mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits
Haihan Zhang, Kevin He, Lam C. Tsoi, Xiang Zhou, Lin Chen, Lin Chen, Lin Chen, Lin Chen · 2026
Read mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits by Haihan Zhang, Kevin He, Lam C. Tsoi, Xiang Zhou, Lin Chen, Lin Chen, Lin Chen, Lin Chen (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13225670.txt.
Description
Haihan Zhang and colleagues introduce a 2026 statistical method, mFABIO, for pinpointing genes that likely cause disease rather than merely correlate with it. The approach builds on transcriptome-wide association studies, which link predicted gene activity to illness. Earlier fine-mapping tools handled continuous traits and single tissues, leaving yes-or-no diseases and multi-tissue data poorly served.
Using expression data from thirty-eight tissues, the method was applied to six diseases in the UK Biobank, including asthma, gout, and rheumatoid arthritis. It nominated dozens of likely causal genes per disease, plus gene-tissue pairs showing where each gene acts. Most nominations matched existing evidence, and candidate lists shrank by roughly half compared with single-tissue methods.
Catalog record
| CREATOR | Haihan Zhang, Kevin He, Lam C. Tsoi, Xiang Zhou, Lin Chen, Lin Chen, Lin Chen, Lin Chen |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 110 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |