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.
PubMed: MeSH publication types
- Journal Article