Genome-Wide Association Study of Lint Percentage in Gossypium hirsutum L. Races.
Yuanyuan WangXinlei GuoXiaoyan CaiYanchao XuRunrun SunMuhammad Jawad UmerKunbo WangTengfei QinYuqing HouYuhong WangPan ZhangZihan WangFang LiuQinglian WangZhongli ZhouPublished in: International journal of molecular sciences (2023)
Lint percentage is one of the most essential yield components and an important economic index for cotton planting. Improving lint percentage is an effective way to achieve high-yield in cotton breeding worldwide, especially upland cotton ( Gossypium hirsutum L.). However, the genetic basis controlling lint percentage has not yet been systematically understood. Here, we performed a genome-wide association mapping for lint percentage using a natural population consisting of 189 G. hirsutum accessions (188 accessions of G. hirsutum races and one cultivar TM-1). The results showed that 274 single-nucleotide polymorphisms (SNPs) significantly associated with lint percentage were detected, and they were distributed on 24 chromosomes. Forty-five SNPs were detected at least by two models or at least in two environments, and their 5 Mb up- and downstream regions included 584 makers related to lint percentage identified in previous studies. In total, 11 out of 45 SNPs were detected at least in two environments, and their 550 Kb up- and downstream region contained 335 genes. Through RNA sequencing, gene annotation, qRT-PCR, protein-protein interaction analysis, the cis -elements of the promotor region, and related miRNA prediction, Gh_D12G0934 and Gh_A08G0526 were selected as key candidate genes for fiber initiation and elongation, respectively. These excavated SNPs and candidate genes could supplement marker and gene information for deciphering the genetic basis of lint percentage and facilitate high-yield breeding programs of G. hirsutum ultimately.
Keyphrases
- genome wide identification
- genome wide
- genome wide analysis
- genome wide association
- transcription factor
- dna methylation
- copy number
- protein protein
- small molecule
- public health
- genome wide association study
- single cell
- gene expression
- high resolution
- social media
- healthcare
- health information
- mass spectrometry
- case control
- high density
- bioinformatics analysis