ArticleAmerican journal of human genetics2018
FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications.
Article in American journal of human genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 4 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 4 syntheses or guidelines pooled it, 85 citations in OpenAlex.
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- Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets.Nature communications · 2021Pooled it
- The genetic architecture of membranous nephropathy and its potential to improve non-invasive diagnosis.Nature communications · 2020Pooled it
- Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood.Nature communications · 2018Pooled it
- The polygenic architecture of hidradenitis suppurativa reveals signaling mechanisms that implicate epithelial remodeling.medRxiv : the preprint server for health sciences · 2025Article
- ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types.Genome biology · 2025Article
- Polygenic scores and their applications in kidney disease.Nature reviews. Nephrology · 2025Review
- Genome-wide studies define new genetic mechanisms of IgA vasculitis.medRxiv : the preprint server for health sciences · 2024Article
- Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility.HGG advances · 2024Article
- Modeling tissue co-regulation estimates tissue-specific contributions to disease.Nature genetics · 2023Article
- Genome-wide association analyses define pathogenic signaling pathways and prioritize drug targets for IgA nephropathy.Nature genetics · 2023Article
- Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy.Handbook of experimental pharmacology · 2023Article
- Imputation method for single-cell RNA-seq data using neural topic model.GigaScience · 2022Article
- Deep learning-assisted genome-wide characterization of massively parallel reporter assays.Nucleic acids research · 2022Article
- Exploration of Tools for the Interpretation of Human Non-Coding Variants.International journal of molecular sciences · 2022Review
- Genetic variants associated with psychiatric disorders are enriched at epigenetically active sites in lymphoid cells.Nature communications · 2022Article
- TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.Bioinformatics (Oxford, England) · 2022Article
- SNPs at SMG7 Associated with Time from Biochemical Recurrence to Prostate Cancer Death.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
- A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.American journal of human genetics · 2022Article
- Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome.Nucleic acids research · 2022Article
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell-type- and/or tissue-specific way (FUN-LDA). Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome-wide association studies and (2) estimating a tissue-based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN-LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN-LDA with state-of-the-art functional annotation methods and show that FUN-LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue- and cell-type-specific functional prediction methods tend to have substantially better prediction accuracy than organism-level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources).
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