Associative Transcriptomics (AT) is an advanced integrative approach that synergizes Genome Wide Association Studies (GWAS) with transcriptome profiling to unravel the complex genetic architecture underlying phenotypic traits. Unlike conventional GWAS, which relies solely on DNA marker polymorphism such as Single Nucleotide Polymorphisms (SNPs), AT incorporates both SNPs and Gene Expression Markers (GEMs), thereby enhancing the resolution and sensitivity of trait discovery. This dual-marker strategy is particularly valuable in genetically complex or polyploid species, where linkage disequilibrium patterns and gene redundancy often obscure signals in traditional analyses. Over the past decade, AT has emerged as a powerful tool for identifying trait-associated loci in several agriculturally important crops, including Brassica species and Triticum aestivum. The increasing availability of high-throughput sequencing platforms, combined with advancements in machine learning and statistical modelling, has accelerated the ability to integrate transcriptomic and genomic data on a large scale. Recent methodological innovations include the use of pan-transcriptomic references, co-expression networks and multi-omics integration to refine trait mapping. Despite its potential, AT faces ongoing challenges, including the management of population structure, transcriptome complexity across tissues and developmental stages and the dynamic influence of environmental factors on gene expression. AT holds considerable promise in supporting precision breeding programs, enabling targeted genetic interventions and advancing the development of climate-resilient crop varieties.
