Personalized polygenic risk prediction and assessment with a Mixture-of-Experts framework
Bibliographic record
Abstract
Abstract With the increasing availability of high quality genomic data from diverse cohorts, polygenic scores (PRS) have become a mainstay of genetic analyses of complex traits and diseases. Despite their proliferation in numerous research domains, a major obstacle to wider adoption in clinical settings has been the well-established heterogeneity in prediction accuracy across a variety of demographic variables, such as age, sex, and genetic ancestry. To address this deficiency, recent research efforts aimed to improve representation in genetic studies and develop stratified PRS inference methods that greatly enhanced accuracy in minority populations. However, with these stratified scores in hand, it remains unclear how to assign the best score, or mixture of scores, for a particular test individual in the clinic. To bridge this gap, we present MoEPRS , an ensemble learning method based on the Mixture-of-Experts framework, that blends the stratified scores using personalized mixing weights to predict the target phenotype. In biobank-scale analyses of 7 complex traits in the UK and CARTaGENE biobanks, we show that MoEPRS generally provides modest improvements in prediction accuracy over single source PRS models and its predictive performance is maintained across biobanks. Furthermore, we demonstrate practical use cases where the model automatically identifies and adapts to diverse sources of heterogeneity in the data, which allows for evaluating the strengths and weaknesses of current polygenic scores across various cohort strata.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".