Artificial Intelligence-Powered Discovery of Regulatory Variants Across Human and Model Organism Genomes
DOI:
https://doi.org/10.4238/95mf6852Abstract
Regulatory variants, genomic positions differing across species or strains within regulatory elements, play a crucial role in modulating transcription factor binding, chromatin accessibility, and gene expression. These noncoding variants are increasingly recognized as contributors to human disease and complex traits, with a substantial portion of genome-wide association study (GWAS) hits residing in regulatory regions. Identifying regulatory variants at single-nucleotide resolution remains challenging but essential for understanding epigenetic regulation and functional genomics. Model organisms, such as mouse, zebrafish, Drosophila, and yeast, offer valuable insights into the conservation and divergence of regulatory mechanisms, enabling cross-species discovery of candidate regulatory variants. Advances in artificial intelligence and large-scale genomic datasets, including ENCODE, FANTOM, and GTEx, facilitate systematic annotation, prediction, and functional interpretation of regulatory variants across human and model organism genomes. This integrative approach holds promise for uncovering causal variants that contribute to complex traits and diseases
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Copyright (c) 2026 Nodira Alibekova, Mekhriban Rizayeva, Shakhnoza Kimsanbaeva, Toshpulat Nazarov, Farhad Akilov, Nuriddin Abduqodirov, Tuktash Qurbonov (Author)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

