ArticleGenetic epidemiology2020
Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification.
Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 7 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 7 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Brain transcriptome-wide association studies in diverse ancestral populations reveal genes implicated in an anxiety-related phenotype.G3 (Bethesda, Md.) · 2026Pooled it
- Variants in CALD1, ESRP1, and RBFOX1 are associated with orofacial cleft risk.PLoS genetics · 2025Pooled it
- Genome-wide association meta-analyses of drug-resistant epilepsy.EBioMedicine · 2025Pooled it
- Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk.Genome medicine · 2024Pooled it
- Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status.Breast cancer research : BCR · 2024Pooled it
- A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes.American journal of human genetics · 2023Pooled it
- Transcriptome-Wide Association Study for Inflammatory Bowel Disease Reveals Novel Candidate Susceptibility Genes in Specific Colon Subsites and Tissue Categories.Journal of Crohn's & colitis · 2022Pooled it
- Genetically Predicted Blood DNA Methylation Reveals Putative Regulatory Signals Associated with ALS Risk.medRxiv : the preprint server for health sciences · 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
- Multi-ancestry transcriptome-wide association study reveals shared and population-specific genetic effects in Alzheimer disease.American journal of human genetics · 2026Article
- Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies.American journal of human genetics · 2026Article
- Association between genetically predicted expression of TPMT and azathioprine adverse events.BMC pharmacology & toxicology · 2026Article
- Multi-ancestry Transcriptome-Wide Association Study Reveals Shared and Population-Specific Genetic Effects in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2025Article
- Article
- An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment.Cell genomics · 2025Article
- Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups.Genetic epidemiology · 2025Article
- Integrative Multi-Omics Approach for Improving Causal Gene Identification.Genetic epidemiology · 2025Article
- Association of Genetically Predicted Skipping of COL4A4 Exon 27 with Hematuria and Albuminuria.Journal of the American Society of Nephrology : JASN · 2025Article
- Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.Nature communications · 2024Article
- Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer.Journal of the National Cancer Institute · 2024Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
The integration of transcriptomic studies and genome-wide association studies (GWAS) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reliable causal gene discovery. With the goal of improving discoveries without increasing false positives, we develop and compare multiple transcriptomic imputation approaches using the most recent GTEx release of expression and splicing data on 17,382 RNA-sequencing samples from 948 post-mortem donors in 54 tissues. We find that informing prediction models with posterior causal probability from fine-mapping (dap-g) and borrowing information across tissues (mashr) can lead to better performance in terms of number and proportion of significant associations that are colocalized and the proportion of silver standard genes identified as indicated by precision-recall and receiver operating characteristic curves. All prediction models are made publicly available at predictdb.org.
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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.