ArticleeLife2020
Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.Nature genetics · 2022Pooled it
- Bridging the variant-to-function gap in type 2 diabetes: advances and challenges.Diabetologia · 2026Review
- 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
- From omics to AI-mapping the pathogenic pathways in type 2 diabetes.FEBS letters · 2025Review
- Comprehensive molecular impact mapping of common and rare variants at GWAS loci.bioRxiv : the preprint server for biology · 2025Article
- Neur-Ally: a deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders.NAR genomics and bioinformatics · 2025Article
- An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem.International journal of molecular sciences · 2024Article
- Predicting cell population-specific gene expression from genomic sequence.Frontiers in bioinformatics · 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
- Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.Molecular systems biology · 2023Article
- Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction.Biology · 2023Article
- Building integrative functional maps of gene regulation.Human molecular genetics · 2022Article
- Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.PLoS computational biology · 2022Article
- Genomics enters the deep learning era.PeerJ · 2022Review
- Gaining insight into metabolic diseases from human genetic discoveries.Trends in genetics : TIG · 2021Review
- Functional characterization of T2D-associated SNP effects on baseline and ER stress-responsive β cell transcriptional activation.Nature communications · 2021Article
- Genetic Variants in Transcription Factor Binding Sites in Humans: Triggered by Natural Selection and Triggers of Diseases.International journal of molecular sciences · 2021Review
- Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.Nucleic acids research · 2021Article
- Genome annotation across species using deep convolutional neural networks.PeerJ. Computer science · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue - pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization - genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.