Long-read sequencing reveals telomere inheritance patterns from human trios
Bibliographic record
Abstract
Abstract Telomeres are essential for maintaining genomic integrity and are associated with cellular aging and disease, yet the factors influencing their inheritance across generations remain poorly understood. Leveraging PacBio HiFi long-read sequencing and 75 parent-offspring trios (n = 225) from the Genomic Answers for Kids program, we analyzed individual telomeres across chromosomes and their inheritance. Telomere length (TL) varied between chromosome arms in a way that was consistent in parents and offsprings, with average values ranging from 5000 to 8000 base pairs. Maternal and paternal TL together were a strong predictor of child TL (R 2 = 0.59). Notably, using telomeric variant repeats, we developed a tool that enabled allelic tracing for 53.3% of maternally and 49.9% of paternally inherited telomeres. In the child, paternally transmitted alleles were significantly longer than age-matched maternal ones (Δmean = 409 bp, p = 2.6e-05), particularly when from older parents (Δmean = 698 bp, p = 8.9e-05) and at chromosome arms with shorter average TL (Δmean = 752 bp, p = 1.6e-06). These findings reveal parent-of-origin effects and heritable influences on TL, providing novel insights into telomere dynamics and their potential implications in age-related disease susceptibility.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".