Genome-wide association meta-analysis and rare copy number variant analysis of treatment-resistant depression
Bibliographic record
Abstract
Abstract Treatment-resistant depression (TRD), defined as major depressive disorder (MDD) with multiple failed responses to antidepressant treatments, has been suggested to be heritable, but identifying its genetic component is challenging. Using a restrictive TRD definition based on antidepressant medication followed by electroconvulsive therapy (ECT), which may represent a severe subset of TRD cases, we investigated both common variants and rare copy number variations (CNVs) associated with a) TRD risk (2 062 TRD vs. 441 037 healthy controls) and b) treatment resistance in MDD (2 062 TRD vs. 38 544 non-TRD) across three Nordic countries. We observed a significant SNP-based heritability for TRD risk at 26% (SE = 5%). Genome-wide association analysis identified one locus on chromosome 3 (intronic region of SPATA16 ) for TRD risk and one suggestive locus for treatment resistance in MDD. TRD risk showed positive genetic correlations ( r g ) with other psychiatric disorders, with notably r g with bipolar disorder (0.86, SE = 0.20) and schizophrenia (0.57, SE = 0.13), as well as a negative r g with intelligence (−0.13, SE = 0.07). Analyses using PRS showed that TRD had higher common-variant burdens of various psychiatric disorders compared to non-TRD. Furthermore, TRD carried a higher CNV deletion burden in total and average lengths than healthy controls or non-TRD cases and was associated with a group of 54 known neuropsychiatric CNVs (ORs = 1.74–2.86). Given that our definition of TRD involves the use of ECT, our findings may reflect a severe form of treatment resistance. This work adds evidence on a genetic basis and provides insights into the genetic architecture of TRD, underscoring the need for further genomic research into this ‘difficult-to-treat’ condition.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".