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Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice
BACKGROUND: Plant variety identification is the one most important of agricultural systems. Development of DNA marker profiles of released varieties to compare with candidate variety or future variety is required. However, strictly speaking, scientists did not use most existing variety identificatio...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751067/ https://www.ncbi.nlm.nih.gov/pubmed/35012448 http://dx.doi.org/10.1186/s12859-022-04562-9 |
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author | Yuan, Xiong Li, Zirong Xiong, Liwen Song, Sufeng Zheng, Xingfei Tang, Zhonghai Yuan, Zheming Li, Lanzhi |
author_facet | Yuan, Xiong Li, Zirong Xiong, Liwen Song, Sufeng Zheng, Xingfei Tang, Zhonghai Yuan, Zheming Li, Lanzhi |
author_sort | Yuan, Xiong |
collection | PubMed |
description | BACKGROUND: Plant variety identification is the one most important of agricultural systems. Development of DNA marker profiles of released varieties to compare with candidate variety or future variety is required. However, strictly speaking, scientists did not use most existing variety identification techniques for “identification” but for “distinction of a limited number of cultivars,” of which generalization ability always not be well estimated. Because many varieties have similar genetic backgrounds, even some essentially derived varieties (EDVs) are involved, which brings difficulties for identification and breeding progress. A fast, accurate variety identification method, which also has good performance on EDV determination, needs to be developed. RESULTS: In this study, with the strategy of “Divide and Conquer,” a variety identification method Conditional Random Selection (CRS) method based on SNP of the whole genome of 3024 rice varieties was developed and be applied in essentially derived variety (EDV) identification of rice. CRS is a fast, efficient, and automated variety identification method. Meanwhile, in practical, with the optimal threshold of identity score searched in this study, the set of SNP (including 390 SNPs) showed optimal performance on EDV and non-EDV identification in two independent testing datasets. CONCLUSION: This approach first selected a minimal set of SNPs to discriminate non-EDVs in the 3000 Rice Genome Project, then united several simplified SNP sets to improve its generalization ability for EDV and non-EDV identification in testing datasets. The results suggested that the CRS method outperformed traditional feature selection methods. Furthermore, it provides a new way to screen out core SNP loci from the whole genome for DNA fingerprinting of crop varieties and be useful for crop breeding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04562-9. |
format | Online Article Text |
id | pubmed-8751067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87510672022-01-11 Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice Yuan, Xiong Li, Zirong Xiong, Liwen Song, Sufeng Zheng, Xingfei Tang, Zhonghai Yuan, Zheming Li, Lanzhi BMC Bioinformatics Research BACKGROUND: Plant variety identification is the one most important of agricultural systems. Development of DNA marker profiles of released varieties to compare with candidate variety or future variety is required. However, strictly speaking, scientists did not use most existing variety identification techniques for “identification” but for “distinction of a limited number of cultivars,” of which generalization ability always not be well estimated. Because many varieties have similar genetic backgrounds, even some essentially derived varieties (EDVs) are involved, which brings difficulties for identification and breeding progress. A fast, accurate variety identification method, which also has good performance on EDV determination, needs to be developed. RESULTS: In this study, with the strategy of “Divide and Conquer,” a variety identification method Conditional Random Selection (CRS) method based on SNP of the whole genome of 3024 rice varieties was developed and be applied in essentially derived variety (EDV) identification of rice. CRS is a fast, efficient, and automated variety identification method. Meanwhile, in practical, with the optimal threshold of identity score searched in this study, the set of SNP (including 390 SNPs) showed optimal performance on EDV and non-EDV identification in two independent testing datasets. CONCLUSION: This approach first selected a minimal set of SNPs to discriminate non-EDVs in the 3000 Rice Genome Project, then united several simplified SNP sets to improve its generalization ability for EDV and non-EDV identification in testing datasets. The results suggested that the CRS method outperformed traditional feature selection methods. Furthermore, it provides a new way to screen out core SNP loci from the whole genome for DNA fingerprinting of crop varieties and be useful for crop breeding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04562-9. BioMed Central 2022-01-10 /pmc/articles/PMC8751067/ /pubmed/35012448 http://dx.doi.org/10.1186/s12859-022-04562-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Yuan, Xiong Li, Zirong Xiong, Liwen Song, Sufeng Zheng, Xingfei Tang, Zhonghai Yuan, Zheming Li, Lanzhi Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice |
title | Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice |
title_full | Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice |
title_fullStr | Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice |
title_full_unstemmed | Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice |
title_short | Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice |
title_sort | effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751067/ https://www.ncbi.nlm.nih.gov/pubmed/35012448 http://dx.doi.org/10.1186/s12859-022-04562-9 |
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