Genomics combined with UAS data enhances prediction of grain yield in winter wheat
With 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 t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1124218/full |
| _version_ | 1797857835371986944 |
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| author | Osval A. Montesinos-López Andrew W. Herr José Crossa José Crossa Arron H. Carter |
| author_facet | Osval A. Montesinos-López Andrew W. Herr José Crossa José Crossa Arron H. Carter |
| author_sort | Osval A. Montesinos-López |
| collection | DOAJ |
| description | With 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. |
| first_indexed | 2024-04-09T21:04:16Z |
| format | Article |
| id | doaj.art-93c07c5aede94f418edfced33bb82546 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-09T21:04:16Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-93c07c5aede94f418edfced33bb825462023-03-29T05:12:41ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-03-011410.3389/fgene.2023.11242181124218Genomics combined with UAS data enhances prediction of grain yield in winter wheatOsval A. Montesinos-López0Andrew W. Herr1José Crossa2José Crossa3Arron H. Carter4Facultad de Telemática, Universidad de Colima, Colima, MéxicoDepartment of Crop and Soil Sciences, Washington State University, Pullman, WA, United StatesInternational Maize and Wheat Improvement Center (CIMMYT), Texcoco, Edo. de México, MéxicoColegio de Postgraduados, Montecillos, Edo. de México, MéxicoDepartment of Crop and Soil Sciences, Washington State University, Pullman, WA, United StatesWith 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.https://www.frontiersin.org/articles/10.3389/fgene.2023.1124218/fullhigh throughput phenotypinggenomic predictionwinter wheatselection accuracygenomic selection |
| spellingShingle | Osval A. Montesinos-López Andrew W. Herr José Crossa José Crossa Arron H. Carter Genomics combined with UAS data enhances prediction of grain yield in winter wheat Frontiers in Genetics high throughput phenotyping genomic prediction winter wheat selection accuracy genomic selection |
| title | Genomics combined with UAS data enhances prediction of grain yield in winter wheat |
| title_full | Genomics combined with UAS data enhances prediction of grain yield in winter wheat |
| title_fullStr | Genomics combined with UAS data enhances prediction of grain yield in winter wheat |
| title_full_unstemmed | Genomics combined with UAS data enhances prediction of grain yield in winter wheat |
| title_short | Genomics combined with UAS data enhances prediction of grain yield in winter wheat |
| title_sort | genomics combined with uas data enhances prediction of grain yield in winter wheat |
| topic | high throughput phenotyping genomic prediction winter wheat selection accuracy genomic selection |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1124218/full |
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