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

2018 ◽  
Vol 9 ◽  
Author(s):  
Ruixian Liu ◽  
Juwu Gong ◽  
Xianghui Xiao ◽  
Zhen Zhang ◽  
Junwen Li ◽  
...  
Keyword(s):  
Yield Components ◽  
Upland Cotton ◽  
Fiber Quality ◽  
Snp Markers ◽  
High Density ◽  
Quality Traits ◽  
Fiber Quality Traits ◽  
Gwas Analysis ◽  
Qtl Identification

scholarly journals High-density linkage map construction and QTL analyses for fiber quality, yield and morphological traits using CottonSNP63K array in upland cotton (Gossypium hirsutum L.)

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Kuang Zhang ◽  
Vasu Kuraparthy ◽  
Hui Fang ◽  
Linglong Zhu ◽  
Shilpa Sood ◽  
...  
Keyword(s):  
Upland Cotton ◽  
Morphological Traits ◽  
Fiber Quality ◽  
High Density ◽  
Genetic Maps ◽  
Yield Traits ◽  
Quality Traits ◽  
Fiber Quality Traits ◽  
Favorable Alleles ◽  
Cotton Breeding

Abstract Background Improving fiber quality and yield are the primary research objectives in cotton breeding for enhancing the economic viability and sustainability of Upland cotton production. Identifying the quantitative trait loci (QTL) for fiber quality and yield traits using the high-density SNP-based genetic maps allows for bridging genomics with cotton breeding through marker assisted and genomic selection. In this study, a recombinant inbred line (RIL) population, derived from cross between two parental accessions, which represent broad allele diversity in Upland cotton, was used to construct high-density SNP-based linkage maps and to map the QTLs controlling important cotton traits. Results Molecular genetic mapping using RIL population produced a genetic map of 3129 SNPs, mapped at a density of 1.41 cM. Genetic maps of the individual chromosomes showed good collinearity with the sequence based physical map. A total of 106 QTLs were identified which included 59 QTLs for six fiber quality traits, 38 QTLs for four yield traits and 9 QTLs for two morphological traits. Sub-genome wide, 57 QTLs were mapped in A sub-genome and 49 were mapped in D sub-genome. More than 75% of the QTLs with favorable alleles were contributed by the parental accession NC05AZ06. Forty-six mapped QTLs each explained more than 10% of the phenotypic variation. Further, we identified 21 QTL clusters where 12 QTL clusters were mapped in the A sub-genome and 9 were mapped in the D sub-genome. Candidate gene analyses of the 11 stable QTL harboring genomic regions identified 19 putative genes which had functional role in cotton fiber development. Conclusion We constructed a high-density genetic map of SNPs in Upland cotton. Collinearity between genetic and physical maps indicated no major structural changes in the genetic mapping populations. Most traits showed high broad-sense heritability. One hundred and six QTLs were identified for the fiber quality, yield and morphological traits. Majority of the QTLs with favorable alleles were contributed by improved parental accession. More than 70% of the mapped QTLs shared the similar map position with previously reported QTLs which suggest the genetic relatedness of Upland cotton germplasm. Identification of QTL clusters could explain the correlation among some fiber quality traits in cotton. Stable and major QTLs and QTL clusters of traits identified in the current study could be the targets for map-based cloning and marker assisted selection (MAS) in cotton breeding. The genomic region on D12 containing the major stable QTLs for micronaire, fiber strength and lint percentage could be potential targets for MAS and gene cloning of fiber quality traits in cotton.

Construction of genetic map and QTL analysis of fiber quality traits for Upland cotton (Gossypium hirsutum L.)

Euphytica ◽  
2014 ◽  
Vol 201 (2) ◽  
pp. 195-213 ◽  
Author(s):  
Shiyi Tang ◽  
Zhonghua Teng ◽  
Tengfei Zhai ◽  
Xiaomei Fang ◽  
Fang Liu ◽  
...  
Keyword(s):  
Gossypium Hirsutum ◽  
Genetic Map ◽  
Qtl Analysis ◽  
Upland Cotton ◽  
Fiber Quality ◽  
Quality Traits ◽  
Gossypium Hirsutum L ◽  
Fiber Quality Traits

Genetic map and QTL controlling fiber quality traits in upland cotton (Gossypium hirsutum L.)

Euphytica ◽  
2014 ◽  
Vol 203 (3) ◽  
pp. 615-628 ◽  
Author(s):  
Zhaoyun Tan ◽  
Xiaomei Fang ◽  
Shiyi Tang ◽  
Jian Zhang ◽  
Dajun Liu ◽  
...  
Keyword(s):  
Gossypium Hirsutum ◽  
Genetic Map ◽  
Upland Cotton ◽  
Fiber Quality ◽  
Quality Traits ◽  
Gossypium Hirsutum L ◽  
Fiber Quality Traits

scholarly journals QTL and genetic analysis controlling fiber quality traits using paternal backcross population in Upland Cotton

2019 ◽  
Author(s):  
Lingling Ma ◽  
Ying Su ◽  
Yumei Wang ◽  
Hushai Nie ◽  
Yupeng Cui ◽  
...  
Keyword(s):  
Upland Cotton ◽  
Fiber Strength ◽  
Fiber Quality ◽  
Field Trials ◽  
Quality Traits ◽  
Backcross Population ◽  
Fiber Quality Traits ◽  
Ril Population ◽  
Cotton Hybrid ◽  
Novel Alleles

ABSTRACTIn present study, F14 recombinant inbred line (RIL) population was backcrossed to paternal parent for a paternal backcross (BC/P) population, deriving from one Upland cotton hybrid. Three repetitive BC/P field trials and one BC/M field trial were performed including both two BC populations and the original RIL population. Totally, for fiber quality traits, 24 novel QTLs were detected and 13 QTLs validated previous results. And 19 quantitative trait loci (QTL) in BC/P populations explained 5.01% - 22.09% of phenotype variation (PV). Among the 19 QTLs, three QTLs were detected simultaneously in BC/M population. The present study provided novel alleles of male parent for fiber quality traits with positive genetic effects. Particularly, qFS-Chr3-1 controlling fiber strength explained 22.09% of PV in BC/P population, which increased 0.48 cN/tex for fiber strength. A total of seven, two, eight, two and six QTLs explained over 10.00% of PV for fiber length, fiber uniformity, fiber strength, fiber elongation and fiber micronaire, respectively. In the RIL population, six common QTLs detected in more than one environment such as qFL-Chr1-2, qFS-Chr5-1, qFS-Chr9-1, qFS-Chr21-1, qFM-Chr9-1 and qFM-Chr9-2. Two common QTLs of qFE-Chr2-2 (TMB2386-SWU12343) and qFM-Chr9-1 (NAU2873-CGR6771) explained 22.42% and 21.91% of PV. In addition, a total of 142 and 46 epistatic QTLs and QTL × environments (E-QTLs and QQEs) were identified in RIL-P and BC/P populations, respectively.

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