DeepCAST-GWAS: Improving the Discovery of Genetic Associations Using Deep Learning-Based Regulatory SNP Prioritization

Abstract

Genome-wide association studies (GWAS) have uncovered numerous variants linked to complex traits, yet power remains limited by the large multiple testing burden and the inclusion of many variants with minimal regulatory impact. We present Deep learning-based Chromatin Accessibility SNP Targeting for GWAS (DeepCAST-GWAS), a framework that integrates functional annotations derived from deep learning models to improve both the yield and the reliability of GWAS findings. DeepCAST-GWAS uses SNP Activity Difference (SAD) scores from in silico mutagenesis with the Enformer model to estimate the predicted effect of each variant on chromatin accessibility across tissues, allowing statistical testing to focus on variants with stronger regulatory evidence. Using conservative family-wise error rate (FWER) control, DeepCAST-FWER produces fewer associations than existing power-boosting approaches, but the associations it reports replicate in larger cohort GWAS at substantially higher rates. For applications where discovery count is more important, DeepCAST-sFDR increases the number of genome-wide significant findings above baseline GWAS by using the Enformer SAD scores for stratified False Discovery Rate (sFDR) control. DeepCAST-sFDR achieves performance comparable to the strongest competing method, while maintaining reliability on par with a standard GWAS. Subsampling analyses across a wide range of traits confirm these improvements in both sensitivity and replicability. DeepCAST-GWAS offers a principled way to incorporate sequence-based regulatory predictions into population-scale association testing, demonstrating that chromatin accessibility activity scores can improve the stability of GWAS discoveries. The framework is made available at https://github.com/BoevaLab/DeepCAST-GWAS.