FABIO: TWAS fine-mapping to prioritize causal genes for binary traits

Transcriptome-wide association studies (TWAS) have emerged as a powerful tool for identifying gene-trait associations by integrating gene expression mapping studies with genome-wide association studies (GWAS). 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 GATA3 for asthma, ABCG2 for gout, and SH2B3 for hypertension. Overall, FABIO represents an effective tool for TWAS fine-mapping of disease traits.Author summary: In our study, we developed a new method called FABIO, designed to improve the accuracy of identifying genes linked to diseases. Traditional methods typically analyze genes one at a time, which can miss important connections between genes. FABIO, however, looks at all genes on a chromosome together and focuses specifically on diseases that can be categorized in a ’yes or no’ manner, such as asthma or hypertension. By accounting for the relationships between genes, FABIO provides a more precise way to find those that may be contributing to the disease. After illustrating the benefits of FABIO through extensive simulations, we tested our method on real data: FABIO reduced the number of potential causal genes by 28–37%, making it easier to pinpoint key genes. For example, FABIO highlighted GATA3 as important for asthma and ABCG2 for gout. We believe FABIO will help researchers better understand the genetic basis of diseases and could eventually lead to more targeted treatments.