ArticleGenome biology2025
MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Transcriptome-wide association study (TWAS) has emerged as a powerful tool for translating the myriad variations identified by genome-wide association studies (GWAS) into regulated genes in the post-GWAS era. While integrating annotation information has been shown to enhance power, current annotation-assisted TWAS tools predominantly focus on epigenomic annotations. When including more annotations, the assumption of a positive correlation between annotation scores and SNPs' effect sizes, as adopted by current methods, often falls short. Here, we propose MAAT expanding the horizons of existing TWAS studies, generating a new model incorporating multiple annotations into TWAS and a new metric indicating the most important annotation.
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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 Socratic graph.