Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.

The popularity of genomic selection as an efficient and cost-effective approach to estimate breeding values continues to increase, due in part to the significant saving in phenotyping. Ridge regression is one of the most popular methods used for genomic prediction; however, its efficiency (in terms...

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Detalles Bibliográficos
Autores: Montesinos-López, Abelardo, Montesinos-López, Osval A., Lecumberry, Federico, Fariello, Maria Ines, Montesinos-López, José C., Crossa, José
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:Uruguay
Institución:Universidad de la República
Repositorio:COLIBRI
Idioma:inglés
OAI Identifier:oai:colibri.udelar.edu.uy:20.500.12008/46936
Acceso en línea:https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkae246/7888815
https://hdl.handle.net/20.500.12008/46936
Access Level:acceso abierto
Palabra clave:Ridge regression
Genomic prediction
GenPred
Shared Data Resource
Plant breeding
Breeding values
Penalized regression
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dc.title.none.fl_str_mv Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.
title Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.
spellingShingle Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.
Montesinos-López, Abelardo
Ridge regression
Genomic prediction
GenPred
Shared Data Resource
Plant breeding
Breeding values
Penalized regression
title_short Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.
title_full Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.
title_fullStr Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.
title_full_unstemmed Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.
title_sort Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.
dc.creator.none.fl_str_mv Montesinos-López, Abelardo
Montesinos-López, Osval A.
Lecumberry, Federico
Fariello, Maria Ines
Montesinos-López, José C.
Crossa, José
author Montesinos-López, Abelardo
author_facet Montesinos-López, Abelardo
Montesinos-López, Osval A.
Lecumberry, Federico
Fariello, Maria Ines
Montesinos-López, José C.
Crossa, José
author_role author
author2 Montesinos-López, Osval A.
Lecumberry, Federico
Fariello, Maria Ines
Montesinos-López, José C.
Crossa, José
author2_role author
author
author
author
author
dc.contributor.filiacion.none.fl_str_mv Montesinos-López Abelardo, Universidad de Guadalajara, Jalisco, México
Montesinos-López Osval A., Universidad de Colima, Colima, México
Lecumberry Federico, Universidad de la República (Uruguay). Facultad de Ingeniería.
Fariello Maria Ines, Universidad de la República (Uruguay). Facultad de Ingeniería.
Montesinos-López José C., University of California Davis, CA, USA
Crossa José, Louisiana State University, LA, USA
dc.subject.es.fl_str_mv Ridge regression
Genomic prediction
GenPred
Shared Data Resource
Plant breeding
Breeding values
Penalized regression
topic Ridge regression
Genomic prediction
GenPred
Shared Data Resource
Plant breeding
Breeding values
Penalized regression
description The popularity of genomic selection as an efficient and cost-effective approach to estimate breeding values continues to increase, due in part to the significant saving in phenotyping. Ridge regression is one of the most popular methods used for genomic prediction; however, its efficiency (in terms of prediction performance) depends on the appropriate tunning of the penalization parameter. In this paper we propose a novel, more efficient method to select the optimal penalization parameter for Ridge regression. We compared the proposed method with the conventional method to select the penalization parameter in 14 real data sets and we found that in 13 of these, the proposed method outperformed the conventional method and across data sets the gains in prediction accuracy in terms of Pearson's correlation was of 56.15%, with not-gains observed in terms of normalized mean square error. Finally, our results show evidence of the potential of the proposed method, and we encourage its adoption to improve the selection of candidate lines in the context of plant breeding.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-11-12T16:12:35Z
dc.date.available.none.fl_str_mv 2024-11-12T16:12:35Z
dc.date.issued.none.fl_str_mv 2024
dc.type.es.fl_str_mv Artículo
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.citation.es.fl_str_mv Montesinos-López, A., Montesinos-López, O., Lecumberry, F. y otros. "Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction". G3 : Genes, Genomes, Genetics. [en línea]. 2024, pp. 1-15. DOI: 10.1093/g3journal/jkae246.
dc.identifier.uri.none.fl_str_mv https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkae246/7888815
https://hdl.handle.net/20.500.12008/46936
