Genome-wide association and DNA methylation analyses of SSRI treatment response in major depressive disorder
Bibliographic record
Abstract
BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs) are first-line treatments for major depressive disorder (MDD); however, patient responses vary significantly. This study explored genetic and epigenetic factors linked to SSRI response to better understand this heterogeneity. METHODS: We performed a genome-wide association study (GWAS) in 852 MDD patients classified as SSRI responders or non-responders. A polygenic risk score (PRS) analysis was conducted to estimate the cumulative effect of common genetic variants. In a subset of 66 participants, genome-wide DNA methylation profiling was carried out using the Illumina Methylation EPIC array. Analyses included the identification of differentially methylated positions (DMPs) and regions (DMRs), as well as weighted gene co-methylation network analysis (WGCNA) and downstream pathway and protein–protein interaction (PPI) analyses. RESULTS: GWAS revealed several loci with suggestive associations, including intronic variants in SRCIN1 and NKAIN3, although none reached genome-wide significance. The PRS explained a small proportion of variance in SSRI response and did not yield statistically significant results. DNA methylation analysis identified nominally differentially methylated CpGs in genes involved in synaptic signaling and neurodevelopment (e.g., OLFM1, PTN, CACNB2, LHX6). In addition, WGCNA identified a co-methylation module that showed a trend-level association with SSRI response after adjustment for clinical covariates, and hub genes within this module were functionally linked to neuronal signaling in PPI network analysis. CONCLUSIONS: Although no single marker reached significance, the results suggest that synaptic plasticity and neurodevelopment may influence SSRI response. These genomic and epigenomic insights offer molecular clues that may inform future studies on the biological mechanisms underlying antidepressant response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".