Suicide and suicide behaviors: A review of transcriptomics and multiomics studies in psychiatric disorders
Bibliographic record
Abstract
Aberrant gene expression can contribute to brain and nervous system dysfunction that causes many psychiatric illnesses. Here, we review how transcriptomic approaches have deepened our understanding of the neurobiological underpinnings of psychiatric disorders and how they have to the identification of biomarkers for these disorders, with a focus on their relevance to suicide and suicide behaviors. We begin by providing an overview of the genetic, transcriptomic, and epigenetic factors (including non-coding RNAs) implicated in suicide and their roles in the regulation of gene expression. Then, we highlight the unique benefits and limitations of using either postmortem brain or peripheral tissues in transcriptomic analyses. We examine the current shift from candidate gene to genome-wide approaches in psychiatric research, which are concurrently emerging with the increased consideration of the Research Domain Criteria (RDoC) framework for classifying mental illnesses. Furthermore, we discuss the use of transcription networks and how they can be integrated into multiomic analyses. Finally, we end by highlighting recent findings of peripheral markers of suicide risk identified through the use of transcriptomic tools. Technological advancements and increased accessibility of these technologies are drastically shaping the current research landscape. We present an overview of the significant changes currently taking place to usher in a new era of psychiatric research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".