Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels

The objectives of this study were: (i) to assess genotype imputation accuracy in different scenarios using genome-wide single nucleotide polymorphisms (SNP) data from a population comprising two generations of farmed Atlantic salmon and (ii) to assess the accuracy of genomic predictions for a quanti...

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Autores: Yoshida, Grazyella M. [UNESP], Carvalheiro, Roberto [UNESP], Lhorente, Jean P., Correa, Katharina, Figueroa, René, Houston, Ross D., Yáñez, José M.
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2018
País:Brasil
Recursos:Universidade Estadual Paulista (UNESP)
Repositório:Repositório Institucional da UNESP
Idioma:inglês
OAI Identifier:oai:repositorio.unesp.br:11449/170787
Acesso em linha:http://dx.doi.org/10.1016/j.aquaculture.2018.03.004
http://hdl.handle.net/11449/170787
Access Level:Acceso aberto
Palavra-chave:Cost-effectiveness
Genome-wide association studies
Genomic selection
Salmo salar
Single nucleotide polymorphism
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repository_id_str
dc.title.none.fl_str_mv Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels
title Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels
spellingShingle Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels
Yoshida, Grazyella M. [UNESP]
Cost-effectiveness
Genome-wide association studies
Genomic selection
Salmo salar
Single nucleotide polymorphism
title_short Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels
title_full Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels
title_fullStr Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels
title_full_unstemmed Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels
title_sort Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels
dc.creator.none.fl_str_mv Yoshida, Grazyella M. [UNESP]
Carvalheiro, Roberto [UNESP]
Lhorente, Jean P.
Correa, Katharina
Figueroa, René
Houston, Ross D.
Yáñez, José M.
author Yoshida, Grazyella M. [UNESP]
author_facet Yoshida, Grazyella M. [UNESP]
Carvalheiro, Roberto [UNESP]
Lhorente, Jean P.
Correa, Katharina
Figueroa, René
Houston, Ross D.
Yáñez, José M.
author_role author
author2 Carvalheiro, Roberto [UNESP]
Lhorente, Jean P.
Correa, Katharina
Figueroa, René
Houston, Ross D.
Yáñez, José M.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Chile
Universidade Estadual Paulista (Unesp)
Aquainnovo
University of Edinburgh
Núcleo Milenio INVASAL
dc.subject.por.fl_str_mv Cost-effectiveness
Genome-wide association studies
Genomic selection
Salmo salar
Single nucleotide polymorphism
topic Cost-effectiveness
Genome-wide association studies
Genomic selection
Salmo salar
Single nucleotide polymorphism
description The objectives of this study were: (i) to assess genotype imputation accuracy in different scenarios using genome-wide single nucleotide polymorphisms (SNP) data from a population comprising two generations of farmed Atlantic salmon and (ii) to assess the accuracy of genomic predictions for a quantitative trait (body weight) using the imputed genotypes. The pedigree consisted of 53 parents and 1069 offspring genotyped using a high-density SNP panel (50 K). Two groups were created: Group A: 90% of the offspring were included into training and 10% into validation sets; Group B: 10% of the offspring were included into training and 90% into validation sets. Different scenarios of available genotypic information from relatives were tested for the two groups previously described. Imputation was performed using three in silico low-density panels (0.5, 3 and 6 K) with all markers except the markers present on the low-density panel masked in the validation sets. The accuracy of genomic selection was tested using the scenarios that resulted in the best and the worst imputation accuracy for the three low density panels and were compared to accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions using the 50 K SNP panel. In general, imputation accuracy ranged from 0.74 to 0.98 depending on scenario. For the best scenario with the highest number of animals in reference population (Group A), the accuracy of imputation ranged from 0.95 to 0.98 depending on the low-density panel used. For the best scenario with the lowest number of animals in reference population (Group B), the accuracy of imputation ranged from 0.94 to 0.98 depending on the low-density panel used. In general, the number of SNPs in the low-density panels had a greater influence on the accuracy of imputation than the size of the reference set. The accuracies of genomic predictions using imputed genotypes, ranging from 0.71 to 0.73, outperformed PBLUP (0.66) and were identical or very similar to the use of all true genotype data (0.73). The high imputation and genomic prediction accuracy suggest that the imputation of genotypes from low density (0.5 to 3 K) to high density (50 K) could be a cost-effective strategy for the feasibility of the practical implementation of genomic selection in Atlantic salmon.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-11T16:52:25Z
2018-12-11T16:52:25Z
2018-04-01
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/10.1016/j.aquaculture.2018.03.004
Aquaculture, v. 491, p. 147-154.
