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Molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium
BACKGROUND: Wheat (Triticum aestivium L.) is an important crop globally which has a complex genome. To identify the parents with useful agronomic characteristics that could be used in the various breeding programs, it is very important to understand the genetic diversity among global wheat genotypes...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318758/ https://www.ncbi.nlm.nih.gov/pubmed/32586286 http://dx.doi.org/10.1186/s12864-020-06835-0 |
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author | Mourad, Amira M. I. Belamkar, Vikas Baenziger, P. Stephen |
author_facet | Mourad, Amira M. I. Belamkar, Vikas Baenziger, P. Stephen |
author_sort | Mourad, Amira M. I. |
collection | PubMed |
description | BACKGROUND: Wheat (Triticum aestivium L.) is an important crop globally which has a complex genome. To identify the parents with useful agronomic characteristics that could be used in the various breeding programs, it is very important to understand the genetic diversity among global wheat genotypes. Also, understanding the genetic diversity is useful in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection. RESULTS: To understand the genetic diversity in wheat, a set of 103 spring wheat genotypes which represented five different continents were used. These genotypes were genotyped using 36,720 genotyping-by-sequencing derived SNPs (GBS-SNPs) which were well distributed across wheat chromosomes. The tested 103-wheat genotypes contained three different subpopulations based on population structure, principle coordinate, and kinship analyses. A significant variation was found within and among the subpopulations based on the AMOVA. Subpopulation 1 was found to be the more diverse subpopulation based on the different allelic patterns (Na, Ne, I, h, and uh). No high linkage disequilibrium was found between the 36,720 SNPs. However, based on the genomic level, D genome was found to have the highest LD compared with the two other genomes A and B. The ratio between the number of significant LD/number of non-significant LD suggested that chromosomes 2D, 5A, and 7B are the highest LD chromosomes in their genomes with a value of 0.08, 0.07, and 0.05, respectively. Based on the LD decay, the D genome was found to be the lowest genome with the highest number of haplotype blocks on chromosome 2D. CONCLUSION: The recent study concluded that the 103-spring wheat genotypes and their GBS-SNP markers are very appropriate for GWAS studies and QTL-mapping. The core collection comprises three different subpopulations. Genotypes in subpopulation 1 are the most diverse genotypes and could be used in future breeding programs if they have desired traits. The distribution of LD hotspots across the genome was investigated which provides useful information on the genomic regions that includes interesting genes. |
format | Online Article Text |
id | pubmed-7318758 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73187582020-06-29 Molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium Mourad, Amira M. I. Belamkar, Vikas Baenziger, P. Stephen BMC Genomics Research Article BACKGROUND: Wheat (Triticum aestivium L.) is an important crop globally which has a complex genome. To identify the parents with useful agronomic characteristics that could be used in the various breeding programs, it is very important to understand the genetic diversity among global wheat genotypes. Also, understanding the genetic diversity is useful in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection. RESULTS: To understand the genetic diversity in wheat, a set of 103 spring wheat genotypes which represented five different continents were used. These genotypes were genotyped using 36,720 genotyping-by-sequencing derived SNPs (GBS-SNPs) which were well distributed across wheat chromosomes. The tested 103-wheat genotypes contained three different subpopulations based on population structure, principle coordinate, and kinship analyses. A significant variation was found within and among the subpopulations based on the AMOVA. Subpopulation 1 was found to be the more diverse subpopulation based on the different allelic patterns (Na, Ne, I, h, and uh). No high linkage disequilibrium was found between the 36,720 SNPs. However, based on the genomic level, D genome was found to have the highest LD compared with the two other genomes A and B. The ratio between the number of significant LD/number of non-significant LD suggested that chromosomes 2D, 5A, and 7B are the highest LD chromosomes in their genomes with a value of 0.08, 0.07, and 0.05, respectively. Based on the LD decay, the D genome was found to be the lowest genome with the highest number of haplotype blocks on chromosome 2D. CONCLUSION: The recent study concluded that the 103-spring wheat genotypes and their GBS-SNP markers are very appropriate for GWAS studies and QTL-mapping. The core collection comprises three different subpopulations. Genotypes in subpopulation 1 are the most diverse genotypes and could be used in future breeding programs if they have desired traits. The distribution of LD hotspots across the genome was investigated which provides useful information on the genomic regions that includes interesting genes. BioMed Central 2020-06-26 /pmc/articles/PMC7318758/ /pubmed/32586286 http://dx.doi.org/10.1186/s12864-020-06835-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Mourad, Amira M. I. Belamkar, Vikas Baenziger, P. Stephen Molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium |
title | Molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium |
title_full | Molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium |
title_fullStr | Molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium |
title_full_unstemmed | Molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium |
title_short | Molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium |
title_sort | molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318758/ https://www.ncbi.nlm.nih.gov/pubmed/32586286 http://dx.doi.org/10.1186/s12864-020-06835-0 |
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