Comparison of polygenic risk scores for coronary artery disease highlights obstacles to overcome for clinical use
Bibliographic record
Abstract
Abstract Polygenic risk scores, or PRS, are a tool to estimate individuals’ liabilities to a disease or trait measurement based solely on genetic information. One commonly discussed potential use is in the clinic to identify people who are at greater risk of developing a disease. In this paper, we compare three PRS models that incorporate a large number of genetic markers for coronary artery disease (CAD). In the UK Biobank, the cohort which was used at some point in the creation or validation of each score, we calculated the association between CAD, the scores, and population structure for the white British subset. After adjusting for geographic and socioeconomic factors, CAD was not associated with the first principal components of genetic diversity, which reflect fine-scale population structure. In contrast, all three scores were confounded by these genetic components, highlighting that PRS may be influenced by genetic factors not directly causal for CAD, thereby raising concerns about their biases in clinical application.Furthermore, we investigated the differences in risk stratification using four different UK Biobank assessment centers as separate cohorts, and tested how missing genetic data affected risk stratification through simulation. We show that missing data impact classification for extreme individuals for high- and low-risk, and quantiles of risk are sensitive to individual-level genotype missingness. Distributions of scores varied between assessment centers, revealing that thresholding based on quantiles can be problematic for consistency across centers and populations. Based on these results, we discuss potential avenues of improvements of PRS methodologies for usage in clinical practice.
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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 | 0.190 | 0.407 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".