Genome-Wide Survey of Large Rare Copy Number Variants in Alzheimer’s Disease Among Caribbean Hispanics
Bibliographic record
Abstract
Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7. However, the pathogenic variants in these loci have not yet been found. We conducted a genome-wide scan for large copy number variation (CNV) in a dataset of Caribbean Hispanic origin (554 controls and 559 AD cases that were previously investigated in a SNP-based genome-wide association study using Illumina HumanHap 650Y platform). We ran four CNV calling algorithms to obtain high-confidence calls for large CNVs (>100 kb) that were detected by at least two algorithms. Global burden analyses did not reveal significant differences between cases and controls in CNV rate, distribution of deletions or duplications, total or average CNV size; or number of genes affected by CNVs. However, we observed a nominal association between AD and a ∼470 kb duplication on chromosome 15q11.2 (P = 0.037). This duplication, encompassing up to five genes (TUBGCP5, CYFIP1, NIPA2, NIPA1, and WHAMML1) was present in 10 cases (2.6%) and 3 controls (0.8%). The dosage increase of CYFIP1 and NIPA1 genes was further confirmed by quantitative PCR. The current study did not detect CNVs that affect novel AD loci identified by recent genome-wide association studies. However, because the array technology used in our study has limitations in detecting small CNVs, future studies must carefully assess novel AD genes for the presence of disease-related CNVs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".