Locus-specific stratification and prioritization unveil genetic risk mechanism underlying complex diseases.

Although genome-wide association studies have identified thousands of disease-associated loci, the mechanistic understanding and drug target discovery remain challenging, particularly for complex diseases. The multi-signal architecture of complex diseases complicates the interpretation of genetic contributions. To address this challenge, we develop an approach comprising locus-specific stratification (LSS) and gene regulatory prioritization score (GRPS), which uniquely considers multi-signals during fine-mapping and target gene identification. LSS significantly enhances the interpretability of genetic risk associated with complex diseases. For loci associated with serum urate levels, the method identifies candidate causal genes in 34.43% of loci, surpassing the performance of other methods by 5.47% to 25.14%. GRPS considers the regulatory network of LSS-variants comprehensively and successfully nominates under-explored drug targets for hyperuricemia with high confidence such as SLC17A4, which is further validated using epigenetic activation and phenotypic assays. This study introduces an approach to efficiently and comprehensively address the multi-signal challenges in complex diseases.