dc.identifier.doi.none.fl_str_mv 10.1093/g3journal/jkae246
dc.identifier.eissn.none.fl_str_mv 2160-1836
identifier_str_mv Montesinos-López, A., Montesinos-López, O., Lecumberry, F. y otros. "Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction". G3 : Genes, Genomes, Genetics. [en línea]. 2024, pp. 1-15. DOI: 10.1093/g3journal/jkae246.
10.1093/g3journal/jkae246
2160-1836
url https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkae246/7888815
https://hdl.handle.net/20.500.12008/46936
dc.language.iso.none.fl_str_mv en
eng
language_invalid_str_mv en
language eng
dc.relation.none.fl_str_mv G3 : Genes, Genomes, Genetics, nov. 2024, pp. 1-15.
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución (CC - By 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv Licencia Creative Commons Atribución (CC - By 4.0)
dc.format.extent.es.fl_str_mv 15 p.
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dc.publisher.es.fl_str_mv Genetics Society of America, Oxford University Press.
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
instname_str Universidad de la República
instacron_str Universidad de la República
institution Universidad de la República
reponame_str COLIBRI
collection COLIBRI
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spelling Montesinos-López Abelardo, Universidad de Guadalajara, Jalisco, MéxicoMontesinos-López Osval A., Universidad de Colima, Colima, MéxicoLecumberry Federico, Universidad de la República (Uruguay). Facultad de Ingeniería.Fariello Maria Ines, Universidad de la República (Uruguay). Facultad de Ingeniería.Montesinos-López José C., University of California Davis, CA, USACrossa José, Louisiana State University, LA, USA2024-11-12T16:12:35Z2024-11-12T16:12:35Z2024Montesinos-López, A., Montesinos-López, O., Lecumberry, F. y otros. "Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction". G3 : Genes, Genomes, Genetics. [en línea]. 2024, pp. 1-15. DOI: 10.1093/g3journal/jkae246.https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkae246/7888815https://hdl.handle.net/20.500.12008/4693610.1093/g3journal/jkae2462160-1836The popularity of genomic selection as an efficient and cost-effective approach to estimate breeding values continues to increase, due in part to the significant saving in phenotyping. Ridge regression is one of the most popular methods used for genomic prediction; however, its efficiency (in terms of prediction performance) depends on the appropriate tunning of the penalization parameter. In this paper we propose a novel, more efficient method to select the optimal penalization parameter for Ridge regression. We compared the proposed method with the conventional method to select the penalization parameter in 14 real data sets and we found that in 13 of these, the proposed method outperformed the conventional method and across data sets the gains in prediction accuracy in terms of Pearson's correlation was of 56.15%, with not-gains observed in terms of normalized mean square error. Finally, our results show evidence of the potential of the proposed method, and we encourage its adoption to improve the selection of candidate lines in the context of plant breeding.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2024-11-11T18:12:46Z No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) MMLFMC24.pdf: 1749663 bytes, checksum: 26925fd1fd41c1942262ed1f98512dd7 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2024-11-12T15:31:08Z (GMT) No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) MMLFMC24.pdf: 1749663 bytes, checksum: 26925fd1fd41c1942262ed1f98512dd7 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2024-11-12T16:12:35Z (GMT). No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) MMLFMC24.pdf: 1749663 bytes, checksum: 26925fd1fd41c1942262ed1f98512dd7 (MD5) Previous issue date: 2024Bill & Melinda Gates Foundation15 p.application/pdfenengGenetics Society of America, Oxford University Press.G3 : Genes, Genomes, Genetics, nov. 2024, pp. 1-15.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)Ridge regressionGenomic predictionGenPredShared Data ResourcePlant breedingBreeding valuesPenalized regressionRefining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaMontesinos-López, AbelardoMontesinos-López, Osval A.Lecumberry, FedericoFariello, Maria InesMontesinos-López, José C.Crossa, JoséProcesamiento de SeñalesTratamiento de ImagenesLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/46936/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; 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públicahttps://udelar.edu.uy/https://www.colibri.udelar.edu.uy/oai/requestkarina.camps@seciu.edu.uyUruguayopendoar:47712024-11-12T16:12:35COLIBRI - Universidad de la Repúblicafalse
score 14,712934

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