Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

ABSTRACT: Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three v

SUBMITTER: Hudaiberdiev S 

PROVIDER: S-EPMC10469333 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature