Evaluating pathogenicity of variants of unknown significance in APP, PSEN1, and PSEN2
Bibliographic record
Abstract
Autosomal dominant Alzheimer's disease (ADAD) is driven by rare variants in APP, PSEN1, and PSEN2. Although more than 200 pathogenic variants in these genes are known to cause ADAD, other variants are benign, may act as risk factors, or may even reduce Alzheimer's disease risk (e.g. protective). Classifying novel variants in APP, PSEN1, or PSEN2 as pathogenic, risk, benign, or protective is a critical step in evaluating disease risk profiles which further impacts eligibility for clinical trials focused on the ADAD population. Here, we classify 53 novel variants in APP, PSEN1, and PSEN2 based on bioinformatic data and cell-based assays. We identified 6 benign variants, 2 risk variants, and 32 likely pathogenic variants. Thirteen variants were associated with reduced Aβ levels in cell-based assays, consistent with a potential protective effect. Together, this study highlights the complexities associated with classification of rare variants in ADAD genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".