Advancing cotton improvement: a review of functional gene mining through molecular markers, GWAS, and map-based cloning

Abstract The production of cotton ( Gossypium spp.) faces significant challenges, including stagnating yields, climate change, and both biotic and abiotic stresses, while conventional breeding remains time-consuming and inefficient. This review summarizes recent advances in functional gene mining using molecular markers, genome-wide association studies (GWAS), and map-based cloning. Map-based cloning has facilitated the identification of key genes that control petal color ( GaPC , GhTT19 ), brown fiber ( GhTT2-3A ), and fiber quality ( GH_D02G2269 , qFL-chr1 ). GWAS has revealed hundreds of loci linked to fiber yield, quality, and stress tolerance, including the identification of GhMYB_D13 for fiber length, GhBRH1_A12 for boll weight, and GhAMT2 for Verticillium wilt resistance. The combination of high-throughput genotyping with association mapping has accelerated marker-assisted selection and the introgression of superior alleles from wild germplasm. These technologies collectively address key limitations of traditional breeding and provide direct targets for genetic improvement. The future integration of multi-omics data with artificial intelligence (Breeding 5.0) promises to further revolutionize cotton breeding, enabling the development of high-yielding, climate-resilient varieties for a sustainable textile industry.