Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant p...

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Autores principales:	Mohsen Yoosefzadeh-Najafabadi, Sepideh Torabi, Dan Tulpan, Istvan Rajcan, Milad Eskandari
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	proximal sensing support vector machine hierarchical data integration soybean breeding recursive feature elimination (RFE) genome-wide association study (GWAS) Plant culture SB1-1110
Acceso en línea:	https://doaj.org/article/b5f591e763d943e390ba7827f0d5b0e3
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spelling	oai:doaj.org-article:b5f591e763d943e390ba7827f0d5b0e32021-11-30T10:13:23ZGenome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods1664-462X10.3389/fpls.2021.777028https://doaj.org/article/b5f591e763d943e390ba7827f0d5b0e32021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpls.2021.777028/fullhttps://doaj.org/toc/1664-462XIn conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.Mohsen Yoosefzadeh-NajafabadiSepideh TorabiDan TulpanIstvan RajcanMilad EskandariFrontiers Media S.A.articleproximal sensingsupport vector machinehierarchical data integrationsoybean breedingrecursive feature elimination (RFE)genome-wide association study (GWAS)Plant cultureSB1-1110ENFrontiers in Plant Science, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	proximal sensing support vector machine hierarchical data integration soybean breeding recursive feature elimination (RFE) genome-wide association study (GWAS) Plant culture SB1-1110
spellingShingle	proximal sensing support vector machine hierarchical data integration soybean breeding recursive feature elimination (RFE) genome-wide association study (GWAS) Plant culture SB1-1110 Mohsen Yoosefzadeh-Najafabadi Sepideh Torabi Dan Tulpan Istvan Rajcan Milad Eskandari Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods
description	In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.
format	article
author	Mohsen Yoosefzadeh-Najafabadi Sepideh Torabi Dan Tulpan Istvan Rajcan Milad Eskandari
author_facet	Mohsen Yoosefzadeh-Najafabadi Sepideh Torabi Dan Tulpan Istvan Rajcan Milad Eskandari
author_sort	Mohsen Yoosefzadeh-Najafabadi
title	Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods
title_short	Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods
title_full	Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods
title_fullStr	Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods
title_full_unstemmed	Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods
title_sort	genome-wide association studies of soybean yield-related hyperspectral reflectance bands using machine learning-mediated data integration methods
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/b5f591e763d943e390ba7827f0d5b0e3
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_version_	1718406704668868608

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

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