Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets

Abstract Barley production worldwide is limited by several abiotic and biotic stresses and breeding of highly productive and adapted varieties is key to overcome these challenges. Leaf scald, caused by Rhynchosporium commune is a major disease of barley that requires the identification of novel sour...

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Autores principales:	Houda Hiddar, Sajid Rehman, Berhane Lakew, Ramesh Pal Singh Verma, Muamar Al-Jaboobi, Adil Moulakat, Zakaria Kehel, Abdelkarim Filali-Maltouf, Michael Baum, Ahmed Amri
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/16f4930193254391b77f9e07c64c4715
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spelling	oai:doaj.org-article:16f4930193254391b77f9e07c64c47152021-12-02T18:49:27ZAssessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets10.1038/s41598-021-94587-62045-2322https://doaj.org/article/16f4930193254391b77f9e07c64c47152021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94587-6https://doaj.org/toc/2045-2322Abstract Barley production worldwide is limited by several abiotic and biotic stresses and breeding of highly productive and adapted varieties is key to overcome these challenges. Leaf scald, caused by Rhynchosporium commune is a major disease of barley that requires the identification of novel sources of resistance. In this study two subsets of genebank accessions were used: one extracted from the Reference set developed within the Generation Challenge Program (GCP) with 191 accessions, and the other with 101 accessions selected using the filtering approach of the Focused Identification of Germplasm Strategy (FIGS). These subsets were evaluated for resistance to scald at the seedling stage under controlled conditions using two Moroccan isolates, and at the adult plant stage in Ethiopia and Morocco. The results showed that both GCP and FIGS subsets were able to identify sources of resistance to leaf scald at both plant growth stages. In addition, the test of independence and goodness of fit showed that FIGS filtering approach was able to capture higher percentages of resistant accessions compared to GCP subset at the seedling stage against two Moroccan scald isolates, and at the adult plant stage against four field populations of Morocco and Ethiopia, with the exception of Holetta nursery 2017. Furthermore, four machine learning models were tuned on training sets to predict scald reactions on the test sets based on diverse metrics (accuracy, specificity, and Kappa). All models efficiently identified resistant accessions with specificities higher than 0.88 but showed different performances between isolates at the seedling and to field populations at the adult plant stage. The findings of our study will help in fine-tuning FIGS approach using machine learning for the selection of best-bet subsets for resistance to scald disease from the large number of genebank accessions.Houda HiddarSajid RehmanBerhane LakewRamesh Pal Singh VermaMuamar Al-JaboobiAdil MoulakatZakaria KehelAbdelkarim Filali-MaltoufMichael BaumAhmed AmriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Houda Hiddar Sajid Rehman Berhane Lakew Ramesh Pal Singh Verma Muamar Al-Jaboobi Adil Moulakat Zakaria Kehel Abdelkarim Filali-Maltouf Michael Baum Ahmed Amri Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets
description	Abstract Barley production worldwide is limited by several abiotic and biotic stresses and breeding of highly productive and adapted varieties is key to overcome these challenges. Leaf scald, caused by Rhynchosporium commune is a major disease of barley that requires the identification of novel sources of resistance. In this study two subsets of genebank accessions were used: one extracted from the Reference set developed within the Generation Challenge Program (GCP) with 191 accessions, and the other with 101 accessions selected using the filtering approach of the Focused Identification of Germplasm Strategy (FIGS). These subsets were evaluated for resistance to scald at the seedling stage under controlled conditions using two Moroccan isolates, and at the adult plant stage in Ethiopia and Morocco. The results showed that both GCP and FIGS subsets were able to identify sources of resistance to leaf scald at both plant growth stages. In addition, the test of independence and goodness of fit showed that FIGS filtering approach was able to capture higher percentages of resistant accessions compared to GCP subset at the seedling stage against two Moroccan scald isolates, and at the adult plant stage against four field populations of Morocco and Ethiopia, with the exception of Holetta nursery 2017. Furthermore, four machine learning models were tuned on training sets to predict scald reactions on the test sets based on diverse metrics (accuracy, specificity, and Kappa). All models efficiently identified resistant accessions with specificities higher than 0.88 but showed different performances between isolates at the seedling and to field populations at the adult plant stage. The findings of our study will help in fine-tuning FIGS approach using machine learning for the selection of best-bet subsets for resistance to scald disease from the large number of genebank accessions.
format	article
author	Houda Hiddar Sajid Rehman Berhane Lakew Ramesh Pal Singh Verma Muamar Al-Jaboobi Adil Moulakat Zakaria Kehel Abdelkarim Filali-Maltouf Michael Baum Ahmed Amri
author_facet	Houda Hiddar Sajid Rehman Berhane Lakew Ramesh Pal Singh Verma Muamar Al-Jaboobi Adil Moulakat Zakaria Kehel Abdelkarim Filali-Maltouf Michael Baum Ahmed Amri
author_sort	Houda Hiddar
title	Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets
title_short	Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets
title_full	Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets
title_fullStr	Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets
title_full_unstemmed	Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets
title_sort	assessment and modeling using machine learning of resistance to scald (rhynchosporium commune) in two specific barley genetic resources subsets
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/16f4930193254391b77f9e07c64c4715
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Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets

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