Accuracy of genotype imputation in Nelore cattle

Detalhes bibliográficos
Autor(a) principal: Carvalheiro, Roberto [UNESP]
Data de Publicação: 2014
Outros Autores: Boison, Solomon A., Neves, Haroldo H. R. [UNESP], Sargolzaei, Mehdi, Schenkel, Flavio S., Utsunomiya, Yuri T. [UNESP], O'Brien, Ana Maria Perez, Soelkner, Johann, McEwan, John C., Van Tassell, Curtis P., Sonstegard, Tad S., Fernando Garcia, Jose [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1186/s12711-014-0069-1
http://hdl.handle.net/11449/117329
Resumo: Background: Genotype imputation from low-density (LD) to high-density single nucleotide polymorphism (SNP) chips is an important step before applying genomic selection, since denser chips tend to provide more reliable genomic predictions. Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. Bos indicus cattle (e.g. Nelore breed) are characterized, in general, by lower levels of linkage disequilibrium between genetic markers at short distances, compared to taurine breeds. Thus, it is important to evaluate the accuracy of imputation to better define which imputation method and chip are most appropriate for genomic applications in indicine breeds.Methods: Accuracy of genotype imputation in Nelore cattle was evaluated using different LD chips, imputation software and sets of animals. Twelve commercial and customized LD chips with densities ranging from 7 K to 75 K were tested. Customized LD chips were virtually designed taking into account minor allele frequency, linkage disequilibrium and distance between markers. Software programs Flmpute and BEAGLE were applied to impute genotypes. From 995 bulls and 1247 cows that were genotyped with the Illumina (R) BovineHD chip (HD), 793 sires composed the reference set, and the remaining 202 younger sires and all the cows composed two separate validation sets for which genotypes were masked except for the SNPs of the LD chip that were to be tested.Results: Imputation accuracy increased with the SNP density of the LD chip. However, the gain in accuracy with LD chips with more than 15 K SNPs was relatively small because accuracy was already high at this density. Commercial and customized LD chips with equivalent densities presented similar results. Flmpute outperformed BEAGLE for all LD chips and validation sets. Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip.Conclusions: If the Illumina (R) BovineHD is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the animals with a chip containing around 15 K useful SNPs and imputing their high-density missing genotypes with Flmpute.
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spelling Accuracy of genotype imputation in Nelore cattleBackground: Genotype imputation from low-density (LD) to high-density single nucleotide polymorphism (SNP) chips is an important step before applying genomic selection, since denser chips tend to provide more reliable genomic predictions. Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. Bos indicus cattle (e.g. Nelore breed) are characterized, in general, by lower levels of linkage disequilibrium between genetic markers at short distances, compared to taurine breeds. Thus, it is important to evaluate the accuracy of imputation to better define which imputation method and chip are most appropriate for genomic applications in indicine breeds.Methods: Accuracy of genotype imputation in Nelore cattle was evaluated using different LD chips, imputation software and sets of animals. Twelve commercial and customized LD chips with densities ranging from 7 K to 75 K were tested. Customized LD chips were virtually designed taking into account minor allele frequency, linkage disequilibrium and distance between markers. Software programs Flmpute and BEAGLE were applied to impute genotypes. From 995 bulls and 1247 cows that were genotyped with the Illumina (R) BovineHD chip (HD), 793 sires composed the reference set, and the remaining 202 younger sires and all the cows composed two separate validation sets for which genotypes were masked except for the SNPs of the LD chip that were to be tested.Results: Imputation accuracy increased with the SNP density of the LD chip. However, the gain in accuracy with LD chips with more than 15 K SNPs was relatively small because accuracy was already high at this density. Commercial and customized LD chips with equivalent densities presented similar results. Flmpute outperformed BEAGLE for all LD chips and validation sets. Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip.Conclusions: If the Illumina (R) BovineHD is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the animals with a chip containing around 15 K useful SNPs and imputing their high-density missing genotypes with Flmpute.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Estadual Paulista, Fac Ciencias Agr & Vet, BR-14884900 Jaboticabal, SP, BrazilUniv Nat Resources & Life Sci, Dept Sustainable Agr Syst BOKU, Div Livestock Sci, A-1180 Vienna, AustriaGenSys Consultores Associados S C Ltda, BR-90680000 Porto Alegre, RS, BrazilUniv Guelph, Ctr Genet Improvement Livestock, Guelph, ON N1G 2W1, CanadaSemex Alliance, Guelph, ON, CanadaAgResearch, Ctr Reprod & Gen, Invermay, Mosgiel, New ZealandUSDA ARS, Bovine Funct Genom Lab, Beltsville, MD 20705 USAUniv Estadual Paulista, Fac Med Vet Aracatuba, BR-16050680 Aracatuba, SP, BrazilUniv Estadual Paulista, Fac Ciencias Agr & Vet, BR-14884900 Jaboticabal, SP, BrazilUniv Estadual Paulista, Fac Med Vet Aracatuba, BR-16050680 Aracatuba, SP, BrazilCNPq: 560922/2010-8Biomed Central LtdUniversidade Estadual Paulista (Unesp)Univ Nat Resources & Life SciGenSys Consultores Associados S C LtdaUniv GuelphSemex AllianceAgResearchUSDA ARSCarvalheiro, Roberto [UNESP]Boison, Solomon A.Neves, Haroldo H. R. [UNESP]Sargolzaei, MehdiSchenkel, Flavio S.Utsunomiya, Yuri T. [UNESP]O'Brien, Ana Maria PerezSoelkner, JohannMcEwan, John C.Van Tassell, Curtis P.Sonstegard, Tad S.Fernando Garcia, Jose [UNESP]2015-03-18T15:55:51Z2015-03-18T15:55:51Z2014-10-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfhttp://dx.doi.org/10.1186/s12711-014-0069-1Genetics Selection Evolution. London: Biomed Central Ltd, v. 46, 11 p., 2014.0999-193Xhttp://hdl.handle.net/11449/11732910.1186/s12711-014-0069-1WOS:000344227600001WOS000344227600001.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengGenetics Selection Evolution3.743info:eu-repo/semantics/openAccess2023-12-06T06:14:26Zoai:repositorio.unesp.br:11449/117329Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:35:29.331166Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Accuracy of genotype imputation in Nelore cattle
title Accuracy of genotype imputation in Nelore cattle
spellingShingle Accuracy of genotype imputation in Nelore cattle
Carvalheiro, Roberto [UNESP]
title_short Accuracy of genotype imputation in Nelore cattle
title_full Accuracy of genotype imputation in Nelore cattle
title_fullStr Accuracy of genotype imputation in Nelore cattle
title_full_unstemmed Accuracy of genotype imputation in Nelore cattle
title_sort Accuracy of genotype imputation in Nelore cattle
author Carvalheiro, Roberto [UNESP]
author_facet Carvalheiro, Roberto [UNESP]
Boison, Solomon A.
