Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data

Abstract

Genome-wide association studies (GWAS) of suicidal thoughts and behaviors support the existence of genetic contributions. Continuous measures of psychiatric disorder symptom severity can sometimes model polygenic risk better than binarized definitions. We compared two severity measures of suicidal thoughts and behaviors at the molecular and functional levels using genome-wide data. We used summary association data from GWAS of four traits analyzed in 122,935 individuals of European ancestry:thought life was not worth living(TLNWL),thoughts of self-harm, actual self-harm, andattempted suicide. The fifth trait, suicidality, was constructed with phenotypically as an aggregate of these four traits and genetically using Genomic Structural Equation modeling. Suicidality and S-factor were compared at the level of SNP-heritability (h2), genetic correlation, partitionedh2, effect size distribution, transcriptomic effects in the brain, and cross-population polygenic scoring (PGS). The S-factor had good model fit (χ2=0.21, AIC=16.21, CFI=1.00, SRMR=0.024). Suicidality (h2=7.6%) had higherh2than the S-factor (h2=2.54, Pdiff=4.78×10-13). Although the S-factor had a larger number of non-null susceptibility loci (πc=0.010), these loci had small effect sizes compared to those influencing suicidality (πc=0.005, Pdiff=0.045). Theh2of both traits was enrichment for conserved biological pathways. Therg andρGEsupport highly overlapping genetic and transcriptomic features between suicidality and the S-factor. PGS using European-ancestry SNP effect sizes strongly associated with TLNWL in Admixed Americans: Nagelkerke’sR2=8.56%, P=0.009 (PGSsuicidality) and Nagelkerke’sR2=7.48%, P=0.045 (PGSS-factor). An aggregate suicidality phenotype was statistically more heritable than the S-factor across all analyses and may be more informative for future study genetic designs than individual suicidality indicator traits.