From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target...

Genome-wide association studies (GWAS) have identified thousands of loci associated with complex human diseases. However, the majority of the association signals reside in non-coding regions of the genome, and do not directly reveal the causal variant, effector gene, regulatory biomolecule, cell type, pathway, biomarker, or therapeutic target. Because many disease-associated variants act through non-coding regulatory mechanisms, post-GWAS interpretation increasingly depends on fine-mapping, expression quantitative trait loci, transcriptome-wide association studies, and functional evidence from single-cell multi-omics, network biology, and genetic target prioritization. Artificial intelligence can attempt to integrate these heterogeneous molecular evidence layers, but the resulting black-box prediction is insufficient when outputs cannot be biologically reproduced or experimentally tested. This review evaluates explainable artificial intelligence (XAI) as a framework for linking genetic association signals to molecular mechanisms and causal gene hypotheses. We argue that explainability is best treated as a biological requirement because useful models must expose evidence paths from significant disease-associated variants to regulatory elements, genes, transcripts, proteins, pathways, cell states, and therapeutic hypotheses. By emphasizing transparent evidence provenance, ancestry-aware interpretation, and functional validation, XAI can support the translation of GWAS signals into molecularly testable hypotheses for target prioritization and precision molecular medicine. The review focuses on the question of how to accomplish AI-accelerated functionalization of GWAS outputs across complex human diseases and traits.