Meta-analysis Confirms CR1, CLU, and PICALM as Alzheimer Disease Risk Loci and Reveals Interactions With APOE Genotypes
Bibliographic record
Abstract
OBJECTIVES: To determine whether genotypes at CLU, PICALM, and CR1 confer risk for Alzheimer disease (AD) and whether risk for AD associated with these genes is influenced by apolipoprotein E (APOE) genotypes. DESIGN: Association study of AD and CLU, PICALM, CR1, and APOE genotypes. SETTING: Academic research institutions in the United States, Canada, and Israel. PARTICIPANTS: Seven thousand seventy cases with AD, 3055 with autopsies, and 8169 elderly cognitively normal controls, 1092 with autopsies, from 12 different studies, including white, African American, Israeli-Arab, and Caribbean Hispanic individuals. RESULTS: Unadjusted, CLU (odds ratio [OR], 0.91; 95% confidence interval [CI], 0.85-0.96 for single-nucleotide polymorphism [SNP] rs11136000), CR1 (OR, 1.14; 95% CI, 1.07-1.22; SNP rs3818361), and PICALM (OR, 0.89; 95% CI, 0.84-0.94, SNP rs3851179) were associated with AD in white individuals. None were significantly associated with AD in the other ethnic groups. APOE ε4 was significantly associated with AD (ORs, 1.80-9.05) in all but 1 small white cohort and in the Arab cohort. Adjusting for age, sex, and the presence of at least 1 APOE ε4 allele greatly reduced evidence for association with PICALM but not CR1 or CLU. Models with the main SNP effect, presence or absence of APOE ε4, and an interaction term showed significant interaction between presence or absence of APOE ε4 and PICALM. CONCLUSIONS: We confirm in a completely independent data set that CR1, CLU, and PICALM are AD susceptibility loci in European ancestry populations. Genotypes at PICALM confer risk predominantly in APOE ε4-positive subjects. Thus, APOE and PICALM synergistically interact.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".