Positioning Personal Polygenic Risk score against the population background
Bibliographic record
Abstract
Abstract The polygenic risk scores (PRS) approach has been widely used across different traits for estimating polygenic risk, pleiotropy and disease prediction, but mostly in European populations. The predictive ability of the PRS in non-European populations is currently limited due to the lack of genetic research performed in populations of non-European ancestry. One of the main challenges of the practical use of PRS is to place an individual’s personal score in the context of the PRS distribution in the underlying population. In this paper we present an approach for estimating the parameters of the PRS distribution in a population using summary information from public data. Unstandardized PRS are usually not directly comparable even between European studies. Our approach can be used for standardisation whilst accounting for genotyping platforms, data quality and ancestry. It can be applied to assessing polygenic disease risk for individuals from a European population for any complex genetic disorder and, assuming that most of the disease risk loci are likely to be shared between populations, to estimating the disease risk for individuals from other populations. We demonstrate the precision of our method with simulations. We show the utility of our estimates in application to Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We present population specific PRSs for different populations using 1000 Genomes data.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".