ArticlePLoS computational biology2020
Improving the coverage of credible sets in Bayesian genetic fine-mapping.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- 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
- Meta-Analysis and Multivariate GWAS Analyses in 80,950 Individuals of African Ancestry Identify Novel Variants Associated with Blood Pressure Traits.International journal of molecular sciences · 2023Pooled it
- Novel genes and sex differences in COVID-19 severity.Human molecular genetics · 2022Pooled it
- Polygenic Contribution to Sensorineural Hearing Loss Implicates Novel Risk Loci and Convergence with Congenital Hearing Loss Genes.Journal of the Association for Research in Otolaryngology : JARO · 2026Article
- Enhanced genetic fine mapping accuracy with Bayesian Linear Regression models in diverse genetic architectures.PLoS genetics · 2025Article
- Whole-genome sequencing study in Koreans identifies novel loci for Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- A genome-wide association study of adults with community-acquired pneumonia.Respiratory research · 2024Article
- BEATRICE: Bayesian fine-mapping from summary data using deep variational inference.Bioinformatics (Oxford, England) · 2024Article
- BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference.bioRxiv : the preprint server for biology · 2024Article
- Functional Genomics in Psoriasis.International journal of molecular sciences · 2024Review
- Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision.NAR genomics and bioinformatics · 2023Article
- Genetic architecture of heart failure with preserved versus reduced ejection fraction.Nature communications · 2022Article
- Fine-mapping from summary data with the "Sum of Single Effects" model.PLoS genetics · 2022Article
- TSABL: Trait Specific Annotation Based Locus predictor.BMC genomics · 2022Article
- A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation.Nature genetics · 2022Article
- Genetics of osteoarthritis.Osteoarthritis and cartilage · 2022Review
- Methods for statistical fine-mapping and their applications to auto-immune diseases.Seminars in immunopathology · 2022Review
- Genome-wide association study identifies five risk loci for pernicious anemia.Nature communications · 2021Article
- Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs.Nature communications · 2021Article
- Beyond association: successes and challenges in linking non-coding genetic variation to functional consequences that modulate Alzheimer's disease risk.Molecular neurodegeneration · 2021Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Genome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some "coverage probability". Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to re-estimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the "adjusted coverage estimate". This is extended to find "adjusted credible sets", which are the smallest set of variants such that their adjusted coverage estimate meets the target coverage. We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95% or 99% credible sets required the inclusion of more variants-a pattern matched in simulations of well powered GWAS. Crucially, our method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases.
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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.