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Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat
BACKGROUND: Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and...
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/PMC9190149/ https://www.ncbi.nlm.nih.gov/pubmed/35698038 http://dx.doi.org/10.1186/s12870-022-03677-8 |
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author | Wei, Jun Fang, Yu Jiang, Hao Wu, Xing-ting Zuo, Jing-hong Xia, Xian-chun Li, Jin-quan Stich, Benjamin Cao, Hong Liu, Yong-xiu |
author_facet | Wei, Jun Fang, Yu Jiang, Hao Wu, Xing-ting Zuo, Jing-hong Xia, Xian-chun Li, Jin-quan Stich, Benjamin Cao, Hong Liu, Yong-xiu |
author_sort | Wei, Jun |
collection | PubMed |
description | BACKGROUND: Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analysis has displayed the powerful capability to identify candidate genes for major quantitative trait loci (QTLs), but such studies have rarely been carried out in wheat. In this study, candidate genes related to yield were predicted by a combined use of linkage mapping and weighted gene co-expression network analysis (WGCNA) in a recombinant inbred line population. RESULTS: QTL mapping was performed for plant height (PH), spike length (SL) and seed traits. A total of 68 QTLs were identified for them, among which, 12 QTLs were stably identified across different environments. Using RNA sequencing, we scanned the 99,168 genes expression patterns of the whole spike for the recombinant inbred line population. By the combined use of QTL mapping and WGCNA, 29, 47, 20, 26, 54, 46 and 22 candidate genes were predicted for PH, SL, kernel length (KL), kernel width, thousand kernel weight, seed dormancy, and seed vigor, respectively. Candidate genes for different traits had distinct preferences. The known PH regulation genes Rht-B and Rht-D, and the known seed dormancy regulation genes TaMFT can be selected as candidate gene. Moreover, further experiment revealed that there was a SL regulatory QTL located in an interval of about 7 Mbp on chromosome 7A, named TaSL1, which also involved in the regulation of KL. CONCLUSIONS: A combination of QTL mapping and WGCNA was applied to predicted wheat candidate genes for PH, SL and seed traits. This strategy will facilitate the identification of candidate genes for related QTLs in wheat. In addition, the QTL TaSL1 that had multi-effect regulation of KL and SL was identified, which can be used for wheat improvement. These results provided valuable molecular marker and gene information for fine mapping and cloning of the yield-related trait loci in the future. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12870-022-03677-8. |
format | Online Article Text |
id | pubmed-9190149 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91901492022-06-14 Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat Wei, Jun Fang, Yu Jiang, Hao Wu, Xing-ting Zuo, Jing-hong Xia, Xian-chun Li, Jin-quan Stich, Benjamin Cao, Hong Liu, Yong-xiu BMC Plant Biol Research BACKGROUND: Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analysis has displayed the powerful capability to identify candidate genes for major quantitative trait loci (QTLs), but such studies have rarely been carried out in wheat. In this study, candidate genes related to yield were predicted by a combined use of linkage mapping and weighted gene co-expression network analysis (WGCNA) in a recombinant inbred line population. RESULTS: QTL mapping was performed for plant height (PH), spike length (SL) and seed traits. A total of 68 QTLs were identified for them, among which, 12 QTLs were stably identified across different environments. Using RNA sequencing, we scanned the 99,168 genes expression patterns of the whole spike for the recombinant inbred line population. By the combined use of QTL mapping and WGCNA, 29, 47, 20, 26, 54, 46 and 22 candidate genes were predicted for PH, SL, kernel length (KL), kernel width, thousand kernel weight, seed dormancy, and seed vigor, respectively. Candidate genes for different traits had distinct preferences. The known PH regulation genes Rht-B and Rht-D, and the known seed dormancy regulation genes TaMFT can be selected as candidate gene. Moreover, further experiment revealed that there was a SL regulatory QTL located in an interval of about 7 Mbp on chromosome 7A, named TaSL1, which also involved in the regulation of KL. CONCLUSIONS: A combination of QTL mapping and WGCNA was applied to predicted wheat candidate genes for PH, SL and seed traits. This strategy will facilitate the identification of candidate genes for related QTLs in wheat. In addition, the QTL TaSL1 that had multi-effect regulation of KL and SL was identified, which can be used for wheat improvement. These results provided valuable molecular marker and gene information for fine mapping and cloning of the yield-related trait loci in the future. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12870-022-03677-8. BioMed Central 2022-06-13 /pmc/articles/PMC9190149/ /pubmed/35698038 http://dx.doi.org/10.1186/s12870-022-03677-8 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 Wei, Jun Fang, Yu Jiang, Hao Wu, Xing-ting Zuo, Jing-hong Xia, Xian-chun Li, Jin-quan Stich, Benjamin Cao, Hong Liu, Yong-xiu Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat |
title | Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat |
title_full | Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat |
title_fullStr | Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat |
title_full_unstemmed | Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat |
title_short | Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat |
title_sort | combining qtl mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9190149/ https://www.ncbi.nlm.nih.gov/pubmed/35698038 http://dx.doi.org/10.1186/s12870-022-03677-8 |
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