Generally, imputed genotypes were successfully used to estimate genomic relationships among close relatives but considerable bias was observed for true pairwise relationships of zero. In conclusion, high imputation accuracies can be achieved for non-genotyped animals when genotype information of 5 or 10 direct progeny is available for imputation. Performing preliminary IBD analysis and using non-ambiguous genotypes from that analysis in conventional imputation increased the imputation accuracy considerably.
650 $aBeagle
653 $aFImpute
653 $aImputation
653 $aMaCH
653 $aMERLIN
653 $aNon(un)-genotyped
700 1 $aNEVES, H. H. R.
700 1 $aO'BRIEN, A. M. P.
700 1 $aUTSUNOMIYA, Y. T.
700 1 $aCARVALHEIRO, R.
700 1 $aSILVA, M. V. G. B.
700 1 $aSÖLKNER, J.
700 1 $aGARCIA, J. F.
773 $tLivestock Science$gv. 166, p. 176-189, 2014.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Gado de Leite (CNPGL) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
|
Data corrente: |
08/02/2017 |
|
Data da última atualização: |
06/06/2023 |
|
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
|
Autoria: |
JESUS, O. N. de; FALEIRO, F. G. |
|
Afiliação: |
ONILDO NUNES DE JESUS, CNPMF; FABIO GELAPE FALEIRO, CPAC. |
|
Título: |
Classificação botânica e biodiversidade. |
|
Ano de publicação: |
2016 |
|
Fonte/Imprenta: |
In: FALEIRO, F. G.; JUNQUEIRA, N. T. V. (Ed.). Maracujá: o produtor pergunta, a Embrapa responde. Brasília, DF: Embrapa, 2016. Cap.2. (Coleção 500 perguntas, 500 respostas). |
|
Idioma: |
Português |
|
Thesagro: |
Maracujá. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/155093/1/Classificacao-botanica-e-biodiversidade.pdf
|
|
Marc: |
LEADER 00509naa a2200133 a 4500
001 2063217
005 2023-06-06
008 2016 bl uuuu u00u1 u #d
100 1 $aJESUS, O. N. de
245 $aClassificação botânica e biodiversidade.$h[electronic resource]
260 $c2016
650 $aMaracujá
700 1 $aFALEIRO, F. G.
773 $tIn: FALEIRO, F. G.; JUNQUEIRA, N. T. V. (Ed.). Maracujá: o produtor pergunta, a Embrapa responde. Brasília, DF: Embrapa, 2016. Cap.2. (Coleção 500 perguntas, 500 respostas).
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Mandioca e Fruticultura (CNPMF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
