Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)

Autores
Martini, Johannes W. R.; Gao, Ning; Cardoso, Diercles F.; Wimmer, Valentin; Erbe, Malena; Cantet, Rodolfo Juan Carlos; Simianer, Henner
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: Epistasis marker effect models incorporating products of marker values as predictor variables in a linear regression approach (extended GBLUP, EGBLUP) have been assessed as potentially beneficial for genomic prediction, but their performance depends on marker coding. Although this fact has been recognized in literature, the nature of the problem has not been thoroughly investigated so far. Results: We illustrate how the choice of marker coding implicitly specifies the model of how effects of certain allele combinations at different loci contribute to the phenotype, and investigate coding-dependent properties of EGBLUP. Moreover, we discuss an alternative categorical epistasis model (CE) eliminating undesired properties of EGBLUP and show that the CE model can improve predictive ability. Finally, we demonstrate that the coding-dependent performance of EGBLUP offers the possibility to incorporate prior experimental information into the prediction method by adapting the coding to already available phenotypic records on other traits. Conclusion: Based on our results, for EGBLUP, a symmetric coding {−1,1} or {−1,0,1} should be preferred, whereas a standardization using allele frequencies should be avoided. Moreover, CE can be a valuable alternative since it does not possess the undesired theoretical properties of EGBLUP. However, which model performs best will depend on characteristics of the data and available prior information. Data from previous experiments can for instance be incorporated into the marker coding of EGBLUP.
Fil: Martini, Johannes W. R.. Georg-August University; Alemania
Fil: Gao, Ning. Georg-August University; Alemania
Fil: Cardoso, Diercles F.. Georg-August University; Alemania
Fil: Wimmer, Valentin. KWS SAAT SE; Alemania
Fil: Erbe, Malena. Georg-August University; Alemania
Fil: Cantet, Rodolfo Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Unidad Ejecutora de Investigaciones en Producción Animal. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Unidad Ejecutora de Investigaciones en Producción Animal; Argentina
Fil: Simianer, Henner. Georg-August University; Alemania
Materia
EPISTASIS MODEL
GENOMIC PREDICTION
INTERACTION
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:11336/59317
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/59317
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)Martini, Johannes W. R.Gao, NingCardoso, Diercles F.Wimmer, ValentinErbe, MalenaCantet, Rodolfo Juan CarlosSimianer, HennerEPISTASIS MODELGENOMIC PREDICTIONINTERACTIONhttps://purl.org/becyt/ford/4.2https://purl.org/becyt/ford/4Background: Epistasis marker effect models incorporating products of marker values as predictor variables in a linear regression approach (extended GBLUP, EGBLUP) have been assessed as potentially beneficial for genomic prediction, but their performance depends on marker coding. Although this fact has been recognized in literature, the nature of the problem has not been thoroughly investigated so far. Results: We illustrate how the choice of marker coding implicitly specifies the model of how effects of certain allele combinations at different loci contribute to the phenotype, and investigate coding-dependent properties of EGBLUP. Moreover, we discuss an alternative categorical epistasis model (CE) eliminating undesired properties of EGBLUP and show that the CE model can improve predictive ability. Finally, we demonstrate that the coding-dependent performance of EGBLUP offers the possibility to incorporate prior experimental information into the prediction method by adapting the coding to already available phenotypic records on other traits. Conclusion: Based on our results, for EGBLUP, a symmetric coding {−1,1} or {−1,0,1} should be preferred, whereas a standardization using allele frequencies should be avoided. Moreover, CE can be a valuable alternative since it does not possess the undesired theoretical properties of EGBLUP. However, which model performs best will depend on characteristics of the data and available prior information. Data from previous experiments can for instance be incorporated into the marker coding of EGBLUP.Fil: Martini, Johannes W. R.. Georg-August University; AlemaniaFil: Gao, Ning. Georg-August University; AlemaniaFil: Cardoso, Diercles F.. Georg-August University; AlemaniaFil: Wimmer, Valentin. KWS SAAT SE; AlemaniaFil: Erbe, Malena. Georg-August University; AlemaniaFil: Cantet, Rodolfo Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Unidad Ejecutora de Investigaciones en Producción Animal. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Unidad Ejecutora de Investigaciones en Producción Animal; ArgentinaFil: Simianer, Henner. Georg-August University; AlemaniaBioMed Central2017-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59317Martini, Johannes W. R.; Gao, Ning; Cardoso, Diercles F.; Wimmer, Valentin; Erbe, Malena; et al.; Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE); BioMed Central; BMC Bioinformatics; 18; 1; 1-20171471-2105CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1186/s12859-016-1439-1info:eu-repo/semantics/altIdentifier/url/https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1439-1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:35:47Zoai:ri.conicet.gov.ar:11336/59317instacron: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 10:35:48.06CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
title Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
spellingShingle Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
Martini, Johannes W. R.
