ArticleNature communications2024
SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Transcriptome-wide association analysis of Alzheimer's disease: construction and clinical validation of transcriptomic risk scores.European archives of psychiatry and clinical neuroscience · 2026Article
- Exploring the Neuroprotective Effects of Walnut (International journal of molecular sciences · 2026Article
- mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits.PLoS genetics · 2026Article
- Transcriptome-wide association studies at cell-state level using single-cell eQTL data.Cell genomics · 2026Article
- ASTWAS: modeling alternative polyadenylation and SNP effects in kernel-driven TWAS reveal novel genetic associations for complex traits.Briefings in bioinformatics · 2026Article
- TWAS atlas 2.0: an updated data resource for transcriptome-wide association studies.Nucleic acids research · 2026Article
- Co-expression-wide association studies link genetically regulated interactions with complex traits.Nature communications · 2025Article
- TransferTWAS: A transfer learning framework for cross-tissue transcriptome-wide association study.American journal of human genetics · 2025Article
- The integration of genome-wide and transcriptome-wide association studies in neurodegenerative diseases: opportunities, challenges, and current methodological innovations.Briefings in bioinformatics · 2025Review
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10 authors.
Funding
Abstract
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.
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