Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits

dc.creatorMontesinos-Lopez, O.A.
dc.creatorBentley, A.R.
dc.creatorSaint Pierre, C.
dc.creatorCrespo-Herrera, L.A.
dc.creatorSalinas Ruiz, J.
dc.creatorValladares-Celis, P.C.
dc.creatorMontesinos-López, A.
dc.creatorCrossa, J.
dc.date2023-03-10T20:10:13Z
dc.date2023-03-10T20:10:13Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:28Z
dc.date.available2023-07-17T20:10:28Z
dc.identifierhttps://hdl.handle.net/10883/22537
dc.identifier10.3390/genes14020395
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514280
dc.descriptionGenomic selection (GS) is a methodology that is revolutionizing plant breeding because it can select candidate genotypes without phenotypic evaluation in the field. However, its practical implementation in hybrid prediction remains challenging since many factors affect its accuracy. The main objective of this study was to research the genomic prediction accuracy of wheat hybrids by adding covariates with the hybrid parental phenotypic information to the model. Four types of different models (MA, MB, MC, and MD) with one covariate (same trait to be predicted) (MA_C, MB_C, MC_C, and MD_C) or several covariates (of the same trait and other correlated traits) (MA_AC, MB_AC, MC_AC, and MD_AC) were studied. We found that the four models with parental information outperformed models without parental information in terms of mean square error by at least 14.1% (MA vs. MA_C), 5.5% (MB vs. MB_C), 51.4% (MC vs. MC_C), and 6.4% (MD vs. MD_C) when parental information of the same trait was used and by at least 13.7% (MA vs. MA_AC), 5.3% (MB vs. MB_AC), 55.1% (MC vs. MC_AC), and 6.0% (MD vs. MD_AC) when parental information of the same trait and other correlated traits were used. Our results also show a large gain in prediction accuracy when covariates were considered using the parental phenotypic information, as opposed to marker information. Finally, our results empirically demonstrate that a significant improvement in prediction accuracy was gained by adding parental phenotypic information as covariates; however, this is expensive since, in many breeding programs, the parental phenotypic information is unavailable.
dc.languageEnglish
dc.publisherMDPI
dc.relationhttp://hdl.handle.net/11529/10548129
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.source2
dc.source14
dc.source2073-4425
dc.sourceGenes
dc.source395
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectGenomic Prediction
dc.subjectParental Information
dc.subjectPrediction Accuracy
dc.subjectCorrelated Traits
dc.subjectBREEDING
dc.subjectFORECASTING
dc.subjectWHEAT
dc.subjectGENOMICS
dc.subjectGenetic Resources
dc.titleIntegrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits
dc.typeArticle
dc.typePublished Version
dc.coverageBasel (Switzerland)


Este ítem pertenece a la siguiente institución