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Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information

Prediction models based on pedigree and/or molecular marker information are now an inextricable part of the crop breeding programs and have led to increased genetic gains in many crops. Optimization of IRRI’s rice drought breeding program is crucial for better implementation of selections based on p...

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Autores principales: Khanna, Apurva, Anumalla, Mahender, Catolos, Margaret, Bhosale, Sankalp, Jarquin, Diego, Hussain, Waseem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530897/
https://www.ncbi.nlm.nih.gov/pubmed/36204059
http://dx.doi.org/10.3389/fpls.2022.983818
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author Khanna, Apurva
Anumalla, Mahender
Catolos, Margaret
Bhosale, Sankalp
Jarquin, Diego
Hussain, Waseem
author_facet Khanna, Apurva
Anumalla, Mahender
Catolos, Margaret
Bhosale, Sankalp
Jarquin, Diego
Hussain, Waseem
author_sort Khanna, Apurva
collection PubMed
description Prediction models based on pedigree and/or molecular marker information are now an inextricable part of the crop breeding programs and have led to increased genetic gains in many crops. Optimization of IRRI’s rice drought breeding program is crucial for better implementation of selections based on predictions. Historical datasets with precise and robust pedigree information have been a great resource to help optimize the prediction models in the breeding programs. Here, we leveraged 17 years of historical drought data along with the pedigree information to predict the new lines or environments and dissect the G × E interactions. Seven models ranging from basic to proposed higher advanced models incorporating interactions, and genotypic specific effects were used. These models were tested with three cross-validation schemes (CV1, CV2, and CV0) to assess the predictive ability of tested and untested lines in already observed environments and tested lines in novel or new environments. In general, the highest prediction abilities were obtained when the model accounting interactions between pedigrees (additive) and environment were included. The CV0 scheme (predicting unobserved or novel environments) reveals very low predictive abilities among the three schemes. CV1 and CV2 schemes that borrow information from the target and correlated environments have much higher predictive abilities. Further, predictive ability was lower when predicting lines in non-stress conditions using drought data as training set and/or vice-versa. When predicting the lines using the data sets under the same conditions (stress or non-stress data sets), much better prediction accuracy was obtained. These results provide conclusive evidence that modeling G × E interactions are important in predictions. Thus, considering G × E interactions would help to build enhanced genomic or pedigree-based prediction models in the rice breeding program. Further, it is crucial to borrow the correlated information from other environments to improve prediction accuracy.
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spelling pubmed-95308972022-10-05 Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information Khanna, Apurva Anumalla, Mahender Catolos, Margaret Bhosale, Sankalp Jarquin, Diego Hussain, Waseem Front Plant Sci Plant Science Prediction models based on pedigree and/or molecular marker information are now an inextricable part of the crop breeding programs and have led to increased genetic gains in many crops. Optimization of IRRI’s rice drought breeding program is crucial for better implementation of selections based on predictions. Historical datasets with precise and robust pedigree information have been a great resource to help optimize the prediction models in the breeding programs. Here, we leveraged 17 years of historical drought data along with the pedigree information to predict the new lines or environments and dissect the G × E interactions. Seven models ranging from basic to proposed higher advanced models incorporating interactions, and genotypic specific effects were used. These models were tested with three cross-validation schemes (CV1, CV2, and CV0) to assess the predictive ability of tested and untested lines in already observed environments and tested lines in novel or new environments. In general, the highest prediction abilities were obtained when the model accounting interactions between pedigrees (additive) and environment were included. The CV0 scheme (predicting unobserved or novel environments) reveals very low predictive abilities among the three schemes. CV1 and CV2 schemes that borrow information from the target and correlated environments have much higher predictive abilities. Further, predictive ability was lower when predicting lines in non-stress conditions using drought data as training set and/or vice-versa. When predicting the lines using the data sets under the same conditions (stress or non-stress data sets), much better prediction accuracy was obtained. These results provide conclusive evidence that modeling G × E interactions are important in predictions. Thus, considering G × E interactions would help to build enhanced genomic or pedigree-based prediction models in the rice breeding program. Further, it is crucial to borrow the correlated information from other environments to improve prediction accuracy. Frontiers Media S.A. 2022-09-20 /pmc/articles/PMC9530897/ /pubmed/36204059 http://dx.doi.org/10.3389/fpls.2022.983818 Text en Copyright © 2022 Khanna, Anumalla, Catolos, Bhosale, Jarquin and Hussain. 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
Khanna, Apurva
Anumalla, Mahender
Catolos, Margaret
Bhosale, Sankalp
Jarquin, Diego
Hussain, Waseem
Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information
title Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information
title_full Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information
title_fullStr Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information
title_full_unstemmed Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information
title_short Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information
title_sort optimizing predictions in irri’s rice drought breeding program by leveraging 17 years of historical data and pedigree information
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530897/
https://www.ncbi.nlm.nih.gov/pubmed/36204059
http://dx.doi.org/10.3389/fpls.2022.983818
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