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Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations

With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop bree...

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Autores principales: Toda, Yusuke, Sasaki, Goshi, Ohmori, Yoshihiro, Yamasaki, Yuji, Takahashi, Hirokazu, Takanashi, Hideki, Tsuda, Mai, Kajiya-Kanegae, Hiromi, Lopez-Lozano, Raul, Tsujimoto, Hisashi, Kaga, Akito, Nakazono, Mikio, Fujiwara, Toru, Baret, Frederic, Iwata, Hiroyoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8966771/
https://www.ncbi.nlm.nih.gov/pubmed/35371133
http://dx.doi.org/10.3389/fpls.2022.828864
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author Toda, Yusuke
Sasaki, Goshi
Ohmori, Yoshihiro
Yamasaki, Yuji
Takahashi, Hirokazu
Takanashi, Hideki
Tsuda, Mai
Kajiya-Kanegae, Hiromi
Lopez-Lozano, Raul
Tsujimoto, Hisashi
Kaga, Akito
Nakazono, Mikio
Fujiwara, Toru
Baret, Frederic
Iwata, Hiroyoshi
author_facet Toda, Yusuke
Sasaki, Goshi
Ohmori, Yoshihiro
Yamasaki, Yuji
Takahashi, Hirokazu
Takanashi, Hideki
Tsuda, Mai
Kajiya-Kanegae, Hiromi
Lopez-Lozano, Raul
Tsujimoto, Hisashi
Kaga, Akito
Nakazono, Mikio
Fujiwara, Toru
Baret, Frederic
Iwata, Hiroyoshi
author_sort Toda, Yusuke
collection PubMed
description With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.
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spelling pubmed-89667712022-03-31 Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations Toda, Yusuke Sasaki, Goshi Ohmori, Yoshihiro Yamasaki, Yuji Takahashi, Hirokazu Takanashi, Hideki Tsuda, Mai Kajiya-Kanegae, Hiromi Lopez-Lozano, Raul Tsujimoto, Hisashi Kaga, Akito Nakazono, Mikio Fujiwara, Toru Baret, Frederic Iwata, Hiroyoshi Front Plant Sci Plant Science With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials. Frontiers Media S.A. 2022-03-16 /pmc/articles/PMC8966771/ /pubmed/35371133 http://dx.doi.org/10.3389/fpls.2022.828864 Text en Copyright © 2022 Toda, Sasaki, Ohmori, Yamasaki, Takahashi, Takanashi, Tsuda, Kajiya-Kanegae, Lopez-Lozano, Tsujimoto, Kaga, Nakazono, Fujiwara, Baret and Iwata. 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
Toda, Yusuke
Sasaki, Goshi
Ohmori, Yoshihiro
Yamasaki, Yuji
Takahashi, Hirokazu
Takanashi, Hideki
Tsuda, Mai
Kajiya-Kanegae, Hiromi
Lopez-Lozano, Raul
Tsujimoto, Hisashi
Kaga, Akito
Nakazono, Mikio
Fujiwara, Toru
Baret, Frederic
Iwata, Hiroyoshi
Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations
title Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations
title_full Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations
title_fullStr Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations
title_full_unstemmed Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations
title_short Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations
title_sort genomic prediction of green fraction dynamics in soybean using unmanned aerial vehicles observations
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8966771/
https://www.ncbi.nlm.nih.gov/pubmed/35371133
http://dx.doi.org/10.3389/fpls.2022.828864
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