Distinct Neurodevelopmental Signatures of Sex-Specific Transcriptome-Based Polygenic Risk Scores for Depression
Bibliographic record
Abstract
Major depressive disorder (MDD) impacts females and males differently and post-mortem gene expression profiling reveals distinct transcriptomic signatures of the disorder in each sex. Using genes that are transcriptionally altered in MDD in both sexes, we recently developed a novel transcriptome-based polygenic risk score (tPRS), which had sex-specific associations with brain structure and depressive symptoms in both adolescents and adults. Identifying the neurodevelopmental signatures of genetically-induced shifts toward a depression-like brain transcriptome in each sex during a crucial stage, when sex differences in MDD vulnerability initially manifest, could provide useful information about the developmental pathways of early MDD risk. Leveraging sex-specific MDD gene expression data, we sought to develop female- and male-specific tPRSs (tPRS-F and tPRS-M, respectively) and evaluate their impact on regional cortical thickness, cortical surface area, subcortical volume, and depressive symptoms at baseline and 2-year follow-up in a developmental sample of 5002 adolescents (46.6% female, aged 8.9-11.0). In males, tPRS-M was associated with higher depressive symptoms at both timepoints and thicker left posterior cingulate at follow-up. In females, tPRS-F was associated with lower volumes of the right accumbens area, right caudate, and bilateral hippocampi at follow-up. Subcortical volumes of the right caudate and right hippocampus further mediated an indirect effect of tPRS-F on depressive symptoms. For each sex-specific PRS, no effects emerged in the opposite sex. Our findings suggest that sex-specific depression-like shifts in gene expression may contribute to unique vulnerability phenotypes for future MDD risk via distinct mechanisms in each sex.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".