Pomegranate (Punica granatum L.) has been cultivated from ancient times for its economic, ornamental and medicinal properties globally. Here, we report the isolation and characterization of 12 polymorphic microsatellite markers from a repeat-enriched genomic library of Punica granatum L. The genetic diversity of these loci was assessed in 60 genotypes of Punica granatum L. All loci were variable: the number of polymorphic alleles per locus ranged from two to five (average 2.9). The observed and expected heterozygosities ranged from 0.15 to 0.87 and 0.29 to 0.65, respectively. The polymorphic information content ranged from 0.26 to 0.61 (average: 0.43). To the best of our knowledge, this is the first time that polymorphic microsatellite markers have been reported for P. granatum L. These new markers should allow studies of the population structure and genetic diversity of pomegranate to be performed in the future.

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.

Establishment of a core collection, of limited size and better representation of the whole germplasm phenotypic diversity, is fundamental for fruit tree breeding programs from an economic and management points of view. To achieve this goal with pomegranate fruit trees, 221 genotypes were evaluated for 25 morphological and pomological traits during two successive years. Using the maximization strategy in Power Core software, 12 out of 221 pomegranate genotypes were selected for the new core collection, reducing the population size to 5.42% of the entire collection. Variance difference (VD%) and mean difference (MD%) were calculated as 42.68% and 7.03% in core collection, respectively. This indicates an excellent diversity amongst genotypes within the core collection. The Shannon’s diversity index (H′) in the formed collection suggested that 19 out of 25 phenotypic variables were of high diversity. Results showed that core collection genotypes are equally presented in all three population groups formed by cluster analysis through the original collection. The current research is the first in using phenotypic data to establish a core collection of Iranian pomegranate germplasm. The formation of this core collection will be an effective step towards examining the diversity of the original population and breeding prospects.