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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
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.
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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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