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While most existing TWAS approaches focus on marginal analyses through examining one gene at a time, recent developments in TWAS fine-mapping methods enable the joint modeling of multiple genes to refine the identification of potentially causal ones. However, these fine-mapping methods have primarily focused on modeling quantitative traits and examining local genomic regions, leading to potentially suboptimal performance. Here, we present FABIO, a TWAS fine-mapping method specifically designed for binary traits that is capable of modeling all genes jointly on an entire chromosome. FABIO employs a probit model to directly link the genetically regulated expression (GReX) of genes to binary outcomes while taking into account the GReX correlation among all genes residing on a chromosome. As a result, FABIO effectively controls false discoveries while offering substantial power gains over existing TWAS fine-mapping approaches. We performed extensive simulations to evaluate the performance of FABIO and applied it for in-depth analyses of six binary disease traits in the UK Biobank. In the real datasets, FABIO significantly reduced the size of the causal gene sets by 27.9%-36.9% over existing approaches across traits. Leveraging its improved power, FABIO successfully prioritized multiple potentially causal genes associated with the diseases, including <jats:italic>GATA3<\/jats:italic> for asthma, <jats:italic>ABCG2<\/jats:italic> for gout, and <jats:italic>SH2B3<\/jats:italic> for hypertension. Overall, FABIO represents an effective tool for TWAS fine-mapping of disease traits.<\/jats:p>","DOI":"10.1371\/journal.pgen.1011503","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T19:03:02Z","timestamp":1733166182000},"page":"e1011503","update-policy":"https:\/\/doi.org\/10.1371\/journal.pgen.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["FABIO: TWAS fine-mapping to prioritize causal genes for binary 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