ReviewHuman molecular genetics2020
Fine-mapping genetic associations.
Review in Human molecular genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Identification of risk loci for postpartum depression in a genome-wide association study.Psychiatry and clinical neurosciences · 2024Pooled it
- Serum 25-Hydroxyvitamin D and Cancer Risk: A Systematic Review of Mendelian Randomization Studies.Nutrients · 2023Pooled it
- Multi-tissue analyses of allele-specific chromatin accessibility nominate likely functional variants for type 2 diabetes.medRxiv : the preprint server for health sciences · 2026Article
- Buffering of gene dosage response curves for human complex traits.Cell genomics · 2026Article
- Multi-ancestry modeling improves fine-mapping resolution, protein prediction, and discovery for proteome-wide association studies.medRxiv : the preprint server for health sciences · 2026Article
- Integrative analyses elucidate transcriptional regulatory functions of risk alleles for metabolic liver disease.Nature genetics · 2026Article
- Integrating genetic data with biological insight: A practical guide to cis-Mendelian randomization.American journal of human genetics · 2026Review
- Early and late RNA eQTL are driven by different genetic mechanisms.Nature communications · 2026Article
- Personality Genomics.Annual review of psychology · 2026Review
- Towards improved fine-mapping of candidate causal variants.Nature reviews. Genetics · 2025Review
- Integrative functional genomics reveals transcriptional regulatory function of risk alleles for metabolic liver disease.Research square · 2025Article
- fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles.bioRxiv : the preprint server for biology · 2025Article
- Enhanced genetic fine mapping accuracy with Bayesian Linear Regression models in diverse genetic architectures.PLoS genetics · 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
- BAYESIAN VARIABLE SELECTION IN A COX PROPORTIONAL HAZARDS MODEL WITH THE "SUM OF SINGLE EFFECTS" PRIOR.ArXiv · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Untangling the genetics of beta cell dysfunction and death in type 1 diabetes.Molecular metabolism · 2024Review
- Functional Genomics in Psoriasis.International journal of molecular sciences · 2024Review
- PancanQTLv2.0: a comprehensive resource for expression quantitative trait loci across human cancers.Nucleic acids research · 2024Article
- SABER: Statistical Identification of Loci of Interest in GWAS Summary Statistics using a Bayesian Gaussian Mixture Model.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 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
3 authors.
Funding
Abstract
Whilst thousands of genetic variants have been associated with human traits, identifying the subset of those variants that are causal requires a further 'fine-mapping' step. We review the basic fine-mapping approach, which is computationally fast and requires only summary data, but depends on an assumption of a single causal variant per associated region which is recognized as biologically unrealistic. We discuss different ways that the approach has been built upon to accommodate multiple causal variants in a region and to incorporate additional layers of functional annotation data. We further review methods for simultaneous fine-mapping of multiple datasets, either exploiting different linkage disequilibrium (LD) structures across ancestries or borrowing information between distinct but related traits. Finally, we look to the future and the opportunities that will be offered by increasingly accurate maps of causal variants for a multitude of human traits.
Indexed as
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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.