Performance of polygenic risk scores in screening, prediction, and risk stratification
Bibliographic record
Abstract
Abstract Background The clinical value of polygenic risk scores has been questioned. We sought to clarify performance in population screening, individual risk prediction and population risk stratification by analysing 926 polygenic risk scores for 310 diseases from the Polygenic Score (PGS) Catalog. Methods Polygenic risk scores in the PGS Catalog are reported using hazard ratios or odds ratios per standard deviation, or the area under the receiver operating characteristic curve sometimes expressed as the C -index. We used this information to produce estimates of performance in: (a) population screening — by calculating the detection rate ( DR 5 ) for a 5% false positive rate ( FPR ) and the population odds of becoming affected given a positive result ( OAPR ); (b) individual risk prediction — by calculating the individual odds of becoming affected for a person with a particular polygenic score; and (c) population risk stratification — by calculating the odds of becoming affected for groups of individuals in different portions of a polygenic risk score distribution. We use coronary artery disease and breast cancer as illustrative examples. Findings Population screening performance : The median DR 5 for all polygenic risk scores and all diseases studied was 11% [interquartile range 8 − 18%]. The median DR 5 was 12% [9 − 19] for polygenic risk scores for CAD and 10% [9 − 12] for breast cancer, with population OAPRs of 1:8 and 1: 21 respectively, with background 10-year odds of 1:19 and 1:41 respectively, which are typical for these diseases at age 50. Individual risk prediction : The corresponding 10-year odds of becoming affected for individuals aged 50 with a polygenic risk score at the 2.5 th , 25 th , 75 th and 97.5 th centile were 1:54, 1:29, 1:15, and 1:8 for CAD and 1:91, 1:56, 1:34, and 1:21 for breast cancer. Population risk stratification : At age 50, stratifying into quintile groups of CAD risk yielded 10-year odds of 1: 41 and 1: 11 for the lowest and highest quintile groups respectively. The 10-year odds was 1: 7 for the upper 2.5% of the polygenic risk score distribution for CAD, a group that contributed 7% of cases. The corresponding estimates for breast cancer were 1: 72 and 1: 26 for lowest and highest quintiles; and 1:19 for the upper 2.5% of the distribution, which contributed 6% of cases. Interpretation Polygenic risk scores perform poorly in population screening, individual risk prediction, and population risk stratification. Funding British Heart Foundation; UK Research and Innovation; National Institute of Health and Care Research.
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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 | 0.102 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".