ReviewSeminars in immunopathology2022
Methods for statistical fine-mapping and their applications to auto-immune diseases.
Review in Seminars in immunopathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Systematic benchmarking of multi-ancestry fine-mapping under variant heterogeneity and LD mismatch.Briefings in bioinformatics · 2026Article
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- Advancing risk gene discovery across the allele frequency spectrum.HGG advances · 2026Review
- Identifying drug targets for schizophrenia through gene prioritization.Translational psychiatry · 2026Article
- Polygenic Childhood Obesity: Integrating Genetics and Environment for Early Intervention.Hormone research in paediatrics · 2026Review
- Towards improved fine-mapping of candidate causal variants.Nature reviews. Genetics · 2025Review
- Article
- Whole-genome sequencing reveals rare and structural variants contributing to psoriasis and identifies CERCAM as a risk gene.Cell genomics · 2025Article
- Causal Variants in CHRNA1 and CHRNB1 Genes for Anti-acetylcholine Receptor Antibody Positive Myasthenia Gravis: Evidence from Bayesian Fine-Mapping and Genetic Association Study.Molecular neurobiology · 2025Article
- Variant-to-function approaches for adipose tissue: Insights into cardiometabolic disorders.Cell genomics · 2025Review
- The Great Genotyper: a graph-based method for population genotyping of small and structural variants.GigaScience · 2025Article
- Association between Cytotoxic T-Lymphocyte-Associated Antigen 4 (CTLA-4) Locus and Early-Onset Anti-acetylcholine Receptor-Positive Myasthenia Gravis in Serbian Patients.Molecular neurobiology · 2024Article
- Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology.Journal of human genetics · 2024Review
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Relationship between HLA genetic variations, COVID-19 vaccine antibody response, and risk of breakthrough outcomes.Nature communications · 2024Article
- Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision.NAR genomics and bioinformatics · 2023Article
- Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis.BMC medicine · 2023Article
- SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets.Bioinformatics (Oxford, England) · 2023Article
- Genetics and functional genetics of autoimmune diseases.Seminars in immunopathology · 2022Article
- 3D genome organization links non-coding disease-associated variants to genes.Frontiers in cell and developmental biology · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS signals by evaluating which variant(s) are truly causal to the phenotype. Here, we review the types of statistical fine-mapping methods that have been widely used to date, with a focus on recently developed functionally informed fine-mapping (FIFM) methods that utilize functional annotations. We then systematically review the applications of statistical fine-mapping in autoimmune disease studies to highlight the value of statistical fine-mapping in biological contexts.
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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.