Bibliographic record
Abstract
Hematopoiesis is a dynamic, yet tightly regulated process continuously replenishing populations of cells, including those with immune functions, that circulate in peripheral blood. Aging is associated with changes in hematopoiesis, resulting in altered composition and reduced function of immune cells, increasing risk of disease in the elderly. However, some elderly individuals remain healthy throughout their lifetime, which provides an opportunity to identify mechanisms that maintain healthy hematopoiesis. The underlying genomic architecture and molecular mechanisms that regulate hematopoiesis among the healthy aging population have yet to be studied comprehensively at the population level. Using genetic and functional genomic approaches, I utilized large population cohorts to identify genetic and transcriptomic factors associated with variation in blood aging. First, I investigated the impact of pleiotropic variants on blood and age-related phenotypes because they have the potential to simultaneously increase an individual’s risk for multiple phenotypes and chronic conditions. Out of more than 65,000 candidates, I identified six pleiotropic variants associated with diverse traits including blood cell counts, metabolic and cardiovascular disease, and anthropometric traits. I found that pleiotropic variants are more likely to be coding mutations and are highly deleterious yet remain at relatively common allele frequencies, supporting the mutation accumulation and antagonistic pleiotropy theories of aging. Next, I investigated the genomic architecture and mechanisms associated with variation in blood aging. I selected 400 individuals from the extremities of the age and immune health spectrums and performed single cell RNA sequencing on more than 500 000 cells, together with whole genome sequencing and chromatin profiling on bulk CD45+ cells to reveal genetic, epigenetic, and transcriptional factors underlying healthy immune aging. I show that sex and cell-type specific transcriptional signatures rather than cell composition differentiate individuals with healthy or unhealthy blood. We also identified 2565 cell-type specific expression quantitative trait loci associated with blood health enriched in innate cell types, suggesting a significant portion of the heritable component to blood aging mainly acts through innate cells. My research demonstrates how natural variation in healthy agers can help to uncover mechanisms that prevent or protect dysregulated immune function during aging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".