Genomics combined with UAS data enhances prediction of grain yield in winter wheat

dc.creatorMontesinos-Lopez, O.A.
dc.creatorHerr, A.W.
dc.creatorCrossa, J.
dc.creatorCarter, A.
dc.date2023-04-28T00:10:17Z
dc.date2023-04-28T00:10:17Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:33Z
dc.date.available2023-07-17T20:10:33Z
dc.identifierhttps://hdl.handle.net/10883/22582
dc.identifier10.3389/fgene.2023.1124218
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514325
dc.descriptionWith the human population continuing to increase worldwide, there is pressure to employ novel technologies to increase genetic gain in plant breeding programs that contribute to nutrition and food security. Genomic selection (GS) has the potential to increase genetic gain because it can accelerate the breeding cycle, increase the accuracy of estimated breeding values, and improve selection accuracy. However, with recent advances in high throughput phenotyping in plant breeding programs, the opportunity to integrate genomic and phenotypic data to increase prediction accuracy is present. In this paper, we applied GS to winter wheat data integrating two types of inputs: genomic and phenotypic. We observed the best accuracy of grain yield when combining both genomic and phenotypic inputs, while only using genomic information fared poorly. In general, the predictions with only phenotypic information were very competitive to using both sources of information, and in many cases using only phenotypic information provided the best accuracy. Our results are encouraging because it is clear we can enhance the prediction accuracy of GS by integrating high quality phenotypic inputs in the models.
dc.languageEnglish
dc.publisherFrontiers Media
dc.relationhttps://doi.org/10.7273/000004567
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dc.rightsOpen Access
dc.source14
dc.source1664-8021
dc.sourceFrontiers in Genetics
dc.source1124218
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectHigh-Throughput Phenotyping
dc.subjectGenomic Prediction
dc.subjectSelection Accuracy
dc.subjectGenomic Selection
dc.subjectGENOMICS
dc.subjectGRAIN
dc.subjectYIELDS
dc.subjectPHENOTYPES
dc.subjectWINTER WHEAT
dc.subjectMARKER-ASSISTED SELECTION
dc.subjectGenetic Resources
dc.titleGenomics combined with UAS data enhances prediction of grain yield in winter wheat
dc.typeArticle
dc.typePublished Version
dc.coverageSwitzerland


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