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Genetic diversity in Sickleweed (Falcaria vulgaris) and using stepwise regression to identify marker associated with traits
One of the well-known medicinal plants in the Falcaria genus is Sickleweed. Falcaria species exhibit a high degree of genetic variability, posing challenges in the examination of genetic diversity due to the significant potential for hybridization and introgression among them. Utilizing morphologica...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372081/ https://www.ncbi.nlm.nih.gov/pubmed/37495658 http://dx.doi.org/10.1038/s41598-023-39419-5 |
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author | Rahimi, Mehdi AhmadiAfzadi, Masoud Kordrostami, Mojtaba |
author_facet | Rahimi, Mehdi AhmadiAfzadi, Masoud Kordrostami, Mojtaba |
author_sort | Rahimi, Mehdi |
collection | PubMed |
description | One of the well-known medicinal plants in the Falcaria genus is Sickleweed. Falcaria species exhibit a high degree of genetic variability, posing challenges in the examination of genetic diversity due to the significant potential for hybridization and introgression among them. Utilizing morphological traits and molecular markers may prove to be a valuable approach in evaluating and harnessing germplasm, considering the current obstacles faced in breeding this medicinal herb. In 2021, fifteen Sickleweed populations were cultivated in pots under field conditions, employing a randomized complete block design with three replications. This aimed to assess genetic diversity and conduct marker-trait association analyses utilizing morpho-physiological characteristics and SSR markers. The Sickleweed populations displayed considerable genetic diversity across all traits. Through cluster analysis of traits and the utilization of the UPGMA method based on the Gower distance matrix, the population was classified into three distinct clusters. Upon examining all genotypes, 52 polymorphic bands were detected, with an average of 8.68 bands per primer. The average expected heterozygosity across all loci was 0.864, while the average PIC was 0.855. Molecular data analysis employing the Jaccard similarity index and UPGMA method revealed the division of Sickleweed populations into two major groups. Furthermore, the results of molecular variance analysis indicated that variation within the population exceeded that between populations. Thirty-two SSR fragments were found to be significantly associated with genomic regions controlling the studied traits, determined through the application of stepwise regression. Selection based on molecular markers offers a rapid method for breeding programs, with the genetic information obtained from these markers playing a crucial role. Therefore, alongside traits, selecting superior genotypes and populations of high value in breeding programs becomes feasible. The findings highlight that certain markers are linked to multiple traits, emphasizing the critical importance of this characteristic in plant breeding for the simultaneous improvement of numerous traits. The study’s insights regarding markers hold potential for application in Sickleweed breeding programs. |
format | Online Article Text |
id | pubmed-10372081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103720812023-07-28 Genetic diversity in Sickleweed (Falcaria vulgaris) and using stepwise regression to identify marker associated with traits Rahimi, Mehdi AhmadiAfzadi, Masoud Kordrostami, Mojtaba Sci Rep Article One of the well-known medicinal plants in the Falcaria genus is Sickleweed. Falcaria species exhibit a high degree of genetic variability, posing challenges in the examination of genetic diversity due to the significant potential for hybridization and introgression among them. Utilizing morphological traits and molecular markers may prove to be a valuable approach in evaluating and harnessing germplasm, considering the current obstacles faced in breeding this medicinal herb. In 2021, fifteen Sickleweed populations were cultivated in pots under field conditions, employing a randomized complete block design with three replications. This aimed to assess genetic diversity and conduct marker-trait association analyses utilizing morpho-physiological characteristics and SSR markers. The Sickleweed populations displayed considerable genetic diversity across all traits. Through cluster analysis of traits and the utilization of the UPGMA method based on the Gower distance matrix, the population was classified into three distinct clusters. Upon examining all genotypes, 52 polymorphic bands were detected, with an average of 8.68 bands per primer. The average expected heterozygosity across all loci was 0.864, while the average PIC was 0.855. Molecular data analysis employing the Jaccard similarity index and UPGMA method revealed the division of Sickleweed populations into two major groups. Furthermore, the results of molecular variance analysis indicated that variation within the population exceeded that between populations. Thirty-two SSR fragments were found to be significantly associated with genomic regions controlling the studied traits, determined through the application of stepwise regression. Selection based on molecular markers offers a rapid method for breeding programs, with the genetic information obtained from these markers playing a crucial role. Therefore, alongside traits, selecting superior genotypes and populations of high value in breeding programs becomes feasible. The findings highlight that certain markers are linked to multiple traits, emphasizing the critical importance of this characteristic in plant breeding for the simultaneous improvement of numerous traits. The study’s insights regarding markers hold potential for application in Sickleweed breeding programs. Nature Publishing Group UK 2023-07-26 /pmc/articles/PMC10372081/ /pubmed/37495658 http://dx.doi.org/10.1038/s41598-023-39419-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Rahimi, Mehdi AhmadiAfzadi, Masoud Kordrostami, Mojtaba Genetic diversity in Sickleweed (Falcaria vulgaris) and using stepwise regression to identify marker associated with traits |
title | Genetic diversity in Sickleweed (Falcaria vulgaris) and using stepwise regression to identify marker associated with traits |
title_full | Genetic diversity in Sickleweed (Falcaria vulgaris) and using stepwise regression to identify marker associated with traits |
title_fullStr | Genetic diversity in Sickleweed (Falcaria vulgaris) and using stepwise regression to identify marker associated with traits |
title_full_unstemmed | Genetic diversity in Sickleweed (Falcaria vulgaris) and using stepwise regression to identify marker associated with traits |
title_short | Genetic diversity in Sickleweed (Falcaria vulgaris) and using stepwise regression to identify marker associated with traits |
title_sort | genetic diversity in sickleweed (falcaria vulgaris) and using stepwise regression to identify marker associated with traits |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372081/ https://www.ncbi.nlm.nih.gov/pubmed/37495658 http://dx.doi.org/10.1038/s41598-023-39419-5 |
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