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Response to Early Generation Genomic Selection for Yield in Wheat
We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787636/ https://www.ncbi.nlm.nih.gov/pubmed/35087542 http://dx.doi.org/10.3389/fpls.2021.718611 |
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author | Bonnett, David Li, Yongle Crossa, Jose Dreisigacker, Susanne Basnet, Bhoja Pérez-Rodríguez, Paulino Alvarado, G. Jannink, J. L. Poland, Jesse Sorrells, Mark |
author_facet | Bonnett, David Li, Yongle Crossa, Jose Dreisigacker, Susanne Basnet, Bhoja Pérez-Rodríguez, Paulino Alvarado, G. Jannink, J. L. Poland, Jesse Sorrells, Mark |
author_sort | Bonnett, David |
collection | PubMed |
description | We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds. |
format | Online Article Text |
id | pubmed-8787636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87876362022-01-26 Response to Early Generation Genomic Selection for Yield in Wheat Bonnett, David Li, Yongle Crossa, Jose Dreisigacker, Susanne Basnet, Bhoja Pérez-Rodríguez, Paulino Alvarado, G. Jannink, J. L. Poland, Jesse Sorrells, Mark Front Plant Sci Plant Science We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds. Frontiers Media S.A. 2022-01-11 /pmc/articles/PMC8787636/ /pubmed/35087542 http://dx.doi.org/10.3389/fpls.2021.718611 Text en Copyright © 2022 Bonnett, Li, Crossa, Dreisigacker, Basnet, Pérez-Rodríguez, Alvarado, Jannink, Poland and Sorrells. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Bonnett, David Li, Yongle Crossa, Jose Dreisigacker, Susanne Basnet, Bhoja Pérez-Rodríguez, Paulino Alvarado, G. Jannink, J. L. Poland, Jesse Sorrells, Mark Response to Early Generation Genomic Selection for Yield in Wheat |
title | Response to Early Generation Genomic Selection for Yield in Wheat |
title_full | Response to Early Generation Genomic Selection for Yield in Wheat |
title_fullStr | Response to Early Generation Genomic Selection for Yield in Wheat |
title_full_unstemmed | Response to Early Generation Genomic Selection for Yield in Wheat |
title_short | Response to Early Generation Genomic Selection for Yield in Wheat |
title_sort | response to early generation genomic selection for yield in wheat |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787636/ https://www.ncbi.nlm.nih.gov/pubmed/35087542 http://dx.doi.org/10.3389/fpls.2021.718611 |
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