ArticlePLoS computational biology2020
DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Interpreting artificial neural networks to detect genome-wide association signals for complex traits.NAR genomics and bioinformatics · 2026Article
- Personalizing Antidepressant Therapy: Integrating Pharmacogenomics, Therapeutic Drug Monitoring, and Digital Tools for Improved Depression Outcomes.Journal of personalized medicine · 2025Review
- MethAgingDB: a comprehensive DNA methylation database for aging biology.Scientific data · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Weighted 2D-kernel density estimations provide a new probabilistic measure for epigenetic age.Genome biology · 2025Article
- Applying traditional and machine learning-based GWAS approaches for marker-trait identification in wheat.Frontiers in plant science · 2025Article
- Biological aging analysis based on DNA methylation status for social anxiety disorder.Neuropsychopharmacology reports · 2024Article
- Learning genotype-phenotype associations from gaps in multi-species sequence alignments.Briefings in bioinformatics · 2024Article
- Applicability of epigenetic age models to next-generation methylation arrays.Genome medicine · 2024Article
- Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.JMIR nursing · 2024Article
- Cellotype-phenotype associations using 'organoid villages'.Trends in endocrinology and metabolism: TEM · 2024Review
- Genetic factors associated with suicidal behaviors and alcohol use disorders in an American Indian population.Molecular psychiatry · 2024Article
- Article
- Review
- Artificial intelligence for dementia genetics and omics.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.Genome medicine · 2023Article
- CVD-associated SNPs with regulatory potential reveal novel non-coding disease genes.Human genomics · 2023Article
- Genetic Factors Associated with Suicidal Behaviors and Alcohol Use Disorders in an American Indian Population.Research square · 2023Article
- Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank.Genetic epidemiology · 2023Article
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
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Authors and funding
28 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.
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