Genomic prediction with whole-genome sequence data in intensely selected pig lines

Background: Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled...

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Autores: Ros Freixedes, Roger, Johnsson, Martin, Whalen, Andrew, Chen, Ching-Yi, Valente, Bruno D., Herring, William O., Gorjanc, Gregor, Hickey, John M.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/84027
Acceso en línea:https://doi.org/10.1186/s12711‑022‑00756‑0
http://hdl.handle.net/10459.1/84027
Access Level:acceso abierto
Palabra clave:Porcs
Porcs--Cria i desenvolupament
Genètica animal
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spelling Genomic prediction with whole-genome sequence data in intensely selected pig linesRos Freixedes, RogerJohnsson, MartinWhalen, AndrewChen, Ching-YiValente, Bruno D.Herring, William O.Gorjanc, GregorHickey, John M.PorcsPorcs--Cria i desenvolupamentGenètica animalBackground: Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage. Methods: We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests. Results: The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected. Conclusions: Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.The authors acknowledge the financial support from the BBSRC ISPG to The Roslin Institute (BBS/E/D/30002275), from Genus plc, Innovate UK (grant 102271), and from grant numbers BB/N004736/1, BB/N015339/1, BB/ L020467/1, and BB/M009254/1. MJ acknowledges financial support from the Swedish Research Council for Sustainable Development Formas Dnr 2016‑ 01386. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any author accepted manuscript version arising from this submission.BMC2022202220222022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1186/s12711‑022‑00756‑0http://hdl.handle.net/10459.1/84027http://hdl.handle.net/10459.1/84027reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1186/s12711‑022‑00756‑0Genetics Selection Evolution, 2022, 54, Article number: 65cc-by (c) Ros et al., 2022info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/840272026-05-29T05:05:01Z
dc.title.none.fl_str_mv Genomic prediction with whole-genome sequence data in intensely selected pig lines
title Genomic prediction with whole-genome sequence data in intensely selected pig lines
spellingShingle Genomic prediction with whole-genome sequence data in intensely selected pig lines
Ros Freixedes, Roger
Porcs
Porcs--Cria i desenvolupament
Genètica animal
title_short Genomic prediction with whole-genome sequence data in intensely selected pig lines
title_full Genomic prediction with whole-genome sequence data in intensely selected pig lines
title_fullStr Genomic prediction with whole-genome sequence data in intensely selected pig lines
title_full_unstemmed Genomic prediction with whole-genome sequence data in intensely selected pig lines
title_sort Genomic prediction with whole-genome sequence data in intensely selected pig lines
dc.creator.none.fl_str_mv Ros Freixedes, Roger
Johnsson, Martin
Whalen, Andrew
Chen, Ching-Yi
Valente, Bruno D.
Herring, William O.
Gorjanc, Gregor
Hickey, John M.
author Ros Freixedes, Roger
author_facet Ros Freixedes, Roger
Johnsson, Martin
Whalen, Andrew
Chen, Ching-Yi
Valente, Bruno D.
Herring, William O.
Gorjanc, Gregor
Hickey, John M.
author_role author
author2 Johnsson, Martin
Whalen, Andrew
Chen, Ching-Yi
Valente, Bruno D.
Herring, William O.
Gorjanc, Gregor
Hickey, John M.
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Porcs
Porcs--Cria i desenvolupament
Genètica animal
topic Porcs
Porcs--Cria i desenvolupament
Genètica animal
description Background: Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage. Methods: We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests. Results: The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected. Conclusions: Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1186/s12711‑022‑00756‑0
http://hdl.handle.net/10459.1/84027
http://hdl.handle.net/10459.1/84027
url https://doi.org/10.1186/s12711‑022‑00756‑0
http://hdl.handle.net/10459.1/84027
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1186/s12711‑022‑00756‑0
Genetics Selection Evolution, 2022, 54, Article number: 65
dc.rights.none.fl_str_mv cc-by (c) Ros et al., 2022
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Ros et al., 2022
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv BMC
publisher.none.fl_str_mv BMC
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
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