ArticleHGG advances2022
TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8.
Article in HGG advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Hippocampal transcriptome-wide association study and pathway analysis of mitochondrial solute carriers in Alzheimer's disease.Translational psychiatry · 2024Pooled it
- Integrating dorsolateral prefrontal cortex multi-omics and GWAS summary data reveals genetic etiology of Parkinson's disease.Research square · 2026Article
- Integrating dorsolateral prefrontal cortex multi-omics and GWAS summary data reveals genetic etiology of Parkinson's disease.medRxiv : the preprint server for health sciences · 2026Article
- Cell-type-aware transcriptome-wide association studies identify 91 independent risk genes for Alzheimer's disease dementia.Communications biology · 2026Article
- Review
- Transcriptome-wide association studies at cell-state level using single-cell eQTL data.Cell genomics · 2026Article
- Integrative transcriptome-wide association analyses reveal PRKCG-linked GABAergic dysfunction in Fragile X-associated tremor/ataxia syndrome.Nature communications · 2026Article
- Co-expression-wide association studies link genetically regulated interactions with complex traits.Nature communications · 2025Article
- CADET: Enhanced transcriptome-wide association analyses in admixed samples using eQTL summary data.American journal of human genetics · 2025Article
- Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes.Genome biology · 2025Article
- Article
- Identifying Alzheimer's disease-related pathways based on whole-genome sequencing data.Computational and structural biotechnology journal · 2025Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
- Genome-wide study of gene-by-sex interactions identifies risks for cleft palate.Human genetics · 2024Article
- Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia.American journal of human genetics · 2024Article
- SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.Nature communications · 2024Article
- Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia.American journal of human genetics · 2024Article
- Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer's disease dementia.Alzheimer's research & therapy · 2024Article
- Transcriptome-Wide Association Study Reveals Potentially Candidate Genes Responsible for Milk Production Traits in Buffalo.International journal of molecular sciences · 2024Article
- TWAS revealed significant causal loci for milk production and its composition in Murrah buffaloes.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian
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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.