Molecular and Genetic Characterization of Depression: Overlap with Other Psychiatric Disorders and Aging
Why this work is in the frame
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Bibliographic record
Abstract
Genome-wide expression and genotyping technologies have uncovered the genetic bases of complex diseases at unprecedented rates; However despite its heavy burden and high prevalence, the molecular characterization of major depressive disorder (MDD) has lagged behind. Transcriptome studies report multiple brain disturbances but are limited by small sample sizes. Genome-wide association studies (GWAS) report weak results but suggest overlapping genetic risk with other neuropsychiatric disorders. We performed systematic molecular characterization of altered brain function in MDD, using meta-analysis of differential expression in eight gene array studies in three corticolimbic brain regions in 101 subjects. The identified "metaA-MDD" genes suggest altered neurotrophic support, brain plasticity and neuronal signaling in MDD. Notably, metaA-MDD genes display low connectivity and hubness in coexpression networks, and uniform genomic distribution, consistent with diffuse polygenic mechanisms. We next integrated these findings with results from over 1800 published GWAS and show that genetic variations nearby metaA-MDD genes predict greater risk for neuropsychiatric disorders and notably for age-related phenotypes, but not for other medical illnesses, including those frequently co-morbid with depression, or body characteristics. Collectively, the intersection of unbiased investigations of gene function (transcriptome) and structure (GWAS) provides novel leads to investigate molecular mechanisms of MDD and suggest common biological pathways between depression, other neuropsychiatric diseases, and brain aging.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it