Empowering rare variant burden-based gene-trait association studies via optimized computational predictor choice
Bibliographic record
Abstract
Abstract Background Causal gene/trait relationships can be identified via observation of an excess (or reduced) burden of rare variation in a given gene within humans who have that trait. Although computational predictors can improve the power of such ‘burden’ tests, it is unclear which are optimal for this task. Method Using 140 gene-trait combinations with a reported rare-variant burden association, we evaluated the ability of 20 computational predictors to predict human traits. We used the best-performing predictors to increase the power of genome-wide rare variant burden scans based on ∼450K UK Biobank participants. Results Two predictors—VARITY and REVEL—outperformed all others in predicting human traits in the UK Biobank from missense variation. Genome-scale burden scans using the two best-performing predictors identified 1,038 gene-trait associations (FDR < 5%), including 567 (55%) that had not been previously reported. We explore 54 cardiovascular gene-trait associations (including 15 not reported in other burden scans) in greater depth. Conclusions Rigorous selection of computational missense variant effect predictors can improve the power of rare-variant burden scans for human gene-trait associations, yielding many new associations with potential value in informing mechanistic understanding and therapeutic development. The strategy we describe here is generalizable to future computational variant effect predictors, traits and organisms.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".