ArticleProceedings of the National Academy of Sciences of the United States of America2022
Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Application of deep learning in cancer epigenetics through DNA methylation analysis.Briefings in bioinformatics · 2023Pooled it
- DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.PLoS computational biology · 2026Article
- Distinct cellular DNA methylation mechanisms underlie common and rare genetic risk for brain disorders.bioRxiv : the preprint server for biology · 2026Article
- Metabolic Mechanisms in Electroconvulsive Therapy for Schizophrenia: Role, Potential and Future Directions.International journal of molecular sciences · 2026Review
- Large-scale multi-omic biosequence transformers for modeling protein-nucleic acid interactions.PloS one · 2026Article
- SETD1A regulates psychiatric gene networks involved in genomic stability and synaptic function in rare and sporadic schizophrenia.Nature communications · 2025Article
- Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions.ArXiv · 2025Article
- Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.Cell genomics · 2025Article
- Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.medRxiv : the preprint server for health sciences · 2025Article
- Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.Science advances · 2025Article
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine.Journal of personalized medicine · 2024Review
- Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.Bioinformatics (Oxford, England) · 2024Article
- From tradition to innovation: conventional and deep learning frameworks in genome annotation.Briefings in bioinformatics · 2024Review
- DiseaseNet: a transfer learning approach to noncommunicable disease classification.BMC bioinformatics · 2024Article
- scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia.bioRxiv : the preprint server for biology · 2024Article
- Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.bioRxiv : the preprint server for biology · 2024Article
- MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation.Theory in biosciences = Theorie in den Biowissenschaften · 2023Article
- Deep Learning Methods for Omics Data Imputation.Biology · 2023Review
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.
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Registered trials
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