ArticleHuman genetics2020
A powerful fine-mapping method for transcriptome-wide association studies.
Article in Human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multiancestry Transcriptome-Wide Association Study Identifies Candidate Genes Associated with Hepatoblastoma.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.PLoS genetics · 2026Article
- FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.bioRxiv : the preprint server for biology · 2026Article
- Genetic architecture of primary sclerosing cholangitis: shared pathways with inflammatory bowel disease and gut-liver axis mediation.International journal of surgery (London, England) · 2026Article
- From GWAS to TWAS: multi-omics dissection of sepsis uncovers consistent impact ofArchives of medical science : AMS · 2026Article
- Knockoff procedure improves susceptibility gene identifications in conditional transcriptome-wide association studies.American journal of human genetics · 2025Article
- Multi-Ancestry Transcriptome-Wide Association Studies of Cognitive Function, White Matter Hyperintensity, and Alzheimer's Disease.International journal of molecular sciences · 2025Article
- Leveraging pleiotropy for the improved treatment of psychiatric disorders.Molecular psychiatry · 2025Review
- Integrative Multi-Omics Approach for Improving Causal Gene Identification.Genetic epidemiology · 2025Article
- Integrating transcriptomic and polygenic risk scores to enhance predictive accuracy for ischemic stroke subtypes.Human genetics · 2025Article
- FABIO: TWAS fine-mapping to prioritize causal genes for binary traits.PLoS genetics · 2024Article
- SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.Human molecular genetics · 2024Article
- Conditional transcriptome-wide association study for fine-mapping candidate causal genes.Nature genetics · 2024Article
- Article
- Transcriptome-wide association studies: recent advances in methods, applications and available databases.Communications biology · 2023Review
- Identifying causal genes for migraine by integrating the proteome and transcriptome.The journal of headache and pain · 2023Article
- Cross-trait prediction accuracy of summary statistics in genome-wide association studies.Biometrics · 2023Article
- Article
- Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data.Journal of the American Statistical Association · 2023Article
- Network regression analysis for binary and ordinal categorical phenotypes in transcriptome-wide association studies.Genetics · 2022Article
Corrections and comments
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2 authors.
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Abstract
Transcriptome-wide association studies (TWAS) have been recently applied to successfully identify many novel genes associated with complex traits. While appealing, TWAS tend to identify multiple significant genes per locus, and many of them may not be causal due to confounding through linkage disequilibrium (LD) among SNPs. Here we introduce a powerful fine-mapping method that prioritizes putative causal genes by accounting for local LD. We apply a weighted adaptive test with eQTL-derived weights to maintain high power across various scenarios. Through simulations, we show that our new approach yielded a well-controlled Type I error rate while achieving higher power and AUC than competing methods. We applied our approach to a schizophrenia GWAS summary dataset and successfully prioritized some well-known schizophrenia-related genes, such as C4A. Importantly, our approach identified some putative causal genes (e.g., B3GAT1 and RGS6) that were missed by competing methods and TWAS. Our results suggest that our approach is a useful tool to prioritize putative causal genes, gaining insights into the mechanisms of complex traits.
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