GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers

Publication Overview
TitleGWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers
AuthorsLiu R, Gong J, Xiao X, Zhang Z, Li J, Liu A, Lu Q, Shang H, Shi Y, Ge Q, Iqbal MS, Deng X, Li S, Pan J, Duan L, Zhang Q, Jiang X, Zou X, Hafeez A, Chen Q, Geng H, Gong W, Yuan Y
TypeJournal Article
Journal NameFrontiers in plant science
Volume9
Year2018
Page(s)1067
CitationLiu R, Gong J, Xiao X, Zhang Z, Li J, Liu A, Lu Q, Shang H, Shi Y, Ge Q, Iqbal MS, Deng X, Li S, Pan J, Duan L, Zhang Q, Jiang X, Zou X, Hafeez A, Chen Q, Geng H, Gong W, Yuan Y. GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers. Frontiers in plant science. 2018; 9:1067.

Abstract

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 F6:8 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.
Germplasm
This publication contains information about 1 stocks:
Stock NameGRIN IDSpeciesType
LMY28 x XLZ24, RILGossypium hirsutumpopulation
Features
This publication contains information about 364 features:
Feature NameUniquenameType
fiber strengthqFS.LX-RIL_ch07.14AY.2QTL
fiber strengthqFS.LX-RIL_ch07.14KELQTL
fiber strengthqFS.LX-RIL_ch07.14LQ.1QTL
fiber strengthqFS.LX-RIL_ch07.14LQ.2QTL
fiber strengthqFS.LX-RIL_ch07.15ALEQTL
fiber strengthqFS.LX-RIL_ch07.15AYQTL
fiber strengthqFS.LX-RIL_ch07.16AYQTL
fiber strengthqFS.LX-RIL_ch08.15ALEQTL
fiber strengthqFS.LX-RIL_ch09.14AYQTL
fiber strengthqFS.LX-RIL_ch09.14KELQTL
fiber strengthqFS.LX-RIL_ch10.14LQQTL
fiber strengthqFS.LX-RIL_ch10.15ALEQTL
fiber strengthqFS.LX-RIL_ch10.16AYQTL
fiber strengthqFS.LX-RIL_ch11.14AYQTL
fiber strengthqFS.LX-RIL_ch11.14KELQTL
fiber strengthqFS.LX-RIL_ch11.14LQ.1QTL
fiber strengthqFS.LX-RIL_ch11.14LQ.2QTL
fiber strengthqFS.LX-RIL_ch12.13AYQTL
fiber strengthqFS.LX-RIL_ch13.13AY.1QTL
fiber strengthqFS.LX-RIL_ch13.13AY.2QTL
fiber strengthqFS.LX-RIL_ch13.13AY.3QTL
fiber strengthqFS.LX-RIL_ch13.13AY.4QTL
fiber strengthqFS.LX-RIL_ch13.15AYQTL
fiber strengthqFS.LX-RIL_ch15.13LQQTL
fiber strengthqFS.LX-RIL_ch15.14KELQTL

Pages

Projects
This publication contains information about 1 projects:
Project NameDescription
LX-RIL-2018
Featuremaps
This publication contains information about 1 maps:
Map Name
LMY28 x XLZ24, RIL (2018)
Properties
Additional details for this publication include:
Property NameValue
DOI10.3389/fpls.2018.01067
Elocation10.3389/fpls.2018.01067
ISSN1664-462X
Journal AbbreviationFront Plant Sci
Journal CountrySwitzerland
LanguageEnglish
Language Abbreng
pISSN1664-462X
Publication Date2018
Publication ModelElectronic-eCollection
Publication TypeJournal Article
Published Location1067