A scalable framework for identifying allelic series from summary statistics
Bibliographic record
Abstract
Genes where a dose-response relationship exists between functionality and phenotypic impact are appealing therapeutic targets, as the effects of pharmacological modulation can be predicted from natural genetic variation. We refer to such genes as harboring allelic series and have introduced the rare coding-variant allelic series test (COAST) for their identification. The original COAST required access to individual-level data. However, these data are often unavailable due to privacy or logistical constraints. Meanwhile, single-variant summary statistics of the type produced by genome-wide association studies are plentiful. Here, we introduce COAST-SS, an extension of COAST that accepts standard summary statistics as input. As a running example, we consider identifying allelic series for circulating lipid traits, drawing on data from the UK Biobank, the Million Veteran Program, and the Trans-Omics of Precision Medicine Program. Through extensive analyses of real and simulated data, we demonstrate that COAST-SS provides p values effectively equivalent to those from the original COAST. Interestingly, we find that when linkage disequilibrium (LD) is low, as is expected among rare variants, COAST-SS is robust to misspecification of the LD matrix. We explore several strategies for annotating the pathogenicity of variants supplied to COAST-SS, finding that they often yield similar power for detecting candidate allelic series. Lastly, we employ COAST-SS to screen for lipid-trait allelic series in a meta-analyzed cohort of up to 840,000 subjects. COAST-SS has been incorporated into the publicly available AllelicSeries R package.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".