EPISTASIS MODEL
GENOMIC PREDICTION
INTERACTION
title_short Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
title_full Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
title_fullStr Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
title_full_unstemmed Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
title_sort Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
dc.creator.none.fl_str_mv Martini, Johannes W. R.
Gao, Ning
Cardoso, Diercles F.
Wimmer, Valentin
Erbe, Malena
Cantet, Rodolfo Juan Carlos
Simianer, Henner
author Martini, Johannes W. R.
author_facet Martini, Johannes W. R.
Gao, Ning
Cardoso, Diercles F.
Wimmer, Valentin
Erbe, Malena
Cantet, Rodolfo Juan Carlos
Simianer, Henner
author_role author
author2 Gao, Ning
Cardoso, Diercles F.
Wimmer, Valentin
Erbe, Malena
Cantet, Rodolfo Juan Carlos
Simianer, Henner
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv EPISTASIS MODEL
GENOMIC PREDICTION
INTERACTION
topic EPISTASIS MODEL
GENOMIC PREDICTION
INTERACTION
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: Epistasis marker effect models incorporating products of marker values as predictor variables in a linear regression approach (extended GBLUP, EGBLUP) have been assessed as potentially beneficial for genomic prediction, but their performance depends on marker coding. Although this fact has been recognized in literature, the nature of the problem has not been thoroughly investigated so far. Results: We illustrate how the choice of marker coding implicitly specifies the model of how effects of certain allele combinations at different loci contribute to the phenotype, and investigate coding-dependent properties of EGBLUP. Moreover, we discuss an alternative categorical epistasis model (CE) eliminating undesired properties of EGBLUP and show that the CE model can improve predictive ability. Finally, we demonstrate that the coding-dependent performance of EGBLUP offers the possibility to incorporate prior experimental information into the prediction method by adapting the coding to already available phenotypic records on other traits. Conclusion: Based on our results, for EGBLUP, a symmetric coding {−1,1} or {−1,0,1} should be preferred, whereas a standardization using allele frequencies should be avoided. Moreover, CE can be a valuable alternative since it does not possess the undesired theoretical properties of EGBLUP. However, which model performs best will depend on characteristics of the data and available prior information. Data from previous experiments can for instance be incorporated into the marker coding of EGBLUP.
Fil: Martini, Johannes W. R.. Georg-August University; Alemania
Fil: Gao, Ning. Georg-August University; Alemania
Fil: Cardoso, Diercles F.. Georg-August University; Alemania
Fil: Wimmer, Valentin. KWS SAAT SE; Alemania
Fil: Erbe, Malena. Georg-August University; Alemania
Fil: Cantet, Rodolfo Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Unidad Ejecutora de Investigaciones en Producción Animal. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Unidad Ejecutora de Investigaciones en Producción Animal; Argentina
Fil: Simianer, Henner. Georg-August University; Alemania
description Background: Epistasis marker effect models incorporating products of marker values as predictor variables in a linear regression approach (extended GBLUP, EGBLUP) have been assessed as potentially beneficial for genomic prediction, but their performance depends on marker coding. Although this fact has been recognized in literature, the nature of the problem has not been thoroughly investigated so far. Results: We illustrate how the choice of marker coding implicitly specifies the model of how effects of certain allele combinations at different loci contribute to the phenotype, and investigate coding-dependent properties of EGBLUP. Moreover, we discuss an alternative categorical epistasis model (CE) eliminating undesired properties of EGBLUP and show that the CE model can improve predictive ability. Finally, we demonstrate that the coding-dependent performance of EGBLUP offers the possibility to incorporate prior experimental information into the prediction method by adapting the coding to already available phenotypic records on other traits. Conclusion: Based on our results, for EGBLUP, a symmetric coding {−1,1} or {−1,0,1} should be preferred, whereas a standardization using allele frequencies should be avoided. Moreover, CE can be a valuable alternative since it does not possess the undesired theoretical properties of EGBLUP. However, which model performs best will depend on characteristics of the data and available prior information. Data from previous experiments can for instance be incorporated into the marker coding of EGBLUP.
publishDate 2017
dc.date.none.fl_str_mv 2017-01
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/11336/59317
Martini, Johannes W. R.; Gao, Ning; Cardoso, Diercles F.; Wimmer, Valentin; Erbe, Malena; et al.; Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE); BioMed Central; BMC Bioinformatics; 18; 1; 1-2017
1471-2105
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59317
identifier_str_mv Martini, Johannes W. R.; Gao, Ning; Cardoso, Diercles F.; Wimmer, Valentin; Erbe, Malena; et al.; Genomic prediction with epistasis models: On the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE); BioMed Central; BMC Bioinformatics; 18; 1; 1-2017
1471-2105
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1186/s12859-016-1439-1
info:eu-repo/semantics/altIdentifier/url/https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1439-1
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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score 13.070432

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