Replication of EPHA1 and CD33 associations with late-onset Alzheimer's disease: a multi-centre case-control study
Bibliographic record
Abstract
BACKGROUND: A recently published genome-wide association study (GWAS) of late-onset Alzheimer's disease (LOAD) revealed genome-wide significant association of variants in or near MS4A4A, CD2AP, EPHA1 and CD33. Meta-analyses of this and a previously published GWAS revealed significant association at ABCA7 and MS4A, independent evidence for association of CD2AP, CD33 and EPHA1 and an opposing yet significant association of a variant near ARID5B. In this study, we genotyped five variants (in or near CD2AP, EPHA1, ARID5B, and CD33) in a large (2,634 LOAD, 4,201 controls), independent dataset comprising six case-control series from the USA and Europe. We performed meta-analyses of the association of these variants with LOAD and tested for association using logistic regression adjusted by age-at-diagnosis, gender, and APOE ε4 dosage. RESULTS: We found no significant evidence of series heterogeneity. Associations with LOAD were successfully replicated for EPHA1 (rs11767557; OR = 0.87, p = 5 × 10-4) and CD33 (rs3865444; OR = 0.92, p = 0.049), with odds ratios comparable to those previously reported. Although the two ARID5B variants (rs2588969 and rs494288) showed significant association with LOAD in meta-analysis of our dataset (p = 0.046 and 0.008, respectively), the associations did not survive adjustment for covariates (p = 0.30 and 0.11, respectively). We had insufficient evidence in our data to support the association of the CD2AP variant (rs9349407, p = 0.56). CONCLUSIONS: Our data overwhelmingly support the association of EPHA1 and CD33 variants with LOAD risk: addition of our data to the results previously reported (total n > 42,000) increased the strength of evidence for these variants, providing impressive p-values of 2.1 × 10-15 (EPHA1) and 1.8 × 10-13 (CD33).
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".