Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations

Abstract The efficient acquisition and transport of nutrients by plants largely depend on the root architecture. Due to the absence of complex microbial network interactions and soil heterogeneity in a restricted soilless medium, the architecture of roots is a function of genetics defined by the soi...

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Autores principales:	Henry O. Awika, Amit K. Mishra, Haramrit Gill, James DiPiazza, Carlos A. Avila, Vijay Joshi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/57a559d59c7b45349d58690544daf324
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spelling	oai:doaj.org-article:57a559d59c7b45349d58690544daf3242021-12-02T14:49:26ZSelection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations10.1038/s41598-021-87870-z2045-2322https://doaj.org/article/57a559d59c7b45349d58690544daf3242021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87870-zhttps://doaj.org/toc/2045-2322Abstract The efficient acquisition and transport of nutrients by plants largely depend on the root architecture. Due to the absence of complex microbial network interactions and soil heterogeneity in a restricted soilless medium, the architecture of roots is a function of genetics defined by the soilless matrix and exogenously supplied nutrients such as nitrogen (N). The knowledge of root trait combinations that offer the optimal nitrogen use efficiency (NUE) is far from being conclusive. The objective of this study was to define the root trait(s) that best predicts and correlates with vegetative biomass under differed N treatments. We used eight image-derived root architectural traits of 202 diverse spinach lines grown in two N concentrations (high N, HN, and low N, LN) in randomized complete blocks design. Supervised random forest (RF) machine learning augmented by ranger hyperparameter grid search was used to predict the variable importance of the root traits. We also determined the broad-sense heritability (H) and genetic (r g ) and phenotypic (r p ) correlations between root traits and the vegetative biomass (shoot weight, SWt). Each root trait was assigned a predicted importance rank based on the trait’s contribution to the cumulative reduction in the mean square error (MSE) in the RF tree regression models for SWt. The root traits were further prioritized for potential selection based on the r g and SWt correlated response (CR). The predicted importance of the eight root traits showed that the number of root tips (Tips) and root length (RLength) under HN and crossings (Xsings) and root average diameter (RAvdiam) under LN were the most relevant. SWt had a highly antagonistic r g (− 0.83) to RAvdiam, but a high predicted indirect selection efficiency (− 112.8%) with RAvdiam under LN; RAvdiam showed no significant rg or rp to SWt under HN. In limited N availability, we suggest that selecting against larger RAvdiam as a secondary trait might improve biomass and, hence, NUE with no apparent yield penalty under HN.Henry O. AwikaAmit K. MishraHaramrit GillJames DiPiazzaCarlos A. AvilaVijay JoshiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Henry O. Awika Amit K. Mishra Haramrit Gill James DiPiazza Carlos A. Avila Vijay Joshi Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations
description	Abstract The efficient acquisition and transport of nutrients by plants largely depend on the root architecture. Due to the absence of complex microbial network interactions and soil heterogeneity in a restricted soilless medium, the architecture of roots is a function of genetics defined by the soilless matrix and exogenously supplied nutrients such as nitrogen (N). The knowledge of root trait combinations that offer the optimal nitrogen use efficiency (NUE) is far from being conclusive. The objective of this study was to define the root trait(s) that best predicts and correlates with vegetative biomass under differed N treatments. We used eight image-derived root architectural traits of 202 diverse spinach lines grown in two N concentrations (high N, HN, and low N, LN) in randomized complete blocks design. Supervised random forest (RF) machine learning augmented by ranger hyperparameter grid search was used to predict the variable importance of the root traits. We also determined the broad-sense heritability (H) and genetic (r g ) and phenotypic (r p ) correlations between root traits and the vegetative biomass (shoot weight, SWt). Each root trait was assigned a predicted importance rank based on the trait’s contribution to the cumulative reduction in the mean square error (MSE) in the RF tree regression models for SWt. The root traits were further prioritized for potential selection based on the r g and SWt correlated response (CR). The predicted importance of the eight root traits showed that the number of root tips (Tips) and root length (RLength) under HN and crossings (Xsings) and root average diameter (RAvdiam) under LN were the most relevant. SWt had a highly antagonistic r g (− 0.83) to RAvdiam, but a high predicted indirect selection efficiency (− 112.8%) with RAvdiam under LN; RAvdiam showed no significant rg or rp to SWt under HN. In limited N availability, we suggest that selecting against larger RAvdiam as a secondary trait might improve biomass and, hence, NUE with no apparent yield penalty under HN.
format	article
author	Henry O. Awika Amit K. Mishra Haramrit Gill James DiPiazza Carlos A. Avila Vijay Joshi
author_facet	Henry O. Awika Amit K. Mishra Haramrit Gill James DiPiazza Carlos A. Avila Vijay Joshi
author_sort	Henry O. Awika
title	Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations
title_short	Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations
title_full	Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations
title_fullStr	Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations
title_full_unstemmed	Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations
title_sort	selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/57a559d59c7b45349d58690544daf324
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Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations

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