Uncovering the multivariate genetic architecture of frailty with genomic structural equation modeling
Bibliographic record
Abstract
Frailty is a multifaceted clinical state associated with accelerated aging and adverse health outcomes. Informed etiological models of frailty hold promise for producing widespread health improvements across the aging population. Frailty is currently measured using aggregate scores, which obscure etiological pathways that are only relevant to subcomponents of frailty. Here we perform a multivariate genome-wide association study of the latent genetic architecture between 30 frailty deficits, which identifies 408 genomic risk loci. Our model includes a general factor of genetic overlap across all deficits, plus six new factors indexing a shared genetic signal across specific groups of deficits. We demonstrate the added clinical and etiological value of the six factors, including predicting frailty in external datasets, highlighting divergent genetic correlations with clinically relevant outcomes and uncovering unique underlying biology linked to aging. We show that nuanced models of frailty are key to understanding its causes and how it relates to worse health. Multivariate genome-wide association analyses of the latent genetic architecture of frailty identify one general factor of genetic overlap across all frailty deficits and six factors indexing a shared genetic signal across specific groups of deficits.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".