0044-8486
http://hdl.handle.net/11449/170787
10.1016/j.aquaculture.2018.03.004
2-s2.0-85044099550
2-s2.0-85044099550.pdf
url http://dx.doi.org/10.1016/j.aquaculture.2018.03.004
http://hdl.handle.net/11449/170787
identifier_str_mv Aquaculture, v. 491, p. 147-154.
0044-8486
10.1016/j.aquaculture.2018.03.004
2-s2.0-85044099550
2-s2.0-85044099550.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Aquaculture
1,152
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 147-154
application/pdf
dc.source.none.fl_str_mv Scopus
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 repositoriounesp@unesp.br
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spelling Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panelsCost-effectivenessGenome-wide association studiesGenomic selectionSalmo salarSingle nucleotide polymorphismThe objectives of this study were: (i) to assess genotype imputation accuracy in different scenarios using genome-wide single nucleotide polymorphisms (SNP) data from a population comprising two generations of farmed Atlantic salmon and (ii) to assess the accuracy of genomic predictions for a quantitative trait (body weight) using the imputed genotypes. The pedigree consisted of 53 parents and 1069 offspring genotyped using a high-density SNP panel (50 K). Two groups were created: Group A: 90% of the offspring were included into training and 10% into validation sets; Group B: 10% of the offspring were included into training and 90% into validation sets. Different scenarios of available genotypic information from relatives were tested for the two groups previously described. Imputation was performed using three in silico low-density panels (0.5, 3 and 6 K) with all markers except the markers present on the low-density panel masked in the validation sets. The accuracy of genomic selection was tested using the scenarios that resulted in the best and the worst imputation accuracy for the three low density panels and were compared to accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions using the 50 K SNP panel. In general, imputation accuracy ranged from 0.74 to 0.98 depending on scenario. For the best scenario with the highest number of animals in reference population (Group A), the accuracy of imputation ranged from 0.95 to 0.98 depending on the low-density panel used. For the best scenario with the lowest number of animals in reference population (Group B), the accuracy of imputation ranged from 0.94 to 0.98 depending on the low-density panel used. In general, the number of SNPs in the low-density panels had a greater influence on the accuracy of imputation than the size of the reference set. The accuracies of genomic predictions using imputed genotypes, ranging from 0.71 to 0.73, outperformed PBLUP (0.66) and were identical or very similar to the use of all true genotype data (0.73). The high imputation and genomic prediction accuracy suggest that the imputation of genotypes from low density (0.5 to 3 K) to high density (50 K) could be a cost-effective strategy for the feasibility of the practical implementation of genomic selection in Atlantic salmon.Facultad de Ciencias Veterinarias y Pecuarias Universidad de Chile, Av. Santa Rosa 11735School of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), Jaboticabal, Via de Acesso Prof. Paulo Donato CastellaneAquainnovo, Cardonal S/NThe Roslin Institute and Royal (Dick) School of Veterinary Studies University of EdinburghNúcleo Milenio INVASALSchool of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), Jaboticabal, Via de Acesso Prof. Paulo Donato CastellaneUniversidad de ChileUniversidade Estadual Paulista (Unesp)AquainnovoUniversity of EdinburghNúcleo Milenio INVASAL2018-12-11T16:52:25Z2018-12-11T16:52:25Z2018-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article147-154application/pdfhttp://dx.doi.org/10.1016/j.aquaculture.2018.03.004Aquaculture, v. 491, p. 147-154.0044-8486http://hdl.handle.net/11449/17078710.1016/j.aquaculture.2018.03.0042-s2.0-850440995502-s2.0-85044099550.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAquaculture1,152info:eu-repo/semantics/openAccessYoshida, Grazyella M. [UNESP]Carvalheiro, Roberto [UNESP]Lhorente, Jean P.Correa, KatharinaFigueroa, RenéHouston, Ross D.Yáñez, José M.2025-10-22T13:19:31Zoai:repositorio.unesp.br:11449/170787Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462025-10-22T13:19:31Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
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