Neves, Haroldo H. R. [UNESP]
Sargolzaei, Mehdi
Schenkel, Flavio S.
Utsunomiya, Yuri T. [UNESP]
O'Brien, Ana Maria Perez
Soelkner, Johann
McEwan, John C.
Van Tassell, Curtis P.
Sonstegard, Tad S.
Fernando Garcia, Jose [UNESP]
author_role author
author2 Boison, Solomon A.
Neves, Haroldo H. R. [UNESP]
Sargolzaei, Mehdi
Schenkel, Flavio S.
Utsunomiya, Yuri T. [UNESP]
O'Brien, Ana Maria Perez
Soelkner, Johann
McEwan, John C.
Van Tassell, Curtis P.
Sonstegard, Tad S.
Fernando Garcia, Jose [UNESP]
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Univ Nat Resources & Life Sci
GenSys Consultores Associados S C Ltda
Univ Guelph
Semex Alliance
AgResearch
USDA ARS
dc.contributor.author.fl_str_mv Carvalheiro, Roberto [UNESP]
Boison, Solomon A.
Neves, Haroldo H. R. [UNESP]
Sargolzaei, Mehdi
Schenkel, Flavio S.
Utsunomiya, Yuri T. [UNESP]
O'Brien, Ana Maria Perez
Soelkner, Johann
McEwan, John C.
Van Tassell, Curtis P.
Sonstegard, Tad S.
Fernando Garcia, Jose [UNESP]
description Background: Genotype imputation from low-density (LD) to high-density single nucleotide polymorphism (SNP) chips is an important step before applying genomic selection, since denser chips tend to provide more reliable genomic predictions. Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. Bos indicus cattle (e.g. Nelore breed) are characterized, in general, by lower levels of linkage disequilibrium between genetic markers at short distances, compared to taurine breeds. Thus, it is important to evaluate the accuracy of imputation to better define which imputation method and chip are most appropriate for genomic applications in indicine breeds.Methods: Accuracy of genotype imputation in Nelore cattle was evaluated using different LD chips, imputation software and sets of animals. Twelve commercial and customized LD chips with densities ranging from 7 K to 75 K were tested. Customized LD chips were virtually designed taking into account minor allele frequency, linkage disequilibrium and distance between markers. Software programs Flmpute and BEAGLE were applied to impute genotypes. From 995 bulls and 1247 cows that were genotyped with the Illumina (R) BovineHD chip (HD), 793 sires composed the reference set, and the remaining 202 younger sires and all the cows composed two separate validation sets for which genotypes were masked except for the SNPs of the LD chip that were to be tested.Results: Imputation accuracy increased with the SNP density of the LD chip. However, the gain in accuracy with LD chips with more than 15 K SNPs was relatively small because accuracy was already high at this density. Commercial and customized LD chips with equivalent densities presented similar results. Flmpute outperformed BEAGLE for all LD chips and validation sets. Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip.Conclusions: If the Illumina (R) BovineHD is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the animals with a chip containing around 15 K useful SNPs and imputing their high-density missing genotypes with Flmpute.
publishDate 2014
dc.date.none.fl_str_mv 2014-10-10
2015-03-18T15:55:51Z
2015-03-18T15:55:51Z
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/s12711-014-0069-1
Genetics Selection Evolution. London: Biomed Central Ltd, v. 46, 11 p., 2014.
0999-193X
http://hdl.handle.net/11449/117329
10.1186/s12711-014-0069-1
WOS:000344227600001
WOS000344227600001.pdf
url http://dx.doi.org/10.1186/s12711-014-0069-1
http://hdl.handle.net/11449/117329
identifier_str_mv Genetics Selection Evolution. London: Biomed Central Ltd, v. 46, 11 p., 2014.
0999-193X
10.1186/s12711-014-0069-1
WOS:000344227600001
WOS000344227600001.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Genetics Selection Evolution
3.743
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application/pdf
dc.publisher.none.fl_str_mv Biomed Central Ltd
publisher.none.fl_str_mv Biomed Central Ltd
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
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