Archives of Current Research International

Wheat (Triticum aestivum L.) plays a central role in global food and nutritional security, but its productivity is challenged by biotic and abiotic stresses. Its large, polyploid genome and extensive repetitive sequences complicate the identification of genetic variation and the dissection of complex agronomic traits. This review examines emerging genomic resources and the complementary contributions of transcriptomics, proteomics, metabolomics and epigenomics to wheat improvement. High-throughput genotyping platforms, including SNP arrays, genotyping-by-sequencing and DArTseq, facilitate genetic diversity analysis and the identification of genomic regions associated with agronomic traits. Pan-genome approaches extend analyses beyond a single reference assembly by capturing structural, copy-number and presence–absence variation. Functional omics studies provide information on transcriptional regulation, protein responses, metabolite accumulation and chromatin-associated mechanisms involved in development and stress adaptation. The review also considers the use of artificial intelligence and machine learning for integrating complex datasets, predicting yield-related traits, automating phenotypic assessment and supporting genomic selection. Examples from wheat demonstrate the potential value of combining molecular information with computational approaches, although the reliability of predictions depends on data quality, representation and appropriate validation. Together, these advances provide complementary tools for identifying candidate genes and interpreting genotype–phenotype relationships. Their integration with reliable phenotypic information and established breeding practices may support more efficient wheat improvement while acknowledging the technical and biological challenges of translating molecular findings into breeding outcomes.