ArticlePLoS genetics2021
Identifying causal variants by fine mapping across multiple studies.
Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 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
54 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Integrated genome-wide association studies, meta-analysis, and Bayesian fine mapping reveal novel quantitative trait loci's and functional candidate genes for vulva traits in large white pigs.Journal of animal science · 2025Pooled it
- Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals.Nature medicine · 2023Pooled it
- Integrative post-GWAS analysis prioritizes immune regulatory pathways and candidate effector signals in systemic lupus erythematosus.Journal of translational autoimmunity · 2026Article
- Multi-ancestry colocalization approaches.PLoS genetics · 2026Article
- Ancestral diversity in complex disease genetics: from discovery to translation.Nature reviews. Genetics · 2026Review
- mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits.PLoS genetics · 2026Article
- Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping.Nature communications · 2026Article
- Article
- Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model.Nature genetics · 2026Article
- MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data.Nature genetics · 2026Article
- Towards improved fine-mapping of candidate causal variants.Nature reviews. Genetics · 2025Review
- Characterization of shared and ancestry-specific signals driving complex traits using multi-ancestry fine-mapping.medRxiv : the preprint server for health sciences · 2025Article
- Logica: A likelihood framework for cross-ancestry local genetic correlation estimation using summary statistics.American journal of human genetics · 2025Article
- Improved multiancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.Nature genetics · 2025Article
- Trustworthy causal biomarker discovery: a multiomics brain imaging genetics-based approach.Bioinformatics (Oxford, England) · 2025Article
- Fine-mapping in admixed populations using CARMA-X, with applications to Latin American studies.American journal of human genetics · 2025Article
- Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele.Nature communications · 2025Article
- Fine-scale population structure and widespread conservation of genetic effect sizes between human groups across traits.Nature genetics · 2025Article
- Research Advance of Causal Inference in Clinical Medicine: A Bibliometrics Analysis via Citespace.Journal of multidisciplinary healthcare · 2025Review
- BEATRICE: Bayesian fine-mapping from summary data using deep variational inference.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of "fine mapping" methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).
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.