ArticleNature communications2024
Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.
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 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Beyond the Classics: The Synergy of AI and Genomics Reveals an Expanded Repertoire of Pigmentation Genes.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2026Review
- Genetic Evidence for Unified Airway Disease: Shared Epithelial and Immune Architecture Across Major Airway Diseases.International journal of molecular sciences · 2026Article
- Integrating bulk and single-cell RNA sequencing with GWAS reveals regulatory networks underpinning complex traits in beef cattle.Journal of animal science and biotechnology · 2026Article
- Cross-Tissue Transcriptome-Wide Association Study and TWAS-FOCUS Fine-Mapping Reveal Novel Susceptibility Genes for Primary Open-Angle Glaucoma.Translational vision science & technology · 2026Article
- Review
- The integration of genome-wide and transcriptome-wide association studies in neurodegenerative diseases: opportunities, challenges, and current methodological innovations.Briefings in bioinformatics · 2025Review
- A Multi-omic study integrating plasma protein, multiple tissues, and single-cell identifies RNASET2 as a key gene for lung cancer.Discover oncology · 2025Article
- Cross-Tissue Regulatory Network Analyses Reveal Novel Susceptibility Genes and Potential Mechanisms for Endometriosis.Biology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.
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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.