Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits
Genomic 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.
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MDPI
2023
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Genomic Prediction, Parental Information, Prediction Accuracy, Correlated Traits, BREEDING, FORECASTING, WHEAT, GENOMICS, Genetic Resources, |
Online Access: | https://hdl.handle.net/10883/22537 |
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dig-cimmyt-10883-225372023-12-01T16:20:58Z Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits Montesinos-Lopez, O.A. Bentley, A.R. Saint Pierre, C. Crespo Herrera, L.A. Salinas-Ruiz, J. Valladares-Celis, P.C. Montesinos-Lopez, A. Crossa, J. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Prediction Parental Information Prediction Accuracy Correlated Traits BREEDING FORECASTING WHEAT GENOMICS Genetic Resources Genomic 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. 2023-03-10T20:10:13Z 2023-03-10T20:10:13Z 2023 Article Published Version https://hdl.handle.net/10883/22537 10.3390/genes14020395 English http://hdl.handle.net/11529/10548129 CIMMYT 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 Open Access Basel (Switzerland) MDPI 2 14 2073-4425 Genes 395 |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Prediction Parental Information Prediction Accuracy Correlated Traits BREEDING FORECASTING WHEAT GENOMICS Genetic Resources AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Prediction Parental Information Prediction Accuracy Correlated Traits BREEDING FORECASTING WHEAT GENOMICS Genetic Resources |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Prediction Parental Information Prediction Accuracy Correlated Traits BREEDING FORECASTING WHEAT GENOMICS Genetic Resources AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Prediction Parental Information Prediction Accuracy Correlated Traits BREEDING FORECASTING WHEAT GENOMICS Genetic Resources Montesinos-Lopez, O.A. Bentley, A.R. Saint Pierre, C. Crespo Herrera, L.A. Salinas-Ruiz, J. Valladares-Celis, P.C. Montesinos-Lopez, A. Crossa, J. Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits |
description |
Genomic 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. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Prediction Parental Information Prediction Accuracy Correlated Traits BREEDING FORECASTING WHEAT GENOMICS Genetic Resources |
author |
Montesinos-Lopez, O.A. Bentley, A.R. Saint Pierre, C. Crespo Herrera, L.A. Salinas-Ruiz, J. Valladares-Celis, P.C. Montesinos-Lopez, A. Crossa, J. |
author_facet |
Montesinos-Lopez, O.A. Bentley, A.R. Saint Pierre, C. Crespo Herrera, L.A. Salinas-Ruiz, J. Valladares-Celis, P.C. Montesinos-Lopez, A. Crossa, J. |
author_sort |
Montesinos-Lopez, O.A. |
title |
Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits |
title_short |
Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits |
title_full |
Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits |
title_fullStr |
Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits |
title_full_unstemmed |
Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits |
title_sort |
integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits |
publisher |
MDPI |
publishDate |
2023 |
url |
https://hdl.handle.net/10883/22537 |
work_keys_str_mv |
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_version_ |
1787233019094892544 |