‘Omics’ of suicidal behaviour: A path to personalised psychiatry

Abstract

Psychiatric disorders, including suicide, are complex disorders that are affected by many different risk factors. It has been estimated that genetic factors contribute up to 50% to suicide risk. As the candidate gene approach has not identified a gene or set of genes that can be defined as biomarkers for suicidal behaviour, much is expected from cutting edge technological approaches that can interrogate several hundred, or even millions, of biomarkers at a time. These include the ‘-omic’ approaches, such as genomics, transcriptomics, epigenomics, proteomics and metabolomics. Indeed, these have revealed new candidate biomarkers associated with suicidal behaviour. The most interesting of these have been implicated in inflammation and immune responses, which have been revealed through different study approaches, from genome-wide single nucleotide studies and the micro-RNA transcriptome, to the proteome and metabolome. However, the massive amounts of data that are generated by the ‘-omic’ technologies demand the use of powerful computational analysis, and also specifically trained personnel. In this regard, machine learning approaches are beginning to pave the way towards personalized psychiatry.

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder

“…The biological factors which contribute to a) suicide risk and b) antidepressant treatment outcomes likely comprise multiple pathological processes interacting together as a network ( Kouter and Videtic Paska, 2021 ). Integrative omics methods (i.e., genomics, metabolomics, transcriptomics, epigenomics), therefore, are novel and promising approaches for elucidating important molecular mechanisms underlying suicidality and other complex behavioral phenotypes ( Barabasi et al, 2011 ; Bhak et al, 2019 ; Joyce et al, 2021 ; Liu et al, 2021 ; Grant et al, 2022 ).…”