Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide

Abstract

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10−4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10−2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10−5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34–0.81) as well as several psychiatric disorders (rg = 0.26–0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

Genome-wide association study of suicide attempt in psychiatric disorders identifies association with major depression polygenic risk scores

“…Genetic studies have indicated that suicide attempt has a polygenic architecture, as polygenic risk scores for suicide attempt have shown modest predictive ability in independent samples and small but significant SNP-heritability estimates for suicide attempt have been reported (10,13). These findings are consistent with the presence of small genetic effects that the original GWAS were underpowered to detect at genome-wide significance.…”

“…These studies have failed to identify any replicable genetic associations, likely due to limited sample sizes that were underpowered to detect the genetic effects typical for a single SNP. Other GWAS have examined subjects recruited specifically on the basis of suicide attempt, or suicide attempters and non-attempters from population-based cohorts, but to date no loci have been robustly implicated (11)(12)(13).…”

Phenome-wide Investigation of Health Outcomes Associated with Genetic Predisposition to Loneliness

“…Genotyping and QC of this sample have been described elsewhere. 20,33 PheWAS: A logistic regression model was fitted to each of 897 case/control phenotypes to estimate the odds of each diagnosis given the loneliness polygenic score, after adjustment for sex, median age across the EHR, top 10 principal components of ancestry, and genotyping batch. symptoms, and 52 injuries/poisonings.…”

The Differences Between Individuals Engaging in Nonsuicidal Self-Injury and Suicide Attempt Are Complex (vs. Complicated or Simple)

“…On balance, some studies indicate that bootstrap optimism correction methods perform similarly to other internal validation methods (73,74), random forest models can generalize well to new data (75,76), and random forest combined with bootstrap optimism correction performs similarly to other internal validation methods and other machine learning techniques (73,77,78). There is also evidence that Walsh et al's algorithm (64) using this approach generalizes well to new samples and new suicide-related outcomes (79,80). Nonetheless, much remains unknown about how various methods perform under various conditions, so at a minimum these discrepancies indicate that it would be prudent to conduct analyses with multiple internal validation techniques.…”