Genomics-driven screening for causal determinants of suicide attempt. (March 2023)

• Record Type: Journal Article
• Authors: Campos, Adrian I; Garcia-Marin, Luis M; Christensen, Helen; Batterham, Philip J; van Velzen, Laura S; Schmaal, Lianne; Rabinowitz, Jill A; Jahanshad, Neda; Martin, Nicholas G; Cuellar-Partida, Gabriel; Ruderfer, Douglas; Mullins, Niamh; Rentería, Miguel E
• Is Part Of: Australian and New Zealand journal of psychiatry. Volume 57:Number 3(2023)
• Page Start: 423
• Page End: 431
• Publication Date: 2023-03
• Subjects: Suicide -- risk factors -- genetics -- causal inference -- complex traits; Psychiatry -- Periodicals; Psychiatry -- Australia -- Periodicals; Psychiatry -- New Zealand -- Periodicals; 616.89005
• DOI: 10.1177/00048674221091499
• Languages: English
• ISSNs: 0004-8674

Abstract

Objective: Each year, around one million people die by suicide. Despite its recognition as a public health concern, large-scale research on causal determinants of suicide attempt risk is scarce. Here, we leverage results from a recent genome-wide association study (GWAS) of suicide attempt to perform a data-driven screening of traits causally associated with suicide attempt. Methods: We performed a hypothesis-generating phenome-wide screening of causal relationships between suicide attempt risk and 1520 traits, which have been systematically aggregated on the Complex-Traits Genomics Virtual Lab platform. We employed the latent causal variable (LCV) method, which uses results from GWAS to assess whether a causal relationship can explain a genetic correlation between two traits. If a trait causally influences another one, the genetic variants that increase risk for the causal trait will also increase the risk for the outcome inducing a genetic correlation. Nonetheless, a genetic correlation can also be observed when traits share common pathways. The LCV method can assess whether the pattern of genetic effects for two genetically correlated traits support a causal association rather than a shared aetiology. Results: Our approach identified 62 traits that increased risk for suicide attempt. Risk factors identified can be broadly classified into (1) physical health disorders, including oesophagitis, fibromyalgia, hernia and cancer; (2) mental health-related traits, such as depression, substance use disorders and anxiety; and (3) lifestyle traits including being involved in combat or exposure to a war zone, and specific job categories such as being a truck driver or machine operator. Conclusions: Suicide attempt risk is likely explained by a combination of behavioural phenotypes and risk for both physical and psychiatric disorders. Our results also suggest that substance use behaviours and pain-related conditions are associated with an increased suicide attempt risk, elucidating important causal mechanisms that underpin this significant public health problem.