Genomic, phenomic, and geographic associations of leukocyte telomere length in the United States
Bibliographic record
Abstract
Abstract Leukocyte telomere length (LTL) is associated with multiple conditions, including cardiovascular diseases and neoplasms, yet their differential associations across diverse individuals are largely unknown. We estimated LTL from blood-derived whole genome sequences in the All of Us Research Program (n=242,494) with diverse backgrounds across the United States. LTL was associated with lifestyle, socioeconomic status, biomarkers, cardiometabolic diseases, and neoplasms with heterogeneity across genetic ancestries and sexes. Geographical analysis revealed that significantly longer LTL clustered in the West Coast and Central Midwest, while significantly shorter LTL clustered in the Southeast in the United States, accounting for age, sex, and genetic ancestry. Genome-wide association study and meta-analysis with the UK Biobank (n=679,972) found 234 non-overlapping loci, of which 36 were novel. We identified 4 novel loci unique to non-European-like populations and one specific to females. Rare variant analysis uncovered 7 novel genes, providing new functional insights. Our study highlighted previously underappreciated contextual heterogeneities of phenomic and genomic associations with LTL.
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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".