Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Non-protein-coding genetic variants are key contributors to human disease risk.
• Identifying specific non-coding variants and their disease mechanisms remains a significant challenge.
• Existing in silico variant prioritization methods often lack disease-specific context.

Purpose of the Study:

• To develop and validate a disease-specific variant prioritization scheme.
• To improve the accuracy of identifying non-coding variants associated with human diseases.
• To create interpretable models linking genetic variants to diseases through specific biological mechanisms.

Main Methods:

• Combined tissue/cell-type-specific variant scores using logistic regression.
• Applied the approach to approximately 25,000 non-coding variants across 111 diseases.
• Compared disease-specific scores against organism-wide scores.

Main Results:

• Disease-specific variant prioritization significantly improved association with disease (average precision 0.151 vs. 0.129).
• Identified meaningful disease groups based on data-driven aggregation weights.
• Highlighted specific tissues and cell types driving disease similarities, complementing genetic correlation.

Conclusions:

• Disease-specific variant prioritization offers a powerful complementary strategy.
• The proposed method enhances non-coding variant prioritization accuracy.
• The approach provides interpretable links between variants, diseases, and specific cellular contexts.