Cotton genetic mapping for plant biotechnology: from markers to graph pan-genomes and sustainable breeding

Cotton improvement remains a major challenge in plant biotechnology because stable yield, fiber quality, and stress resilience must be delivered from complex allopolyploid genomes under variable environmental conditions. Genetic mapping has progressively transformed this challenge into deployable breeding knowledge, advancing from sparse marker systems to high-density SNP arrays, reference genomes, and, more recently, structural-variation-aware pan-genomes and graph genomes. This review argues that cotton's genomic complexity, homoeolog redundancy, and strong genotype-by-environment interactions have driven methodological innovation rather than the simple transfer of approaches developed in other crops. It synthesizes advances in linkage mapping, GWAS, eQTL analysis, fine mapping, functional validation, and marker deployment, with emphasis on how these tools have enabled breeder-ready assays, marker-assisted selection, and emerging predictive breeding frameworks. It further examines how cotton mapping contributes to sustainable intensification by enhancing disease resistance, abiotic resilience, fiber value, and trait-stacking efficiency. Finally, this review highlights unresolved challenges in homoeolog-aware inference, structural variant genotyping, phenotyping throughput, and environment-aware prediction. It outlines a next-phase agenda in which graph genomes, scalable validation, and climate-informed models become central to cotton biotechnology.