ArticleNature communications2019
Biological relevance of computationally predicted pathogenicity of noncoding variants.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- The performance of genetic-constraint metrics varies significantly across the human noncoding genome.bioRxiv : the preprint server for biology · 2026Article
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Stratifying variant deleteriousness and trait-modulating effect under human recent adaptation using the FIND model.Genome biology · 2025Article
- Comprehensive molecular impact mapping of common and rare variants at GWAS loci.bioRxiv : the preprint server for biology · 2025Article
- MIRACN: a residual convolutional neural network for predicting cell line specific functional regulatory variants.Briefings in bioinformatics · 2025Article
- Deep learning-based assessment of missense variants in theBMJ open ophthalmology · 2025Article
- A cis-regulatory element regulates ERAP2 expression through autoimmune disease risk SNPs.Cell genomics · 2024Article
- Systematic characterization of regulatory variants of blood pressure genes.Cell genomics · 2023Article
- Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants.Genomics, proteomics & bioinformatics · 2023Article
- RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants.Genomics, proteomics & bioinformatics · 2023Article
- Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.Nucleic acids research · 2023Article
- Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy.Handbook of experimental pharmacology · 2023Article
- DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.Bioinformatics (Oxford, England) · 2022Article
- Exploration of Tools for the Interpretation of Human Non-Coding Variants.International journal of molecular sciences · 2022Review
- Multiplexed functional genomic assays to decipher the noncoding genome.Human molecular genetics · 2022Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- MiRLog and dbmiR: Prioritization and functional annotation tools to study human microRNA sequence variants.Human mutation · 2022Article
- Massively parallel identification of functionally consequential noncoding genetic variants in undiagnosed rare disease patients.Scientific reports · 2022Article
- WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.Briefings in bioinformatics · 2021Article
- Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Computational prediction of the phenotypic propensities of noncoding single nucleotide variants typically combines annotation of genomic, functional and evolutionary attributes into a single score. Here, we evaluate if the claimed excellent accuracies of these predictions translate into high rates of success in addressing questions important in biological research, such as fine mapping causal variants, distinguishing pathogenic allele(s) at a given position, and prioritizing variants for genetic risk assessment. A significant disconnect is found to exist between the statistical modelling and biological performance of predictive approaches. We discuss fundamental reasons underlying these deficiencies and suggest that future improvements of computational predictions need to address confounding of allelic, positional and regional effects as well as imbalance of the proportion of true positive variants in candidate lists.
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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 OpenQuestion graph.