Genome-Wide Association Study of Suicide Death and Polygenic Prediction of Clinical Antecedents

Abstract

Objective: Death by suicide is a highly preventable yet growing worldwide health crisis. To date, there has been a lack of adequately powered genomic studies of suicide, with no sizable suicide death cohorts available for analysis. To address this limitation, the authors conducted the first comprehensive genomic analysis of suicide death using previously unpublished genotype data from a large population-ascertained cohort.

Methods: The analysis sample comprised 3,413 population-ascertained case subjects of European ancestry and 14,810 ancestrally matched control subjects. Analytical methods included principal component analysis for ancestral matching and adjusting for population stratification, linear mixed model genome-wide association testing (conditional on genetic-relatedness matrix), gene and gene set-enrichment testing, and polygenic score analyses, as well as single-nucleotide polymorphism (SNP) heritability and genetic correlation estimation using linkage disequilibrium score regression.

Results: Genome-wide association analysis identified two genome-wide significant loci (involving six SNPs: rs34399104, rs35518298, rs34053895, rs66828456, rs35502061, and rs35256367). Gene-based analyses implicated 22 genes on chromosomes 13, 15, 16, 17, and 19 (q<0.05). Suicide death heritability was estimated at an h²_SNP value of 0.25 (SE=0.04) and a value of 0.16 (SE=0.02) when converted to a liability scale. Notably, suicide polygenic scores were significantly predictive across training and test sets. Polygenic scores for several other psychiatric disorders and psychological traits were also predictive, particularly scores for behavioral disinhibition and major depressive disorder.

Conclusions: Multiple genome-wide significant loci and genes were identified and polygenic score prediction of suicide death case-control status was demonstrated, adjusting for ancestry, in independent training and test sets. Additionally, the suicide death sample was found to have increased genetic risk for behavioral disinhibition, major depressive disorder, depressive symptoms, autism spectrum disorder, psychosis, and alcohol use disorder compared with the control sample.

Keywords: GWAS; Polygenic Score; Suicide.

Figure 1.. GWAS Results.

(a) QQ and Manhattan plots from GWAS of suicide death. Y-axes for both plots reflect observed p-values. The x-axis on the qq-plot is the number of significant p-values expected under H₀, and the x-axis on the Manhattan plot maps each chromosome. The purple dashed line indicates FDR corrected nominal statistical significance, the green dotted line representing the threshold for genome-wide significance with Bonferroni correction. 57 SNPs met threshold for nominal significance and 6 met genome-wide significance. (b) Regional plots of genome-wide significant loci on chromosomes 13 and 15.

Figure 2.. GWAS Gene-Based Results.

FUMA gene-based test results: qq plot and Manhattan plot of >18,000 genes. Y-axes for both plots are identical and reflect observed p-values. The x-axis on the qq-plot is the number of significant p-values expected under H₀, and the x-axis on the Manhattan plot maps each chromosome. The purple dashed line indicates threshold for FDR-corrected nominal statistical significance; 10 genes met this threshold for nominal significance.

Figure 3.. Cross Validation of Suicide Polygenic Case-Control Prediction.

Polygenic prediction of suicide death case status across two independent cohorts of cases and controls. Training GWAS summary statistics are used to score the test set for suicide polygenic risk. P-value thresholds are plotted on the x-axis from 0.1–1.0, reflecting the top 10% to 100% of the common variants from the training GWAS. Plots present model fit when a) predicting case-control status with wave 1 suicide vs. Generation Scotland summary statistics and b) predicting with wave 2 suicide vs. UK10K summary statistics. Scores are adjusted for 20 PCs. Summary data for the best fitting and 1.0 p-value threshold models are presented in Table S29.

Figure 4.. Notable Elevations of Psychiatric Polygenic Risk in Suicide Cases.

P-value thresholds are plotted on the x-axis. Scores are adjusted for 20 PCs. Largest effect sizes and significance levels were observed for major depressive disorder, schizophrenia, and behavioral disinhibition. Summary data for the best fitting and 1.0 p-value threshold models for all eight phenotypes—also including alcohol use, autism spectrum disorder, child IQ, depressive symptoms, and loneliness—are presented in Table S29.