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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531512/ https://www.ncbi.nlm.nih.gov/pubmed/34691100 http://dx.doi.org/10.3389/fpls.2021.720123 |
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author | Tomar, Vipin Singh, Daljit Dhillon, Guriqbal Singh Chung, Yong Suk Poland, Jesse Singh, Ravi Prakash Joshi, Arun Kumar Gautam, Yogesh Tiwari, Budhi Sagar Kumar, Uttam |
author_facet | Tomar, Vipin Singh, Daljit Dhillon, Guriqbal Singh Chung, Yong Suk Poland, Jesse Singh, Ravi Prakash Joshi, Arun Kumar Gautam, Yogesh Tiwari, Budhi Sagar Kumar, Uttam |
author_sort | Tomar, Vipin |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8531512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85315122021-10-23 Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.) Tomar, Vipin Singh, Daljit Dhillon, Guriqbal Singh Chung, Yong Suk Poland, Jesse Singh, Ravi Prakash Joshi, Arun Kumar Gautam, Yogesh Tiwari, Budhi Sagar Kumar, Uttam Front Plant Sci Plant Science 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. Frontiers Media S.A. 2021-10-08 /pmc/articles/PMC8531512/ /pubmed/34691100 http://dx.doi.org/10.3389/fpls.2021.720123 Text en Copyright © 2021 Tomar, Singh, Dhillon, Chung, Poland, Singh, Joshi, Gautam, Tiwari and Kumar. 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 Tomar, Vipin Singh, Daljit Dhillon, Guriqbal Singh Chung, Yong Suk Poland, Jesse Singh, Ravi Prakash Joshi, Arun Kumar Gautam, Yogesh Tiwari, Budhi Sagar Kumar, Uttam Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.) |
title | 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_short | 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.) |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531512/ https://www.ncbi.nlm.nih.gov/pubmed/34691100 http://dx.doi.org/10.3389/fpls.2021.720123 |
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