Developing climate-resilient and high-quality cotton cultivars remains an urgent challenge, as the key target traits yield, fibre properties, and stress tolerance are highly polygenic and strongly influenced by genotype–environment interactions. Recent advances in chromosome-scale genome assemblies, pan-genomics, and haplotype-resolved resequencing have greatly enhanced the capacity to identify causal variants and recover non-reference alleles linked to fibre development and environmental adaptation. Parallel progress in functional genomics and precision genome editing, particularly CRISPR/Cas, base editing, and prime editing, now enables rapid, heritable modification of candidate loci across the complex tetraploid cotton genome. When integrated with high-throughput phenotyping, genomic selection, and machine learning, these approaches support predictive ideotype design rather than empirical, trial-and-error breeding. Emerging digital agriculture tools, such as digital twins that combine genomic, phenomic, and environmental data layers, allow simulation of ideotype performance and optimisation of trait combinations in silico before field validation. Speed breeding and phenomic selection further shorten generation time and increase selection intensity, bridging the gap between laboratory discovery and field deployment. However, the large-scale implementation of these technologies faces several practical constraints, including high infrastructural costs, limited accessibility for resource-constrained breeding programmes in developing regions, and uneven regulatory acceptance of genome-edited crops. However, reliance on highly targeted genome editing may inadvertently narrow allelic diversity, underscoring the need to integrate these tools with broad germplasm resources and pangenomic insights to sustain long-term adaptability. To realise these opportunities at scale, standardised data frameworks, interoperable phenotyping systems, robust multi-omic integration, and globally harmonised, science-based regulatory pathways are essential. This review synthesises recent progress, highlights case studies in fibre, oil, and stress-resilience engineering, and outlines a roadmap for translating integrative genomics into climate-smart, high-yield cotton breeding programmes.

Background: Heterosis is a complex phenomenon wherein the hybrids outperform their parents. Understanding the underlying molecular mechanism by which hybridization leads to higher yields in allopolyploid cotton is critical for effective breeding programs. Here, we integrated DNA methylation, transcriptomes, and small RNA profiles to comprehend the genetic and molecular basis of heterosis in allopolyploid cotton at three developmental stages. Results: Transcriptome analysis revealed that numerous DEGs responsive to phytohormones (auxin and salicylic acid) were drastically altered in F1 hybrid compared to the parental lines. DEGs involved in energy metabolism and plant growth were upregulated, whereas DEGs related to basal defense were downregulated. Differences in homoeologous gene expression in F1 hybrid were greatly reduced after hybridization, suggesting that higher levels of parental expression have a vital role in heterosis. Small RNAome and methylome studies showed that the degree of DNA methylation in hybrid is higher when compared to the parents. A substantial number of allele-specific expression genes were found to be strongly regulated by CG allele-specific methylation levels. The hybrid exhibited higher 24-nt-small RNA (siRNA) expression levels than the parents. The regions in the genome with increased levels of 24-nt-siRNA were chiefly related to genes and their flanking regulatory regions, demonstrating a possible effect of these molecules on gene expression. The transposable elements correlated with siRNA clusters in the F1 hybrid had higher methylation levels but lower expression levels, which suggest that these non-additively expressed siRNA clusters, reduced the activity of transposable elements through DNA methylation in the hybrid. Conclusions: These multi-omics data provide insights into how changes in epigenetic mechanisms and gene expression patterns can lead to heterosis in allopolyploid cotton. This makes heterosis a viable tool in cotton breeding.