Genetic overlap between major depression, bipolar disorder and Alzheimer’s Disease
Bibliographic record
Abstract
ABSTRACT Background Mood disorders, including major depression (MD) and bipolar disorder (BD), are risk factors for Alzheimer’s disease (AD) and possibly share an overlapping genetic architecture. However, few studies have investigated the shared loci and potential pleiotropy among these disorders. Methods We carried out a systematic analytical pipeline using GWAS data and three complementary (genome-wide, single variant, and gene-level) statistical approaches to investigate the genetic overlap among MD, BD, and AD. Results GWAS summary statistics data from 679,973 individuals were analyzed herein (59,851 MD cases and 113,154 controls; 20,352 BD cases and 31,358 controls; and 71,880 AD cases and 383,378 controls). We identified a significant positive genetic correlation between MD and AD (r G = 0.162; s.e. = 0.064; p = 0.012), and between BD and AD (r G = 0.162; s.e. = 0.068; p = 0.018). We also identified two pleiotropic candidate genes for MD and AD (TMEM106B and THSD7A) and three forBD and AD ( MTSS2, VAC14 , and FAF1) , and reported candidate biological pathways associated with all three disorders. Discussion Our study identified genetic loci and mechanisms shared by mood disorders and AD. These findings could be relevant to better understand the higher risk for AD among individuals with mood disorders, and to propose new interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".