Alzheimer’s Disease variant portal (ADVP): a catalog of genetic findings for Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Alzheimer’s Disease (AD) genetics has made substantial progress through genome-wide association studies (GWASs). An up-to-date resource providing harmonized, searchable information on AD genetic variants with linking to genes and supporting functional evidence is needed. We developed the Alzheimer’s Disease Variant Portal (ADVP), an extensive collection of associations curated from >200 GWAS publications from Alzheimer’s Disease Genetics Consortium (ADGC) and other researchers. Publications are reviewed systematically to extract top associations for harmonization and genomic annotation. ADVP V1.0 catalogs 6,990 associations with disease-risk, expression quantitative traits, endophenotypes and neuropathology across >900 loci, >1,800 variants, >80 cohorts, and 8 populations. ADVP integrates with NIAGADS Alzheimer’s GenomicsDB where investigators can cross-reference other functional evidence. ADVP is a valuable resource for investigators to quickly and systematically explore high-confidence AD genetic findings and provides insights into population- and tissue-specific AD genetic architecture. ADVP is continually maintained and enhanced by NIAGADS and is freely accessible ( https://advp.niagads.org ).
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.042 |
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".