Dataset Information

Genetic Factors Underlying Single Fiber Quality in A-Genome Donor Asian Cotton (Gossypium arboreum).

ABSTRACT: The study of A-genome Asian cotton as a potential fiber donor in Gossypium species may offer an enhanced understanding of complex genetics and novel players related to fiber quality traits. Assessment of individual fibers providing classified fiber quality information to the textile industry is Advanced Fiber Information System (AFIS) in the recent technological era. Keeping the scenario, a diverse collection of 215 Asiatic cotton accessions were evaluated across three agro-ecological zones of China. Genome-Wide Association Studies (GWAS) was performed to detect association signals related to 17 AFIS fiber quality traits grouped into four categories viz: NEPs, fiber length, maturity, and fineness. Significant correlations were found within as well as among different categories of various traits related to fiber quality. Fiber fineness has shown a strong correlation to all other categories, whereas these categories are shown interrelationships via fiber-fineness. A total of 7,429 SNPs were found in association with 17 investigated traits, of which 177 were selected as lead SNPs. In the vicinity of these lead SNPs, 56 differentially expressed genes in various tissues/development stages were identified as candidate genes. This compendium connecting trait-SNP-genes may allow further prioritization of genes in GWAS loci to enable mechanistic studies. These identified quantitative trait nucleotides (QTNs) may prove helpful in fiber quality improvement in Asian cotton through marker-assisted breeding as well as in reviving eroded genetic factors of G. hirsutum via introgression breeding.

SUBMITTER: Iqbal MS

PROVIDER: S-EPMC8689003 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundAsian cotton (Gossypium arboreum L.), as a precious germplasm resource of cotton with insect resistance and stress tolerance, possesses a broad spectrum of phenotypic variation related to pigmentation. Flower color affects insect pollination and the ornamental value of plants. Studying flower color of Asian cotton varieties improves the rate of hybridization and thus enriches the diversity of germplasm resources. Meanwhile, it also impacts the development of the horticultural industry. Unfortunately, there is a clear lack of studies concerning intricate mechanisms of cotton flower-color differentiation. Hereby, we report an integrative approach utilizing transcriptome and metabolome concerning flower color variation in three Gossypium arboreum cultivars.ResultsA total of 215 differentially accumulated metabolites (DAMs) were identified, including 83 differentially accumulated flavonoids (DAFs). Colorless kaempferol was more abundant in white flowers, while gossypetin-fer showed specificity in white flowers. Quercetin and gossypetin were the main contributors to yellow petal formation. Pelargonidin 3-O-beta-D-glucoside and cyanidin-3-O-(6''-Malonylglucoside) showed high accumulation levels in purple petals. Quercetin and gossypetin pigments also promoted purple flower coloration. Moreover, 8178 differentially expressed genes (DEGs) were identified by RNA sequencing. The correlation results between total anthocyanins and DEGs were explored, indicating that 10 key structural genes and 29 transcription factors promoted anthocyanin biosynthesis and could be candidates for anthocyanin accumulation. Ultimately, we constructed co-expression networks of key DAFs and DEGs and demonstrated the interactions between specific metabolites and transcripts in different color flowers.ConclusionThis study provides new insights into elucidating the regulatory mechanisms of cotton flower color and lays a potential foundation for generate cotton varieties with highly attractive flowers for pollinators.

Dataset Information

Genetic Factors Underlying Single Fiber Quality in A-Genome Donor Asian Cotton (Gossypium arboreum).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets