New insights on the genetic etiology of Alzheimer’s and related dementia
Bibliographic record
Abstract
ABSTRACT Alzheimer’s disease (AD) is a severe and incurable neurodegenerative disease, and the failure to find effective treatments suggests that the underlying pathology remains poorly understood. Due to its strong heritability, deciphering the genetic landscape of AD and related dementia (ADD) is a unique opportunity to advance our knowledge. We completed a meta-analysis of genome-wide association studies (39,106 clinically AD-diagnosed cases, 46,828 proxy-ADD cases and 401,577 controls) with the most promising signals followed-up in 25,392 independent AD cases and 276,086 controls. We report 75 risk loci for ADD, including 42 novel ones. Pathway-enrichment analyses confirm the involvement of amyloid/Tau pathways, highlight the role of microglia and its potential interaction with APP metabolism. Numerous genes exhibited differential expression or splicing in AD-related conditions and gene prioritization implies EGFR signaling and TNF-α pathway through LUBAC complex. We also generated a novel polygenic risk score strongly associated with the risk of future dementia or progression from mild cognitive impairment to dementia. In conclusion, by more than doubling the number of loci associated with ADD risk, our study offers new insights into the pathophysiological processes underlying AD and offers additional therapeutic entry-points and tools for translational genomics.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".