ReviewTranslational psychiatry2023
Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings.
Review in Translational psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Bilateral theta burst stimulation as an add-on to schema therapy in major depression: methods and design of a randomized, sham-controlled study.European archives of psychiatry and clinical neuroscience · 2026Article
- Transcriptomic characteristics of plasma from Chinese ethnic minority patients with major depressive disorder.Translational psychiatry · 2026Article
- Molecular Characterization of the Progressive Landscape of Depression.bioRxiv : the preprint server for biology · 2026Article
- Transcription factors Lef1 and Rest stimulate recovery from depressive states.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Peripheral blood transcriptomic biomarkers for predicting antidepressant response in major depressive disorder.BMC psychiatry · 2026Article
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- Psychiatry and the unknown future: the period of hope.Frontiers in psychiatry · 2026Article
- The Multifaceted Pathophysiology of Major Depressive Disorder: Integrating Neurobiology, Genetics, and Systems-level Perspectives.Current neurovascular research · 2026Review
- Ceramides and neuroinflammation as immunometabolic drivers and biomarkers of major depressive disorder, treatment-resistant depression, and suicidal vulnerability.Frontiers in pharmacology · 2026Review
- Integrative neuroimmunology reveals leukocyte-expressing PAX6 as a critical predictor of major depressive disorder.Translational psychiatry · 2025Article
- Deciphering transcriptomic signatures in schizophrenia, bipolar disorder, and major depressive disorder.Frontiers in psychiatry · 2025Article
- Multi-omics insights into biomarkers of breast cancer associated diabetes: a computational approach.Frontiers in medicine · 2025Article
- Shared and unique transcriptomic signatures of antidepressant and probiotics action in the mammalian brain.Molecular psychiatry · 2024Article
- Decoding the transcriptomic signatures of psychological trauma in human cortex and amygdala.bioRxiv : the preprint server for biology · 2024Article
- Major depressive disorder: hypothesis, mechanism, prevention and treatment.Signal transduction and targeted therapy · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.