Accuracy of genotype imputation in Nelore cattle
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
Outros Autores: | , , , , , , , , , , |
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. |
id |
UNSP_b9e3b817a681c85884fb68f5d87f3df3 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/117329 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
11 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 instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129092536500224 |