ArticleNature genetics2017
Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data.
Article in Nature genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 226 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
226 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Dyslipidemia, inflammation, calcification, and adiposity in aortic stenosis: a genome-wide study.European heart journal · 2023Pooled it
- Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.Nature genetics · 2023Pooled it
- GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.Nature communications · 2022Pooled it
- Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium.Molecular psychiatry · 2019Pooled it
- Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.Current protocols in human genetics · 2019Pooled it
- Genomic analyses implicate noncoding de novo variants in congenital heart disease.Nature genetics · 2020Trial
- HiFIseek: Gene-specific enrichment of high-impact mutations in associated genomic regions.PLoS computational biology · 2026Article
- AWTI-Net Enables Accurate and Interpretable Functional Assessment of Disease-Associated LncRNA Mutations.Interdisciplinary sciences, computational life sciences · 2026Article
- DeNovoSeer:a deep learning framework for pathogenicity prediction of De novo mutations.Briefings in bioinformatics · 2026Article
- MetaSTAARlite: an all-in-one tool for biobank-scale whole-genome sequencing meta-analysis.Nature computational science · 2026Article
- Application of the STAAR framework in detecting rare variant associations with Alzheimer disease and related dementias: Insights and implications.HGG advances · 2026Article
- Article
- Scalable and accurate rare-variant association tests for whole genome sequencing time-to-event analysis in large biobanks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- Stratifying variant deleteriousness and trait-modulating effect under human recent adaptation using the FIND model.Genome biology · 2025Article
- Whole-genome Sequence Analysis Revealed Novel Subjective Cognitive Decline-associated Genes in 10,763 Chinese.Genomics, proteomics & bioinformatics · 2025Article
- A machine-learning framework to characterize functional disease architectures and prioritize disease variants.medRxiv : the preprint server for health sciences · 2025Article
- Antisense oligonucleotide depletion ofmedRxiv : the preprint server for health sciences · 2025Article
- varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction.Genome medicine · 2025Article
166 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
3 authors.
Funding
Abstract
Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the 'big data' available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.