Whole Genome Variable Number Tandem Repeat Analysis in Alzheimer Disease
Bibliographic record
Abstract
Background and Objectives: Investigation into different allelic variants may yield new associative genes to predict late-onset Alzheimer disease (LOAD). Variable number tandem repeats (VNTRs) are important polymorphic components of the genome; however, they have been previously overlooked because of their complex genotyping. New software can now determine differing lengths of VNTRs; however, this has not been tested in a large case-control population. Methods: We used VNTRseek to genotype over 200,000 tandem repeats in 9,501 cases and controls from the Alzheimer's Disease Sequencing Project. We first identified limiting factors of this analysis and then examined the association of VNTRs with AD diagnosis in a subset of non-Hispanic White participants. Results: We found that VNTRs were highly associated with areas of the genome with a high number of previously identified variants. From our case-control analysis, we identified 9 VNTRs with a repeat allele length associated with LOAD including VNTRs on DSC3, NR2E3, CCNY, PKP4, GRAP, and MAP6. Discussion: We were able to show the feasibility of this new type of analysis in large-scale whole-genome sequencing data and identify promising VNTRs that are associated with LOAD.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".