ArticleProceedings of the National Academy of Sciences of the United States of America2023
Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization.bioRxiv : the preprint server for biology · 2026Article
- Regulatory risk loci link disrupted androgen response to the pathophysiology of polycystic ovary syndrome.Journal of the Endocrine Society · 2026Article
- Silencer variants are key drivers of gene up-regulation in Alzheimer's disease.Science advances · 2026Article
- From Human to Mouse and Back Again: Genetic and Genomic Ta(i)les of Islet Dysfunction in Type 2 Diabetes.Annual review of genetics · 2025Review
- Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants.Genes · 2025Article
- Regulatory Plasticity of the Human Genome.Molecular biology and evolution · 2025Article
- Abundant repressor binding sites in human enhancers are associated with the fine-tuning of gene regulation.iScience · 2025Article
- The contribution of silencer variants to human diseases.Genome biology · 2024Article
- Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- ChromDL: a next-generation regulatory DNA classifier.Bioinformatics (Oxford, England) · 2023Article
- ChromDL: A Next-Generation Regulatory DNA Classifier.bioRxiv : the preprint server for biology · 2023Article
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16 authors.
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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 variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.
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