Mapping the genetic landscape of suicide risk: Insights from genomic SEM.

The genetic basis of traits associated with suicide risk remains poorly understood. We applied genomic structural equation modeling and integrated multiple post-genome-wide association study (GWAS) analysis strategies to identify potential causal single-nucleotide polymorphisms independent of known high-risk suicide syndrome GWAS variants, identifying four genome-wide-significant, putatively causal loci and six putatively causal genes. Additionally, we applied multiple transcriptome-wide association study (TWAS) methods at the tissue and cellular levels to fine-map susceptibility genes. Subsequently, we analyzed the key regulatory elements driving their expression. Next, we assessed genetic pleiotropy by evaluating genetic correlations between high-risk suicide syndrome and over one hundred common diseases. Furthermore, we constructed a polygenic risk score (PRS) from the summary statistics to quantify each chromosome's contribution to the associated genetic risk. Our study systematically delineated the overall genetic architecture of high-risk suicide syndrome through a GWAS of this previously unmeasured latent phenotype.