Mendelian randomization integrating GWAS and eQTL data revealed genes pleiotropically associated with major depressive disorder
Bibliographic record
Abstract
Abstract Objectives To prioritize genes that are pleiotropically or potentially causally associated with the risk of MDD. Methods We applied the summary data-based Mendelian randomization (SMR) method integrating GWAS and expression quantitative trait loci (eQTL) data in 13 brain regions to identify genes that were pleiotropically associated with the risk of MDD. In addition, we repeated the analysis by using the meta-analyzed version of the eQTL summary data in the brain (brain-eMeta). Results We identified multiple significant genes across different brain regions that may be involved in the pathogenesis of MDD. The prime-specific gene BTN3A2 (corresponding probe: ENSG00000186470.9) was the top hit showing pleotropic association with MDD in 9 of the 13 brain regions and in brain-eMeta, after correction for multiple testing. Many of the identified genes are located in the human major histocompatibility complex (MHC) region on chromosome 6 and are mainly involved in immune response. Conclusions Our SMR analysis revealed that multiple genes showed pleiotropic association with MDD across the brain regions. These findings provide important leads to a better understanding of the mechanism of MDD, and reveals potential therapeutic targets for the prevention and effective treatment of MDD.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".