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Evolving computational paradigms for noncoding variant pathogenicity prediction
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DOI：10.3389/fmolb.2026.1761673.png)
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Abstract
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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.
Keywords:
clinical translation
computational modeling
pathogenicity prediction
noncoding variants
genome language models
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