Rendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population

Diversifying available natural resources to cope with abrupt climatic changes and the necessity to equalize rising agricultural production with improved ability to endure environmental influence is the dire need of the day. Inherent allelic variability regarding significant economic traits featuring...

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Autores principales:	Zareen Sarfraz, Mohammad Maroof Shah, Muhammad Sajid Iqbal, Mian Faisal Nazir, Ibrahim Al-Ashkar, Muhammad Ishaq Asif Rehmani, Muhammad Shahid Iqbal, Najeeb Ullah, Ayman El Sabagh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multivariate analyses principal component analysis cluster analysis genetic diversity RILs population yield attributes Agriculture S
Acceso en línea:	https://doaj.org/article/6dd43d2272e9431881e4a5ea654f4eb5
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spelling	oai:doaj.org-article:6dd43d2272e9431881e4a5ea654f4eb52021-11-25T16:12:01ZRendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population10.3390/agronomy111123392073-4395https://doaj.org/article/6dd43d2272e9431881e4a5ea654f4eb52021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2339https://doaj.org/toc/2073-4395Diversifying available natural resources to cope with abrupt climatic changes and the necessity to equalize rising agricultural production with improved ability to endure environmental influence is the dire need of the day. Inherent allelic variability regarding significant economic traits featuring both enhanced productivity and environmental adaptability is one such prominent need. To address this requirement, a series of analyses were conducted in this study for exploring natural diploid wheat germplasm resources. The current study involved 98 Recombinant Inbred Lines (RILs) populations developed by crossing two diploid ‘A’ sub-genome wheat species, <i>Triticum</i><i>monococcum</i> and <i>Triticum</i> <i>boeoticum</i>, enriched with valuable alleles controlling, in particular, biotic and abiotic stresses tolerance. Their 12 phenotypic traits were explored to reveal germplasm value. All traits exhibited vast diversity among parents and RILs via multivariate analysis. Most of the investigated traits depicted significant (<i>p</i> < 0.05) positive correlations enlightening spikelet per spike, total biomass, seed weight per spike, number of seeds per spike, plant height, and days to heading as considerably focused traits for improving hexaploid wheat. Principal component analysis (PCA) exhibited 61.513% of total variation with three PCs for 12 traits. Clustering of genotypes happened in three clades, and the two parents were separated into two extreme clusters, validating their enrichment of diversity. This study provided beneficial aspects of parental resources rich in diverse alleles. They can be efficiently exploited in wheat improvement programs focusing on introgression breeding and the recovery of eroded genetic factors in currently available commercial wheat cultivars to sustain calamities of environmental fluctuations.Zareen SarfrazMohammad Maroof ShahMuhammad Sajid IqbalMian Faisal NazirIbrahim Al-AshkarMuhammad Ishaq Asif RehmaniMuhammad Shahid IqbalNajeeb UllahAyman El SabaghMDPI AGarticlemultivariate analysesprincipal component analysiscluster analysisgenetic diversityRILs populationyield attributesAgricultureSENAgronomy, Vol 11, Iss 2339, p 2339 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multivariate analyses principal component analysis cluster analysis genetic diversity RILs population yield attributes Agriculture S
spellingShingle	multivariate analyses principal component analysis cluster analysis genetic diversity RILs population yield attributes Agriculture S Zareen Sarfraz Mohammad Maroof Shah Muhammad Sajid Iqbal Mian Faisal Nazir Ibrahim Al-Ashkar Muhammad Ishaq Asif Rehmani Muhammad Shahid Iqbal Najeeb Ullah Ayman El Sabagh Rendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population
description	Diversifying available natural resources to cope with abrupt climatic changes and the necessity to equalize rising agricultural production with improved ability to endure environmental influence is the dire need of the day. Inherent allelic variability regarding significant economic traits featuring both enhanced productivity and environmental adaptability is one such prominent need. To address this requirement, a series of analyses were conducted in this study for exploring natural diploid wheat germplasm resources. The current study involved 98 Recombinant Inbred Lines (RILs) populations developed by crossing two diploid ‘A’ sub-genome wheat species, <i>Triticum</i><i>monococcum</i> and <i>Triticum</i> <i>boeoticum</i>, enriched with valuable alleles controlling, in particular, biotic and abiotic stresses tolerance. Their 12 phenotypic traits were explored to reveal germplasm value. All traits exhibited vast diversity among parents and RILs via multivariate analysis. Most of the investigated traits depicted significant (<i>p</i> < 0.05) positive correlations enlightening spikelet per spike, total biomass, seed weight per spike, number of seeds per spike, plant height, and days to heading as considerably focused traits for improving hexaploid wheat. Principal component analysis (PCA) exhibited 61.513% of total variation with three PCs for 12 traits. Clustering of genotypes happened in three clades, and the two parents were separated into two extreme clusters, validating their enrichment of diversity. This study provided beneficial aspects of parental resources rich in diverse alleles. They can be efficiently exploited in wheat improvement programs focusing on introgression breeding and the recovery of eroded genetic factors in currently available commercial wheat cultivars to sustain calamities of environmental fluctuations.
format	article
author	Zareen Sarfraz Mohammad Maroof Shah Muhammad Sajid Iqbal Mian Faisal Nazir Ibrahim Al-Ashkar Muhammad Ishaq Asif Rehmani Muhammad Shahid Iqbal Najeeb Ullah Ayman El Sabagh
author_facet	Zareen Sarfraz Mohammad Maroof Shah Muhammad Sajid Iqbal Mian Faisal Nazir Ibrahim Al-Ashkar Muhammad Ishaq Asif Rehmani Muhammad Shahid Iqbal Najeeb Ullah Ayman El Sabagh
author_sort	Zareen Sarfraz
title	Rendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population
title_short	Rendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population
title_full	Rendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population
title_fullStr	Rendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population
title_full_unstemmed	Rendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population
title_sort	rendering multivariate statistical models for genetic diversity assessment in a-genome diploid wheat population
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6dd43d2272e9431881e4a5ea654f4eb5
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Rendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population

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