Transcriptome-wide association studies (TWAS) have been recently applied to successfully identify many novel genes associated with complex traits. While appealing, TWAS tend to identify multiple significant genes per locus, and many of them may not be causal due to confounding through linkage disequilibrium (LD) among SNPs. Here we introduce a powerful fine-mapping method that prioritizes putative causal genes by accounting for local LD. We apply a weighted adaptive test with eQTL-derived weights to maintain high power across various scenarios. Through simulations, we show that our new approach yielded a well-controlled Type I error rate while achieving higher power and AUC than competing methods. We applied our approach to a schizophrenia GWAS summary dataset and successfully prioritized some well-known schizophrenia-related genes, such as C4A. Importantly, our approach identified some putative causal genes (e.g., B3GAT1 and RGS6) that were missed by competing methods and TWAS. Our results suggest that our approach is a useful tool to prioritize putative causal genes, gaining insights into the mechanisms of complex traits.
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Availability of data and materials
We make our software FOGS publicly available on a GitHub repository: https://github.com/ChongWu-Biostat/FOGS. TWAS software and eQTL derived weights can be downloaded at http://gusevlab.org/projects/fusion/. The software FOCUS can be obtained at https://github.com/bogdanlab/focus/. The Schizophrenia GWAS summary data (Pardiñas et al. 2018) can be downloaded at http://walters.psycm.cf.ac.uk, and the Lung Health Study data can be obtained at dbGAP (phs000335.v2.p2). The data used in this paper can be downloaded at https://figshare.com/articles/FOGS_data/7636691.
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We thank reviewers for helpful comments. This research was supported by the Minnesota Supercomputing Institute. We appreciate the availability of the dbGaP data.
This research was supported by NIH Grants R21AG057038, R01HL116720, R01GM113250 and R01HL105397. CW was supported by a First Year Assistant Professor Grant at Florida State University.
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The authors declare no competing interests.
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Wu, C., Pan, W. A powerful fine-mapping method for transcriptome-wide association studies. Hum Genet 139, 199–213 (2020). https://doi.org/10.1007/s00439-019-02098-2