Publication Overview
TitleConstruction of a High-Density Genetic Map and Its Application to QTL Identification for Fiber Strength in Upland Cotton
AuthorsZhen Zhang, Qun Ge, Aiying Liu, Junwen Li, Juwu Gong, Haihong Shang, Yuzhen Shi, Tingting Chen, Yanling Wang, Koffi Kibalou Palanga, Jamshed Muhammad, Quanwei Lu, Xiaoying Deng, Yunna Tan, Ruixian Liu, Xianyan Zou, Harun Rashid, Muhammad Sajid Iqbal, Wankui Gong, Youlu Yuan
TypeJournal Article
Journal NameCrop Science
Volume57
Year2017
Page(s)774-788
CitationZhen Zhang, Qun Ge, Aiying Liu, Junwen Li, Juwu Gong, Haihong Shang, Yuzhen Shi, Tingting Chen, Yanling Wang, Koffi Kibalou Palanga, Jamshed Muhammad, Quanwei Lu, Xiaoying Deng, Yunna Tan, Ruixian Liu, Xianyan Zou, Harun Rashid, Muhammad Sajid Iqbal, Wankui Gong, Youlu Yuan. Construction of a High-Density Genetic Map and Its Application to QTL Identification for Fiber Strength in Upland Cotton. Crop Science. 2017; 57:774-788.

Abstract

Cotton (Gossypium sp.) is an important worldwide cash crop that provides a competitive renewable natural fiber supply for the demands of textile industry. The development of new textile technologies and the improvement of living standards increase the demands for both fiber quantity and fiber quality. ‘0–153’ is an upland cotton cultivar with excellent fiber quality derived from Asiatic cotton sources, especially with regards to fiber strength. To identify quantitative trait loci (QTLs) for fiber strength in this line, a recombinant inbred line population consisting of 196 lines was developed from a cross between it and ‘sGK9708’. A genetic linkage map consisting of 2393 loci was constructed using this recombinant inbred line population, with single nucleotide polymorphism (SNP) markers from the IntlCottonSNPConsortium_70k chip. Quantitative trait loci for fiber strength were detected across 11 environments using both single-environment and combined multipleenvironment models. A total of 63 QTLs controlling fiber strength were detected by the single-environment model. Sixteen QTLs were identified by the combined multipleenvironment model. These QTLs could make a contribution to the improvement of fiber quality via marker-assisted selection and provide useful information for QTL fine mapping and functional gene research activities as well.
Features
This publication contains information about 134 features:
Feature NameUniquenameType
fiber strengthqFS.0s-RIL.17_ch01.ak09QTL
fiber strengthqFS.0s-RIL.17_ch01.ay08QTL
fiber strengthqFS.0s-RIL.17_ch01.ay10QTL
fiber strengthqFS.0s-RIL.17_ch01.qz08QTL
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fiber strengthqFS.0s-RIL.17_ch02.qz09QTL
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fiber strengthqFS.0s-RIL.17_ch03.ay13.1QTL
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fiber strengthqFS.0s-RIL.17_ch03.ay13.3QTL
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fiber strengthqFS.0s-RIL.17_ch03.zz10QTL
fiber strengthqFS.0s-RIL.17_ch04.ak09.1QTL
fiber strengthqFS.0s-RIL.17_ch04.ak09.2QTL
fiber strengthqFS.0s-RIL.17_ch04.ay08QTL
fiber strengthqFS.0s-RIL.17_ch04.ay09QTL
fiber strengthqFS.0s-RIL.17_ch04.ay10QTL
fiber strengthqFS.0s-RIL.17_ch04.gy10QTL
fiber strengthqFS.0s-RIL.17_ch04.qz08QTL
fiber strengthqFS.0s-RIL.17_ch04.qz09QTL
fiber strengthqFS.0s-RIL.17_ch05.ay08QTL
fiber strengthqFS.0s-RIL.17_ch05.gy10QTL

Pages

Projects
This publication contains information about 1 projects:
Project NameDescription
0s-RIL-2017
Featuremaps
This publication contains information about 1 maps:
Map Name
0-153 x sGK9708, RIL (2017)
Properties
Additional details for this publication include:
Property NameValue
URLhttps://dl.sciencesocieties.org/publications/cs/abstracts/57/2/774