Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.)
Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.
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2021
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Single-Environment, Multi-Environment, Genotyping by Sequencing, Genomic Selection, Genomic Prediction, Best Linear Unbiased Prediction, MARKER-ASSISTED SELECTION, GENOMICS, WHEAT, BEST LINEAR UNBIASED PREDICTOR, |
Online Access: | https://hdl.handle.net/10883/21722 |
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dig-cimmyt-10883-217222024-03-19T15:23:12Z Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.) Tomar, V. Singh, D. Dhillon, G.S. Yong Suk Chung Poland, J.A. Singh, R.P. Joshi, A.K. Gautam, Y. Tiwari, B.S. Kumar, U. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Single-Environment Multi-Environment Genotyping by Sequencing Genomic Selection Genomic Prediction Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION GENOMICS WHEAT BEST LINEAR UNBIASED PREDICTOR Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials. 2021-11-09T01:20:18Z 2021-11-09T01:20:18Z 2021 Article Published Version https://hdl.handle.net/10883/21722 10.3389/fpls.2021.720123 English https://figshare.com/collections/Increased_Predictive_Accuracy_of_Multi-Environment_Genomic_Prediction_Model_for_Yield_and_Related_Traits_in_Spring_Wheat_Triticum_aestivum_L_/5653156 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 Switzerland Frontiers 12 1664-462X Frontiers in Plant Science 720123 |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Single-Environment Multi-Environment Genotyping by Sequencing Genomic Selection Genomic Prediction Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION GENOMICS WHEAT BEST LINEAR UNBIASED PREDICTOR AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Single-Environment Multi-Environment Genotyping by Sequencing Genomic Selection Genomic Prediction Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION GENOMICS WHEAT BEST LINEAR UNBIASED PREDICTOR |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Single-Environment Multi-Environment Genotyping by Sequencing Genomic Selection Genomic Prediction Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION GENOMICS WHEAT BEST LINEAR UNBIASED PREDICTOR AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Single-Environment Multi-Environment Genotyping by Sequencing Genomic Selection Genomic Prediction Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION GENOMICS WHEAT BEST LINEAR UNBIASED PREDICTOR Tomar, V. Singh, D. Dhillon, G.S. Yong Suk Chung Poland, J.A. Singh, R.P. Joshi, A.K. Gautam, Y. Tiwari, B.S. Kumar, U. Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.) |
description |
Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Single-Environment Multi-Environment Genotyping by Sequencing Genomic Selection Genomic Prediction Best Linear Unbiased Prediction MARKER-ASSISTED SELECTION GENOMICS WHEAT BEST LINEAR UNBIASED PREDICTOR |
author |
Tomar, V. Singh, D. Dhillon, G.S. Yong Suk Chung Poland, J.A. Singh, R.P. Joshi, A.K. Gautam, Y. Tiwari, B.S. Kumar, U. |
author_facet |
Tomar, V. Singh, D. Dhillon, G.S. Yong Suk Chung Poland, J.A. Singh, R.P. Joshi, A.K. Gautam, Y. Tiwari, B.S. Kumar, U. |
author_sort |
Tomar, V. |
title |
Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.) |
title_short |
Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.) |
title_full |
Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.) |
title_fullStr |
Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.) |
title_full_unstemmed |
Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.) |
title_sort |
increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (triticum aestivum l.) |
publisher |
Frontiers |
publishDate |
2021 |
url |
https://hdl.handle.net/10883/21722 |
work_keys_str_mv |
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