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

ABSTRACT: Deep learning sequence to function models can predict the molecular effects of genetic variants, but their predictions are limited to the cell types and assays they are trained on. Here we describe DNACipher, 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

SUBMITTER: Balderson B 

PROVIDER: S-EPMC12157646 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature