@article{ref1,
title="Predicting suicide",
journal="American journal of psychiatry",
year="2020",
author="Goldman, David",
volume="177",
number="10",
pages="881-883",
abstract="Suicide, terrible and terrifying, is surprisingly common. However, viewed temporally, death by suicide is a rare, and indeed once-in-a lifetime, event. Adding to the mystery of suicide, no other animal consciously devises its own death. Suicide is feared because of its unpredictability and stigmatized in part for the same reason, but also because of the damage it leaves behind. For those grieving suicide victims, graveyards, contrary to de Gaulle's ironic aphorism, are full of irreplaceable people. Prediction and an understanding of the causes of suicide can save lives and inform us about disease conditions that sometimes lead to suicide but would also teach us to understand, and in other ways to improve, the human condition.   As may seem paradoxical, the likelihood of suicide is partly heritable. Our fates are not sealed, in Kierkegaard's terms, but are influenced by inborn factors. In this issue of the Journal, Docherty et al. (1) took a new approach to the prediction of suicide and its clinical antecedents, adding to the elements that might be combined, perhaps via machine learning, to predict suicide (Figure 1). They performed a genome-wide association study (GWAS) on suicide, rather than suicidality, which might also uncover different genes. Their GWAS, of more than 3,400 suicide victims from the state of Utah and several times that number of ethnically matched comparison subjects from outside the state, implicates two genes that were genome-wide significant and many more genes that were significant by gene-wise analysis. Gene-wise GWASs (see Liu et al. [2] for example), take advantage of multiple single-nucleotide polymorphisms (SNPs) genotyped at most genes on arrays used for GWAS and integrate this information to reduce multiple testing, increase statistical power, and facilitate gene-network analyses. The identification of genes that are significantly associated with suicide, and the implication of a much larger number of other genes in the genomic statistical threshold, enabled Docherty et al. to derive a polygenic risk score accounting for a substantial portion of the risk of suicide. Based on estimates that suicidality is 17%−55% heritable, they may have found an even larger portion of risk attributable to genes. Shedding light on why genes contributing to suicide exist, the authors found genetic cross-connections to other psychiatric diseases and behaviors, including behavioral disinhibition, depression, psychosis, autism, and alcohol use disorder. As a species, humans are inherently diverse in behavior, making us stronger as a community. But we also pay a price in individual vulnerability to psychiatric diseases. Identification of genetic risk factors can target interventions to enhance lives--for example, by preventing suicide--without diminishing our essential diversity or reducing the origins of suicide to a simplistic, deterministic formula...<p /> <p>Language: en</p>",
language="en",
issn="0002-953X",
doi="10.1176/appi.ajp.2020.20071138",
url="http://dx.doi.org/10.1176/appi.ajp.2020.20071138"
}