Integration of GWAS and WGCNA reveals novel candidate genes for cottonseed oil content in Gossypium hirsutum L.

Genetic improvement of cottonseed oil content represents a crucial strategy for enhancing the comprehensive utilization of cotton. Here, genome-wide association study (GWAS) and weighted gene co-expression network analysis (WGCNA) were integrated to elucidate the genetic control underlying oil content. Phenotypic evaluation of 159 cotton accessions revealed extensive genetic variation, with kernel oil content ranging from 17.81% to 39.50%. Population structure analysis based on 20,213 single nucleotide polymorphisms (SNPs) classified the accessions into two major subpopulations. A total of 18 SNPs exhibited significant associations with oil content, two of which were stably detected across multiple environments using the FarmCPU model. Further haplotype analysis within linkage disequilibrium (LD) blocks confirmed a favorable haplotype on chromosome A05 that was strongly correlated with elevated oil content. Integration of publicly available transcriptome data from 11 ovule developmental stages with WGCNA identified modules significantly linked to oil content. Of the 74 candidate genes within LD intervals, 17 were assigned to WGCNA modules. Functional annotation and enrichment analyses highlighted four putative candidate genes (GH_A05G1503, GH_A05G1506, GH_A05G1531, and GH_A10G2150) involved in oil biosynthesis. These findings deepen our understanding of the genetic mechanisms governing cottonseed oil biosynthesis and lay a foundation for breeding high-oil cotton varieties.

Authors

Institutions

Publication Details

Journal
BMC Plant Biology
Published
2026-06-26
DOI
https://doi.org/10.1186/s12870-026-09340-w
Primary Topic
Research in Cotton Cultivation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integration of GWAS and WGCNA reveals novel candidate genes for cottonseed oil content in Gossypium hirsutum L.

Chaozhu Xing, Xueyu Zhang, Xiuqin Qiao, Yang Liuliu et al.
BMC Plant Biology
Research in Cotton Cultivation
article

Integration of GWAS and WGCNA reveals novel candidate genes for cottonseed oil content in Gossypium hirsutum L.

Chaozhu Xing, Xueyu Zhang, Xiuqin Qiao, Yang Liuliu, Gang LAN, Beibei Lv, Cairong Cao, Pengbo Li, Hangsong Gao, Zhi Xia, Zhanglong Huangfu, Gang Wang, Meng Zhang, Zhuanxia Pan, Baoguo Hou
article en

Abstract

Genetic improvement of cottonseed oil content represents a crucial strategy for enhancing the comprehensive utilization of cotton. Here, genome-wide association study (GWAS) and weighted gene co-expression network analysis (WGCNA) were integrated to elucidate the genetic control underlying oil content. Phenotypic evaluation of 159 cotton accessions revealed extensive genetic variation, with kernel oil content ranging from 17.81% to 39.50%. Population structure analysis based on 20,213 single nucleotide polymorphisms (SNPs) classified the accessions into two major subpopulations. A total of 18 SNPs exhibited significant associations with oil content, two of which were stably detected across multiple environments using the FarmCPU model. Further haplotype analysis within linkage disequilibrium (LD) blocks confirmed a favorable haplotype on chromosome A05 that was strongly correlated with elevated oil content. Integration of publicly available transcriptome data from 11 ovule developmental stages with WGCNA identified modules significantly linked to oil content. Of the 74 candidate genes within LD intervals, 17 were assigned to WGCNA modules. Functional annotation and enrichment analyses highlighted four putative candidate genes (GH_A05G1503, GH_A05G1506, GH_A05G1531, and GH_A10G2150) involved in oil biosynthesis. These findings deepen our understanding of the genetic mechanisms governing cottonseed oil biosynthesis and lay a foundation for breeding high-oil cotton varieties.

BMC Plant Biology
Shanxi Agricultural University (CN), Xinjiang Academy of Agricultural and Reclamation Science (CN), Cotton Research Institute (CN), Chinese Academy of Agricultural Sciences (CN)
Openalex Percentile: Top 9%
Research in Cotton Cultivation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.