Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials

dc.creatorMontesinos-Lopez, A.
dc.creatorRuncie, D.E.
dc.creatorIbba, M.I.
dc.creatorPerez-Rodriguez, P.
dc.creatorMontesinos-Lopez, O.A.
dc.creatorCrespo Herrera, L.A.
dc.creatorBentley, A.R.
dc.creatorCrossa, J.
dc.date2021-10-16T00:20:25Z
dc.date2021-10-16T00:20:25Z
dc.date2021
dc.date.accessioned2023-07-17T20:08:10Z
dc.date.available2023-07-17T20:08:10Z
dc.identifierhttps://hdl.handle.net/10883/21696
dc.identifier10.1093/g3journal/jkab270
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7513469
dc.descriptionImplementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson's correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson's correlation calculated by fitting a bivariate model was higher than the division of the Pearson's correlation by the squared root of the heritability across traits, by 2.53-11.46%. Across the datasets, the corrected Pearson's correlation was higher than the uncorrected by 5.80-14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.
dc.languageEnglish
dc.publisherGenetics Society of America
dc.relationhttps://hdl.handle.net/11529/10548572
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source10
dc.source11
dc.source2160-1836
dc.sourceG3: Genes, Genomes, Genetics
dc.sourcejkab270
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectMulti-Trait Analysis
dc.subjectMulti-Environment Analysis
dc.subjectGenomic Prediction
dc.subjectShared Data Resources
dc.subjectWHEAT
dc.subjectQUALITY
dc.subjectGENOMICS
dc.subjectDATA
dc.titleMulti-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials
dc.typeArticle
dc.typePublished Version
dc.coverageBethesda, MD (USA)


Este ítem pertenece a la siguiente institución