Whole-Exome Sequencing of an Exceptional Longevity Cohort
Bibliographic record
Abstract
Centenarians represent a unique cohort to study the genetic basis for longevity and factors determining the risk of neurodegenerative disorders, including Alzheimer's disease (AD). The estimated genetic contribution to longevity is highest in centenarians and super-cententenarians, but few genetic variants have been shown to clearly impact this phenotype. While the genetic risk for AD and other dementias is now well understood, the frequency of known dementia risk variants in centenarians is not fully characterized. To address these questions, we performed whole-exome sequencing on 100 individuals of 98-108 years age in search of genes with large effect sizes towards the exceptional aging phenotype. Overall, we were unable to identify a rare protein-altering variant or individual genes with an increased burden of rare variants associated with exceptional longevity. Gene burden analysis revealed three genes of nominal statistical significance associated with extreme aging, including LYST, MDN1, and RBMXL1. Several genes with variants conferring an increased risk for AD and other dementias were identified, including TREM2, EPHA1, ABCA7, PLD3, MAPT, and NOTCH3. Larger centenarian studies will be required to further elucidate the genetic basis for longevity, and factors conferring protection against age-dependent neurodegenerative syndromes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".