Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart.

ABSTRACT: Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal common and rare non-coding variants in human disease, and understanding how selective pressures have shaped the non-coding genome, remains a significant challenge. Here, we predicted the effects of 15 million variants with deep learning models trained on single-cell ATAC-seq across 132 cellular contexts in adult and fetal brain and heart, producing nearly two billion context-specific predictions. Using these predictions, we distinguish candidate causal variants underlying human traits and diseases and their context-specific effects. While common variant effects are more cell-type-specific, ra

SUBMITTER: Marderstein AR 

PROVIDER: S-EPMC11870466 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature