ArticleNucleic acids research2020
CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 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
62 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Genome-wide analyses identify 30 loci associated with obsessive-compulsive disorder.Nature genetics · 2025Pooled it
- Genome Wide Association Study (GWAS) Identifies Novel Genetic Loci for Second-Generation Antipsychotics (SGA)-Induced Metabolic Syndrome (MetS).Clinical and translational science · 2025Pooled it
- Transcriptomic risk scores for attention deficit/hyperactivity disorder.Molecular psychiatry · 2023Pooled it
- Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains.Nature genetics · 2023Pooled it
- Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits.Nature genetics · 2022Pooled it
- Trial
- Pan-cancer epigenetic landscape of human tumor-associated macrophages reveals crucial enhancers governing their heterogenous formation by Pol II pausing modulation.Journal of advanced research · 2026Article
- Multiple instance fine-mapping: Predicting causal regulatory variants with a deep sequence model.PLoS genetics · 2026Article
- Article
- baal-nf identifies motif-disrupting variants that decrease transcription factor binding affinity.Genome biology · 2026Article
- DisCP-Atlas: a comprehensive resource mapping cellular processes to complex diseases.Nucleic acids research · 2026Article
- Multidimensional mapping of stimulation-responsive regulatory elements and candidate causal variants in T cell activation.Science advances · 2026Article
- DisSNPNet: Predicting disease-associated single-nucleotide polymorphisms using linkage disequilibrium, disease similarity, and 1000 Genomes Project datasets with evidence-based validation.Biology methods & protocols · 2026Article
- SPAmix: a scalable, accurate, and universal analysis framework for large-scale genetic association studies in admixed populations.Genome biology · 2025Article
- Article
- Loop Catalog: a comprehensive HiChIP database of human and mouse samples.Genome biology · 2025Article
- Generative prediction of causal gene sets responsible for complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- A nuclear RNA degradation code is recognized by PAXT for eukaryotic transcriptome surveillance.Molecular cell · 2025Article
- TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data.Nature communications · 2025Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.
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.