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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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03485-x |
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| author | Han Wang Xiang Li Teng Li Zhe Li Pak Chung Sham Yan Dora Zhang |
| author_facet | Han Wang Xiang Li Teng Li Zhe Li Pak Chung Sham Yan Dora Zhang |
| author_sort | Han Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d40046ccde794b1ea06fb59c5b290565 |
| institution | Kabale University |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-d40046ccde794b1ea06fb59c5b2905652025-02-09T12:39:24ZengBMCGenome Biology1474-760X2025-02-0126112810.1186/s13059-025-03485-xMAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studiesHan Wang0Xiang Li1Teng Li2Zhe Li3Pak Chung Sham4Yan Dora Zhang5College of Science, China Agricultural UniversityDepartment of Statistics and Actuarial Science, School of Computing and Data Science, The University of Hong KongDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College4+4 Medical Doctor Program, Chinese Academy of Medical Sciences and Peking Union Medical CollegeDepartment of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong KongDepartment of Statistics and Actuarial Science, School of Computing and Data Science, The University of Hong KongAbstract 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.https://doi.org/10.1186/s13059-025-03485-xTranscriptome-wide association studies (TWAS)Functional annotationProduct partition model with covariates (PPMx)Psychiatric traits |
| spellingShingle | Han Wang Xiang Li Teng Li Zhe Li Pak Chung Sham Yan Dora Zhang MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies Genome Biology Transcriptome-wide association studies (TWAS) Functional annotation Product partition model with covariates (PPMx) Psychiatric traits |
| title | MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies |
| title_full | MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies |
| title_fullStr | MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies |
| title_full_unstemmed | MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies |
| title_short | MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies |
| title_sort | maat a new nonparametric bayesian framework for incorporating multiple functional annotations in transcriptome wide association studies |
| topic | Transcriptome-wide association studies (TWAS) Functional annotation Product partition model with covariates (PPMx) Psychiatric traits |
| url | https://doi.org/10.1186/s13059-025-03485-x |
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