Metabolites mediate genetic effects on disease in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Abstract Understanding the biological mechanisms linking genetic variants to disease risk is essential for advancing precision health. We have developed a causal mediation analysis framework, the C-MAPLE (Causal Mediation Analysis of Pathways Linking Exposures) method, to identify disease-causing pathways for which the effect of genetic variants is mediated through metabolites to impact age-related diseases. Unlike Mendelian randomization, our approach is robust to horizontal pleiotropy, and models multiple mediators and interactions between genetic variants and metabolites simultaneously. To ensure robust model selection, we incorporate least absolute shrinkage and selection operator (LASSO) with stability selection, which can effectively select relevant mediators even in the presence of unmeasured confounding. We also introduce a dynamic adjustment to the number of bootstrap trials to reduce computational burden during uncertainty estimation. Applying this novel framework to the Canadian Longitudinal Study on Aging, we identified 190 potential causal links involving 108 genetic variants, 176 metabolites, and 6 age-related diseases. Our method and findings highlight the utility of causal mediation analysis in uncovering metabolite-mediated genetic mechanisms. This method, combined with large-scale population data sets, has the potential to revolutionize the identification of targets for downstream clinical research, and the development of personalized disease prevention, interventions, and therapeutics.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".