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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| Format: | article |
| Language: | English |
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2024
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| Online Access: | https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkae246/7888815 https://hdl.handle.net/20.500.12008/46936 |
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| _version_ | 1868889940411023360 |
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| author | Montesinos-López, Abelardo |
| author2 | Montesinos-López, Osval A. Lecumberry, Federico Fariello, Maria Ines Montesinos-López, José C. Crossa, José |
| author2_role | author author author author author |
| author_browse | Crossa, José Fariello, Maria Ines Lecumberry, Federico Montesinos-López, Abelardo Montesinos-López, José C. Montesinos-López, Osval A. |
| author_facet | Montesinos-López, Abelardo Montesinos-López, Osval A. Lecumberry, Federico Fariello, Maria Ines Montesinos-López, José C. Crossa, José |
| author_role | author |
| collection | COLIBRI |
| dc.contributor.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.creator.none.fl_str_mv | Montesinos-López, Abelardo Montesinos-López, Osval A. Lecumberry, Federico Fariello, Maria Ines Montesinos-López, José C. Crossa, José |
| dc.date.none.fl_str_mv | 2024-11-12T16:12:35Z 2024-11-12T16:12:35Z 2024 |
| dc.format.none.fl_str_mv | 15 p. application/pdf |
| dc.identifier.none.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. https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkae246/7888815 https://hdl.handle.net/20.500.12008/46936 10.1093/g3journal/jkae246 2160-1836 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Genetics Society of America, Oxford University Press. |
| 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 Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Ridge regression Genomic prediction GenPred Shared Data Resource Plant breeding Breeding values Penalized regression |
| dc.title.none.fl_str_mv | Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction. |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_07feddf09c3ddcfb2a6c0c8dee3d27c8 |
| 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 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/46936 |
| publishDate | 2024 |
| publishDateSort | 2024 |
| publisher.none.fl_str_mv | Genetics Society of America, Oxford University Press. |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Refining penalized ridge regression : A novel method for optimizing the regularization parameter in genomic prediction.Montesinos-López, AbelardoMontesinos-López, Osval A.Lecumberry, FedericoFariello, Maria InesMontesinos-López, José C.Crossa, JoséRidge regressionGenomic predictionGenPredShared Data ResourcePlant breedingBreeding valuesPenalized regressionThe 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.Bill & Melinda Gates FoundationGenetics Society of America, Oxford University Press.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, USA2024-11-12T16:12:35Z2024-11-12T16:12:35Z2024Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion15 p.application/pdfMontesinos-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/jkae2462160-1836reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengG3 : 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)oai:colibri.udelar.edu.uy:20.500.12008/469362026-04-14T10:16:00Z |
| 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 |
| status_str | publishedVersion |
| title | 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_short | 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. |
| topic | Ridge regression Genomic prediction GenPred Shared Data Resource Plant breeding Breeding values Penalized regression |
| url | https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkae246/7888815 https://hdl.handle.net/20.500.12008/46936 |