Evolving computational paradigms for noncoding variant pathogenicity prediction

The rapid expansion of whole-genome sequencing (WGS) has highlighted the important contribution of noncoding variants to human disease, yet their pathogenic mechanisms remain difficult to resolve. Traditional statistical and experimental approaches often struggle to capture complex regulatory interactions or establish causal links, leaving many noncoding variants classified as variants of uncertain significance in clinical databases. Recent advances in computational modeling have substantially improved pathogenicity prediction by integrating genomic, epigenetic, and structural information. In parallel, genome language model (gLM)-inspired methods have enabled more context-aware interpretation of noncoding sequences and improved model generalization. This review summarizes current computational approaches, data modalities, and evaluation strategies for noncoding variant pathogenicity prediction, discusses key challenges in interpretability and data heterogeneity, and highlights emerging opportunities for clinical translation.