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Citation |
Turiaco F, Iannuzzo F, Bruno A, Drago A. Eur. Arch. Psychiatry Clin. Neurosci. 2024; ePub(ePub): ePub. |
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Copyright |
(Copyright © 2024, Holtzbrinck Springer Nature Publishing Group) |
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DOI |
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PMID |
38878077 |
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Abstract |
Suicide is a leading cause of death worldwide. Suicide ideation (SI) is a known risk factor for suicide behaviour (SB). The current psychobiology and genetic predisposition to SI and SB are poorly defined. Despite convincing relevance of a genetic background for SI, there is no current implementable knowledge about the genetic makeup that identifies subjects at risk for it. One of the possible reasons for the absence of a clear-cut evidence is the polygenetic nature of SI along with the very large sample sizes that are needed to observe significant genetic association result. The CATIE sample was instrumental to the analysis. SI was retrieved as measured by the Calgary test. Clinical possible covariates were identified by a nested regression model. A principal component analysis helped in defining the possible genetic stratification factors. A GWAS analysis, polygenic risk score associated with a random forest analysis and a molecular pathway analysis were undertaken to identify the genetic contribution to SI. As a result, 741 Schizophrenic individuals from the CATIE were available for the genetic analysis, including 166,325 SNPs after quality control and pruning. No GWAS significant result was found. The random forest analysis conducted by combining the polygenic risk score and several clinical variables resulted in a possibly overfitting model (OOB error rate < 1%). The molecular pathway analysis revealed several molecular pathways possibly involved in SI, of which those involved in microglia functioning were of particular interest. A medium-small sample of SKZ individuals was analyzed to shed a light on the genetic of SI. As an expected result from the underpowered sample, no GWAS positive result was retrieved, but the molecular pathway analysis indicated a possible role of microglia and neurodevelopment in SI. Language: en |
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Keywords |
PCR; Suicide ideation; Machine learning; SNP; GWAS; Molecular pathway analysis |