GWAS of Suicide Attempt in Psychiatric Disorders and Association With Major Depression Polygenic Risk Scores

Abstract

Objective: Over 90% of suicide attempters have a psychiatric diagnosis, however twin and family studies suggest that the genetic etiology of suicide attempt (SA) is partially distinct from that of the psychiatric disorders themselves. Here, we present the largest genome-wide association study (GWAS) on suicide attempt using major depressive disorder (MDD), bipolar disorder (BIP) and schizophrenia (SCZ) cohorts from the Psychiatric Genomics Consortium. Method: Samples comprise 1622 suicide attempters and 8786 non-attempters with MDD, 3264 attempters and 5500 non-attempters with BIP and 1683 attempters and 2946 non-attempters with SCZ. SA GWAS were performed by comparing attempters to non-attempters in each disorder followed by meta-analyses across disorders. Polygenic risk scoring was used to investigate the genetic relationship between SA and the psychiatric disorders. Results: Three genome-wide significant loci for SA were found: one associated with SA in MDD, one in BIP, and one in the meta-analysis of SA in mood disorders. These associations were not replicated in independent mood disorder cohorts from the UK Biobank and iPSYCH. No significant associations were found in the meta-analysis of all three disorders. Polygenic risk scores for major depression were significantly associated with SA in MDD (R 2 =0.25%, P=0.0006), BIP (R 2 =0.24%, P=0.0002) and SCZ (R 2 =0.40%, P=0.0006). Conclusions: This study provides new information on genetic associations and demonstrates that genetic liability for major depression increases risk for suicide attempt across psychiatric disorders. Further collaborative efforts to increase sample size hold potential to robustly identify genetic associations and gain biological insights into the etiology of suicide attempt.

A principal component approach to improve association testing with polygenic risk scores

“…The PRS-PCA approach controls Type I error while maintaining good power. This approach is well-suited to hypothesis testing with many PRSs, because it prevents overfitting each PRS to the outcome and does not require choosing one p-value threshold for all PRSs (Mullins et al, 2019;Richardson et al, 2019;Zheutlin et al, 2019), which can reduce power as seen in our simulations. In this study, we explored how the PRS-PCA approach can improve PRS analyses that implement P+T, by using the F I G U R E 2 Scaled weights on effect sizes of SNPs (betas) for SCZ-PRS assigned by PRS-PCA in the Mayo Clinic sample.…”

Understanding the Complex of Suicide in Depression: from Research to Clinics

“…[23][24][25] A genome-wide association study (GWAS) of suicidal thoughts and behaviour in MDD, indicated a polygenetic architecture with multiple genes implicated even though with small effects. 26 However, although several GWAS studies have been conducted on SA examining individuals with MDD, comparing suicide attempters with non-attempters and testing for genetic variants that might contribute independently to SA, [27][28][29][30][31] epidemiological evidence suggests that the inheritance of suicidality is likely to be independent of the underlying MDD, by supporting a distinct genetic contribution to suicidality. 32 Polygenic risk scores for SA have shown modest predictive capability in independent samples, and small but significant single-nucleotide polymorphism (SNP) heritability estimates for SA have been reported.…”

A principal component approach to improve association testing with polygenic risk scores

“…The PRS-PCA approach was designed to control type I error while maintaining good power. This approach is most suited to hypothesis testing with many PRSs because it prevents overfitting each PRS to the outcome and does not require choosing one p-value threshold for all PRSs 8,9,25 , which can reduce power. In this paper, we explored how the PRS-PCA approach can improve PRS analyses that implement P+T.…”