MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies

MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies

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...

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Bibliographic Details
Main Authors: Han Wang, Xiang Li, Teng Li, Zhe Li, Pak Chung Sham, Yan Dora Zhang
Format: Article
Language:English
Published: BMC 2025-02-01
Series:Genome Biology
Subjects:
Transcriptome-wide association studies (TWAS)
Functional annotation
Product partition model with covariates (PPMx)
Psychiatric traits
Online Access:https://doi.org/10.1186/s13059-025-03485-x
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Summary: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.
ISSN:1474-760X

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