Comprehensive molecular impact mapping of common and rare variants at GWAS loci

作者：Brad Balderson, Sanjana Tule, Mei-Lin Okino, William JF Rieger, Sierra Corban, Jeff Jaureguy, Nathan J. Palpant, Kyle J. Gaulton, Mikael Bodén, Graham McVicker · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2025 · DOI：10.1101/2025.06.05.658079 · 被引用次数：2 · 研究领域：Bioinformatics and Genomic Networks、RNA and protein synthesis mechanisms、Gene expression and cancer classification

, a deep learning model that predicts the effects of genetic variants across diverse biological contexts-including those not directly measured. DNACipher takes 196 kb of genome sequences as input and imputes variant effects across 38,582 cell type-assay combinations. DNACipher generates predictions for >7 times as many contexts as Enformer, which allows for better detection of variant effects at expression quantitative trait loci (eQTLs). We also introduce DNACipher Deep Variant Impact Mapping (DVIM), a method to identify variants with molecular effects at genome-wide association study (GWAS) loci. Application of DVIM to type 1 diabetes (T1D) reduced the mean fine-mapping credible set size from 24 to 1.4 variants per signal. DVIM variants had significantly higher fine-mapping posterior probabilities, and their predicted effects were supported by single-nucleus ATAC-seq and luciferase assays. DVIM also detected 6547 rare variants with molecular effects at 96% of GWAS T1D loci, and these were enriched for associations with immune traits. In summary, DNACipher DVIM prioritises common and rare variants at GWAS loci by predicting molecular effects across a broad range of contexts.