Meta-analysis of genome-wide association from genomic prediction models

Autores
Bernal Rubio, Yeni Liliana; Gualdron Duarte, Jose Luis; Bates, R. O.; Ernst, C. W.; Nonneman, D.; Rohrer, G. A.; King, A.; Shackelford, S. D.; Wheeler, T. L.; Cantet, Rodolfo Juan Carlos; Steibel, J. P.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.
Fil: Bernal Rubio, Yeni Liliana. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gualdron Duarte, Jose Luis. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bates, R. O.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina
Fil: Ernst, C. W.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina
Fil: Nonneman, D.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: Rohrer, G. A.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: King, A.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: Shackelford, S. D.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: Wheeler, T. L.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: Cantet, Rodolfo Juan Carlos. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Steibel, J. P.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados Unidos
Materia
Gblup
Genome-Wide Association Studies
Multiple Populations
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/30191
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/30191
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Meta-analysis of genome-wide association from genomic prediction modelsBernal Rubio, Yeni LilianaGualdron Duarte, Jose LuisBates, R. O.Ernst, C. W.Nonneman, D.Rohrer, G. A.King, A.Shackelford, S. D.Wheeler, T. L.Cantet, Rodolfo Juan CarlosSteibel, J. P.GblupGenome-Wide Association StudiesMultiple Populationshttps://purl.org/becyt/ford/4.2https://purl.org/becyt/ford/4Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.Fil: Bernal Rubio, Yeni Liliana. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gualdron Duarte, Jose Luis. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bates, R. O.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; ArgentinaFil: Ernst, C. W.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; ArgentinaFil: Nonneman, D.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Rohrer, G. A.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: King, A.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Shackelford, S. D.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Wheeler, T. L.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Cantet, Rodolfo Juan Carlos. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Steibel, J. P.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados UnidosWiley2015-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/30191Bernal Rubio, Yeni Liliana; Gualdron Duarte, Jose Luis; Bates, R. O.; Ernst, C. W.; Nonneman, D.; et al.; Meta-analysis of genome-wide association from genomic prediction models; Wiley; Animal Genetics; 47; 1; 11-2015; 36-480268-9146CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/age.12378info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1111/age.12378/abstractinfo:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4738412/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:12Zoai:ri.conicet.gov.ar:11336/30191instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:33:13.127CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Meta-analysis of genome-wide association from genomic prediction models
title Meta-analysis of genome-wide association from genomic prediction models
spellingShingle Meta-analysis of genome-wide association from genomic prediction models
Bernal Rubio, Yeni Liliana
Gblup
Genome-Wide Association Studies
Multiple Populations
title_short Meta-analysis of genome-wide association from genomic prediction models
title_full Meta-analysis of genome-wide association from genomic prediction models
title_fullStr Meta-analysis of genome-wide association from genomic prediction models
title_full_unstemmed Meta-analysis of genome-wide association from genomic prediction models
title_sort Meta-analysis of genome-wide association from genomic prediction models
dc.creator.none.fl_str_mv Bernal Rubio, Yeni Liliana
Gualdron Duarte, Jose Luis
Bates, R. O.
Ernst, C. W.
Nonneman, D.
Rohrer, G. A.
King, A.
Shackelford, S. D.
Wheeler, T. L.
Cantet, Rodolfo Juan Carlos
Steibel, J. P.
author Bernal Rubio, Yeni Liliana
author_facet Bernal Rubio, Yeni Liliana
Gualdron Duarte, Jose Luis
Bates, R. O.
Ernst, C. W.
Nonneman, D.
Rohrer, G. A.
King, A.
Shackelford, S. D.
Wheeler, T. L.
Cantet, Rodolfo Juan Carlos
Steibel, J. P.
author_role author
author2 Gualdron Duarte, Jose Luis
Bates, R. O.
Ernst, C. W.
Nonneman, D.
Rohrer, G. A.
King, A.
Shackelford, S. D.
Wheeler, T. L.
Cantet, Rodolfo Juan Carlos
Steibel, J. P.
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Gblup
Genome-Wide Association Studies
Multiple Populations
topic Gblup
Genome-Wide Association Studies
Multiple Populations
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.2
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.
Fil: Bernal Rubio, Yeni Liliana. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gualdron Duarte, Jose Luis. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bates, R. O.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina
Fil: Ernst, C. W.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina
Fil: Nonneman, D.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: Rohrer, G. A.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: King, A.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: Shackelford, S. D.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: Wheeler, T. L.. United States Department of Agriculture. Agricultural Research Service; Estados Unidos
Fil: Cantet, Rodolfo Juan Carlos. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Steibel, J. P.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados Unidos
description Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.
publishDate 2015
dc.date.none.fl_str_mv 2015-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/30191
Bernal Rubio, Yeni Liliana; Gualdron Duarte, Jose Luis; Bates, R. O.; Ernst, C. W.; Nonneman, D.; et al.; Meta-analysis of genome-wide association from genomic prediction models; Wiley; Animal Genetics; 47; 1; 11-2015; 36-48
0268-9146
CONICET Digital
CONICET
url http://hdl.handle.net/11336/30191
identifier_str_mv Bernal Rubio, Yeni Liliana; Gualdron Duarte, Jose Luis; Bates, R. O.; Ernst, C. W.; Nonneman, D.; et al.; Meta-analysis of genome-wide association from genomic prediction models; Wiley; Animal Genetics; 47; 1; 11-2015; 36-48
0268-9146
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1111/age.12378
info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1111/age.12378/abstract
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4738412/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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