A flexible pipeline for reproducible exome-wide rare variant gene-trait associations
Bibliographic record
Abstract
Abstract Summary Exome-wide gene-burden association studies are widely used to assess gene – trait relationships. By focusing on coding variants, such analyses can directly quantify the magnitude and direction of a gene’s effect on a given trait across the proteome, information that cannot be easily derived from genome-wide associate studies. However, the lack of a standardized workflow poses a significant challenge for reproducibility. Here, we provide a customizable workflow implemented in Python 3 and Nextflow for performing exome-wide rare variant gene – trait association testing. We demonstrated its utility by replicating three recent studies. This workflow will also serve as a framework for performing similar analyses in a standardized and systematic manner. Availability and Implementation This workflow is publicly available at https://github.com/richardslab/EXWAS_pipeline under the MIT license. Supplementary information Supplementary information will be made available online.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.022 |
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".