Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations

Autores
Gualdron Duarte, Jose Luis; Cantet, Rodolfo Juan Carlos; Bates, Ronald O.; Ernst, Catherine W.; Raney, Nancy E.; Steibel, Juan P.
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. Results: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements. The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. Conclusions: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.
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: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bates, Ronald O.. Michigan State University; Estados Unidos
Fil: Ernst, Catherine W.. Michigan State University; Estados Unidos
Fil: Raney, Nancy E.. Michigan State University; Estados Unidos
Fil: Steibel, Juan P.. Michigan State University; Estados Unidos
Materia
GENOME WIDE ASSOCIATION
MARKER VARIANCE
PIG GENOTYPE
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:11336/16317

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oai_identifier_str oai:ri.conicet.gov.ar:11336/16317
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluationsGualdron Duarte, Jose LuisCantet, Rodolfo Juan CarlosBates, Ronald O.Ernst, Catherine W.Raney, Nancy E.Steibel, Juan P.GENOME WIDE ASSOCIATIONMARKER VARIANCEPIG GENOTYPEhttps://purl.org/becyt/ford/4.2https://purl.org/becyt/ford/4Background: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. Results: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements. The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. Conclusions: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.Fil: Gualdron Duarte, Jose Luis. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bates, Ronald O.. Michigan State University; Estados UnidosFil: Ernst, Catherine W.. Michigan State University; Estados UnidosFil: Raney, Nancy E.. Michigan State University; Estados UnidosFil: Steibel, Juan P.. Michigan State University; Estados UnidosBiomed Central2014-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/16317Gualdron Duarte, Jose Luis; Cantet, Rodolfo Juan Carlos; Bates, Ronald O.; Ernst, Catherine W.; Raney, Nancy E.; et al.; Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations; Biomed Central; Bmc Bioinformatics; 15; 7-2014; 246-2561471-2105enginfo:eu-repo/semantics/altIdentifier/doi/10.1186/1471-2105-15-246info:eu-repo/semantics/altIdentifier/url/https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-246info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4112210/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 Técnicas2025-09-29T10:20:45Zoai:ri.conicet.gov.ar:11336/16317instacron: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 10:20:45.656CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
title Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
spellingShingle Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
Gualdron Duarte, Jose Luis
GENOME WIDE ASSOCIATION
MARKER VARIANCE
PIG GENOTYPE
title_short Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
title_full Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
title_fullStr Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
title_full_unstemmed Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
title_sort Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
dc.creator.none.fl_str_mv Gualdron Duarte, Jose Luis
Cantet, Rodolfo Juan Carlos
Bates, Ronald O.
Ernst, Catherine W.
Raney, Nancy E.
Steibel, Juan P.
author Gualdron Duarte, Jose Luis
author_facet Gualdron Duarte, Jose Luis
Cantet, Rodolfo Juan Carlos
Bates, Ronald O.
Ernst, Catherine W.
Raney, Nancy E.
Steibel, Juan P.
author_role author
author2 Cantet, Rodolfo Juan Carlos
Bates, Ronald O.
Ernst, Catherine W.
Raney, Nancy E.
Steibel, Juan P.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv GENOME WIDE ASSOCIATION
MARKER VARIANCE
PIG GENOTYPE
topic GENOME WIDE ASSOCIATION
MARKER VARIANCE
PIG GENOTYPE
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.2
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Background: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. Results: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements. The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. Conclusions: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.
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: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bates, Ronald O.. Michigan State University; Estados Unidos
Fil: Ernst, Catherine W.. Michigan State University; Estados Unidos
Fil: Raney, Nancy E.. Michigan State University; Estados Unidos
Fil: Steibel, Juan P.. Michigan State University; Estados Unidos
description Background: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. Results: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements. The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. Conclusions: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.
publishDate 2014
dc.date.none.fl_str_mv 2014-07
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/11336/16317
Gualdron Duarte, Jose Luis; Cantet, Rodolfo Juan Carlos; Bates, Ronald O.; Ernst, Catherine W.; Raney, Nancy E.; et al.; Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations; Biomed Central; Bmc Bioinformatics; 15; 7-2014; 246-256
1471-2105
url http://hdl.handle.net/11336/16317
identifier_str_mv Gualdron Duarte, Jose Luis; Cantet, Rodolfo Juan Carlos; Bates, Ronald O.; Ernst, Catherine W.; Raney, Nancy E.; et al.; Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations; Biomed Central; Bmc Bioinformatics; 15; 7-2014; 246-256
1471-2105
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1186/1471-2105-15-246
info:eu-repo/semantics/altIdentifier/url/https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-246
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4112210/
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 Biomed Central
publisher.none.fl_str_mv Biomed Central
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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