ArticleNucleic acids research2021
Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 22 citations in OpenAlex.
- UnlockingNAR genomics and bioinformatics · 2026Article
- Cross-species prediction reveals chromatin regions with increased accessibility in humans.Science advances · 2026Article
- Cross-species prediction of histone modifications in plants via deep learning.Genome biology · 2026Article
- 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
- Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.Briefings in bioinformatics · 2024Article
- Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.Journal of Alzheimer's disease : JAD · 2024Article
- deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues.Genomics, proteomics & bioinformatics · 2023Article
- Exploration of Tools for the Interpretation of Human Non-Coding Variants.International journal of molecular sciences · 2022Review
- Building integrative functional maps of gene regulation.Human molecular genetics · 2022Article
- TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.Bioinformatics (Oxford, England) · 2022Article
- Designing optimal convolutional neural network architecture using differential evolution algorithm.Patterns (New York, N.Y.) · 2022Article
- Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases.Frontiers in aging neuroscience · 2022Review
- DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner.Nucleic acids research · 2021Article
- Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases.International journal of molecular sciences · 2021Review
- A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data.Frontiers in genetics · 2021Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.
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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 Socratic graph.