Interactive Effects of Telomere Length and Genetic Variants on Alzheimer Disease Risk Across Multiple Ancestral Populations
Bibliographic record
Abstract
Abstract Background Telomere length (TL), a biomarker of biological aging, but its association with Alzheimer’s disease (AD) remains unclear. Methods We estimated TL in whole-genome sequencing data from 35,014 Alzheimer’s Disease Sequencing Project participants using TelSeq, which after quality control yielded a dataset including 6,973 persons of European ancestry (EA), 4,188 African Americans (AA), 4,005 Caribbean Hispanics (CH), and 4,170 Native American Hispanics (NAH). TL was log-transformed, adjusted for age and blood cell counts, and z-scaled. Scaled TL was dichotomized into long and short groups according to the median. An AD GWAS for the interaction of TL with variants having a minor allele count >20 was performed in each ancestry group using logistic regression models including SNP and TL main effects and a SNP×TL interaction term. Results AD risk was associated with shorter TL (β = -0.18, P < 2×10 -16 ). Longer TL was associated with dosages of APOE ε2 ( P <5.08×10 -8 ) and APOE ε4 ( P =2.10×10 -2 ). In the EA group, genome-wide significant (GWS) TLxSNP interactions were identified for variants in SEMA6A (P =1.42×10 -8 ) and LOC105378654 (P =4.17×10 -8 ), between IL15 and INPP4B (P =1.77×10 -8 ) and upstream of RP11-2N5.2 ( P =4.60×10 -8 ). In the NAH group, GWS interactions were observed with an intronic variant in BSN ( P =3.26×10 -8 ) and missense variant in MST1 ( P =3.26×10 -8 ). In the total sample, interactions with variants between CTD-2160D9.1 and EEF1A1P20 ( P <1.19×10 -8 ), in TBC1D22A ( P =1.06×10 -8 ) and in PLK1 ( P =3.28×10 -8 ) were GWS. Conclusion We identified variants that significantly impact AD risk through their interaction with TL, suggesting that TL maintenance pathways may be central to AD pathogenesis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".