Genetic variability in SQSTM1 and risk of early-onset Alzheimer dementia: a European early-onset dementia consortium study
Bibliographic record
Abstract
Meta-analysis of existing genome-wide association studies on Alzheimer's disease (AD) showed subgenome-wide association of an intronic variant in the sequestosome 1 (SQSTM1) gene with AD. We performed targeted resequencing of SQSTM1 in Flanders-Belgian AD patients selected to be enriched for a genetic background (n = 435) and geographically matched nonaffected individuals (n = 872) to investigate the role of both common and rare SQSTM1 variants. Results were extended to the European early-onset dementia cohorts (926 early-onset Alzheimer's disease [EOAD] patients and 1476 nonaffected individuals). Of the 61 detected exonic variants in SQSTM1, the majority were rare (n = 57). Rare variant (minor allele frequency <0.01) burden analysis did not reveal an increased frequency of rare variants in EOAD patients in any of the separate study populations nor when meta-analyzing all cohorts. Common variants p.D292= and p.R312= showed nominal association with AD (odds ratiop.D292= = 1.11 [95% confidence interval = 1-1.22], p = 0.04), only when including the Flanders-Belgian cohort in the meta-analysis. We cannot exclude a role of SQSTM1 genetic variability in late-onset AD, but our data indicate that SQSTM1 does not play a major role in the etiology of EOAD.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".