Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article].
KEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters. ABSTRACT: Multi-trait genomic prediction models can be used to predict lab...
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| Format: | article |
| Language: | English |
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2018
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| Online Access: | https://ainfo.inia.uy/consulta/busca?b=pc&id=59141&biblioteca=vazio&busca=59141&qFacets=59141 |
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| _version_ | 1868890047127748608 |
|---|---|
| author | LADO, B. |
| author2 | VÁZQUEZ, D. QUINCKE, M. SILVA, P. AGUILAR, I. GUTIÉRREZ, L. |
| author2_role | author author author author author |
| author_browse | AGUILAR, I. GUTIÉRREZ, L. LADO, B. QUINCKE, M. SILVA, P. VÁZQUEZ, D. |
| author_facet | LADO, B. VÁZQUEZ, D. QUINCKE, M. SILVA, P. AGUILAR, I. GUTIÉRREZ, L. |
| author_role | author |
| collection | AINFO |
| dc.creator.none.fl_str_mv | LADO, B. VÁZQUEZ, D. QUINCKE, M. SILVA, P. AGUILAR, I. GUTIÉRREZ, L. |
| dc.date.none.fl_str_mv | 2018 2022-12-16T21:05:33Z 2022-12-16T21:05:33Z 2022-12-16T21:05:33Z |
| dc.identifier.none.fl_str_mv | https://ainfo.inia.uy/consulta/busca?b=pc&id=59141&biblioteca=vazio&busca=59141&qFacets=59141 |
| dc.language.none.fl_str_mv | en eng |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Acceso abierto |
| dc.source.none.fl_str_mv | reponame:AINFO instname:Instituto Nacional de Investigación Agropecuaria instacron:Instituto Nacional de Investigación Agropecuaria |
| dc.subject.none.fl_str_mv | ABILITY TESTING FORECASTING PLANTS (BOTANY) SOFTWARE TESTING QUALITY CONTROL GENOMIC PREDICTIONS PLANT BREEDING PROGRAMS PLATAFORMA AGROALIMENTOS GENES |
| dc.title.none.fl_str_mv | Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
| dc.type.none.fl_str_mv | Article PublishedVersion info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | KEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters. ABSTRACT: Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program. © 2018, The Author(s). |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_4f7bef6217d0e9b882019156be4e231e |
| instacron_str | Instituto Nacional de Investigación Agropecuaria |
| institution | Instituto Nacional de Investigación Agropecuaria |
| instname_str | Instituto Nacional de Investigación Agropecuaria |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:redi.anii.org.uy:20.500.12381/3079 |
| publishDate | 2018 |
| publishDateSort | 2018 |
| reponame_str | AINFO |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Acceso abierto |
| spelling | Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article].LADO, B.VÁZQUEZ, D.QUINCKE, M.SILVA, P.AGUILAR, I.GUTIÉRREZ, L.ABILITY TESTINGFORECASTINGPLANTS (BOTANY)SOFTWARE TESTINGQUALITY CONTROLGENOMIC PREDICTIONSPLANT BREEDING PROGRAMSPLATAFORMA AGROALIMENTOSGENESKEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters. ABSTRACT: Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program. © 2018, The Author(s).2022-12-16T21:05:33Z2022-12-16T21:05:33Z20182022-12-16T21:05:33ZArticlePublishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://ainfo.inia.uy/consulta/busca?b=pc&id=59141&biblioteca=vazio&busca=59141&qFacets=59141reponame:AINFOinstname:Instituto Nacional de Investigación Agropecuariainstacron:Instituto Nacional de Investigación Agropecuariaenenginfo:eu-repo/semantics/openAccessAcceso abiertooai:redi.anii.org.uy:20.500.12381/30792026-02-10T17:37:22Z |
| spellingShingle | Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. LADO, B. ABILITY TESTING FORECASTING PLANTS (BOTANY) SOFTWARE TESTING QUALITY CONTROL GENOMIC PREDICTIONS PLANT BREEDING PROGRAMS PLATAFORMA AGROALIMENTOS GENES |
| status_str | publishedVersion |
| title | Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
| title_full | Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
| title_fullStr | Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
| title_full_unstemmed | Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
| title_short | Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
| title_sort | Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
| topic | ABILITY TESTING FORECASTING PLANTS (BOTANY) SOFTWARE TESTING QUALITY CONTROL GENOMIC PREDICTIONS PLANT BREEDING PROGRAMS PLATAFORMA AGROALIMENTOS GENES |
| url | https://ainfo.inia.uy/consulta/busca?b=pc&id=59141&biblioteca=vazio&busca=59141&qFacets=59141 |