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GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers
It is of great importance to identify quantitative trait loci (QTL) controlling fiber quality traits and yield components for future marker-assisted selection (MAS) and candidate gene function identifications. In this study, two kinds of traits in 231 F(6:8) recombinant inbred lines (RILs), derived...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157485/ https://www.ncbi.nlm.nih.gov/pubmed/30283462 http://dx.doi.org/10.3389/fpls.2018.01067 |
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author | Liu, Ruixian Gong, Juwu Xiao, Xianghui Zhang, Zhen Li, Junwen Liu, Aiying Lu, Quanwei Shang, Haihong Shi, Yuzhen Ge, Qun Iqbal, Muhammad S. Deng, Xiaoying Li, Shaoqi Pan, Jingtao Duan, Li Zhang, Qi Jiang, Xiao Zou, Xianyan Hafeez, Abdul Chen, Quanjia Geng, Hongwei Gong, Wankui Yuan, Youlu |
author_facet | Liu, Ruixian Gong, Juwu Xiao, Xianghui Zhang, Zhen Li, Junwen Liu, Aiying Lu, Quanwei Shang, Haihong Shi, Yuzhen Ge, Qun Iqbal, Muhammad S. Deng, Xiaoying Li, Shaoqi Pan, Jingtao Duan, Li Zhang, Qi Jiang, Xiao Zou, Xianyan Hafeez, Abdul Chen, Quanjia Geng, Hongwei Gong, Wankui Yuan, Youlu |
author_sort | Liu, Ruixian |
collection | PubMed |
description | It is of great importance to identify quantitative trait loci (QTL) controlling fiber quality traits and yield components for future marker-assisted selection (MAS) and candidate gene function identifications. In this study, two kinds of traits in 231 F(6:8) recombinant inbred lines (RILs), derived from an intraspecific cross between Xinluzao24, a cultivar with elite fiber quality, and Lumianyan28, a cultivar with wide adaptability and high yield potential, were measured in nine environments. This RIL population was genotyped by 122 SSR and 4729 SNP markers, which were also used to construct the genetic map. The map covered 2477.99 cM of hirsutum genome, with an average marker interval of 0.51 cM between adjacent markers. As a result, a total of 134 QTLs for fiber quality traits and 122 QTLs for yield components were detected, with 2.18–24.45 and 1.68–28.27% proportions of the phenotypic variance explained by each QTL, respectively. Among these QTLs, 57 were detected in at least two environments, named stable QTLs. A total of 209 and 139 quantitative trait nucleotides (QTNs) were associated with fiber quality traits and yield components by four multilocus genome-wide association studies methods, respectively. Among these QTNs, 74 were detected by at least two algorithms or in two environments. The candidate genes harbored by 57 stable QTLs were compared with the ones associated with QTN, and 35 common candidate genes were found. Among these common candidate genes, four were possibly “pleiotropic.” This study provided important information for MAS and candidate gene functional studies. |
format | Online Article Text |
id | pubmed-6157485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61574852018-10-03 GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers Liu, Ruixian Gong, Juwu Xiao, Xianghui Zhang, Zhen Li, Junwen Liu, Aiying Lu, Quanwei Shang, Haihong Shi, Yuzhen Ge, Qun Iqbal, Muhammad S. Deng, Xiaoying Li, Shaoqi Pan, Jingtao Duan, Li Zhang, Qi Jiang, Xiao Zou, Xianyan Hafeez, Abdul Chen, Quanjia Geng, Hongwei Gong, Wankui Yuan, Youlu Front Plant Sci Plant Science It is of great importance to identify quantitative trait loci (QTL) controlling fiber quality traits and yield components for future marker-assisted selection (MAS) and candidate gene function identifications. In this study, two kinds of traits in 231 F(6:8) recombinant inbred lines (RILs), derived from an intraspecific cross between Xinluzao24, a cultivar with elite fiber quality, and Lumianyan28, a cultivar with wide adaptability and high yield potential, were measured in nine environments. This RIL population was genotyped by 122 SSR and 4729 SNP markers, which were also used to construct the genetic map. The map covered 2477.99 cM of hirsutum genome, with an average marker interval of 0.51 cM between adjacent markers. As a result, a total of 134 QTLs for fiber quality traits and 122 QTLs for yield components were detected, with 2.18–24.45 and 1.68–28.27% proportions of the phenotypic variance explained by each QTL, respectively. Among these QTLs, 57 were detected in at least two environments, named stable QTLs. A total of 209 and 139 quantitative trait nucleotides (QTNs) were associated with fiber quality traits and yield components by four multilocus genome-wide association studies methods, respectively. Among these QTNs, 74 were detected by at least two algorithms or in two environments. The candidate genes harbored by 57 stable QTLs were compared with the ones associated with QTN, and 35 common candidate genes were found. Among these common candidate genes, four were possibly “pleiotropic.” This study provided important information for MAS and candidate gene functional studies. Frontiers Media S.A. 2018-09-13 /pmc/articles/PMC6157485/ /pubmed/30283462 http://dx.doi.org/10.3389/fpls.2018.01067 Text en Copyright © 2018 Liu, Gong, Xiao, Zhang, Li, Liu, Lu, Shang, Shi, Ge, Iqbal, Deng, Li, Pan, Duan, Zhang, Jiang, Zou, Hafeez, Chen, Geng, Gong and Yuan. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Liu, Ruixian Gong, Juwu Xiao, Xianghui Zhang, Zhen Li, Junwen Liu, Aiying Lu, Quanwei Shang, Haihong Shi, Yuzhen Ge, Qun Iqbal, Muhammad S. Deng, Xiaoying Li, Shaoqi Pan, Jingtao Duan, Li Zhang, Qi Jiang, Xiao Zou, Xianyan Hafeez, Abdul Chen, Quanjia Geng, Hongwei Gong, Wankui Yuan, Youlu GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers |
title | GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers |
title_full | GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers |
title_fullStr | GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers |
title_full_unstemmed | GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers |
title_short | GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers |
title_sort | gwas analysis and qtl identification of fiber quality traits and yield components in upland cotton using enriched high-density snp markers |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157485/ https://www.ncbi.nlm.nih.gov/pubmed/30283462 http://dx.doi.org/10.3389/fpls.2018.01067 |
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