RegionScan: a comprehensive R package for region-level genome-wide association testing with integration and visualization of multiple-variant and single-variant hypothesis testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Summary RegionScan is designed for scalable genome-wide association testing of both multiple-variant and single-variant region-level statistics, with visualization of the results. For detection of association under various regional architectures, it implements three classes of state-of-the-art region-level tests, including multiple-variant linear/logistic regression (with and without dimension reduction), a variance-component score test, and region-level minP tests. RegionScan also supports the analysis of multi-allelic variants and unbalanced binary phenotypes and is compatible with widely used variant call format (VCF) files for both genotyped and imputed variants. Association testing leverages linkage disequilibrium (LD) structure in pre-defined regions, for example, LD-adaptive regions obtained by genomic partitioning, and accommodates parallel processing to improve computational and memory efficiency. Detailed outputs (with allele frequencies, variant-LD bin assignment, single/joint variant effect estimates and region-level results) and utility functions are provided to assist comparison, visualization, and interpretation of results. Thus, RegionScan analysis offers valuable insights into region-level genetic architecture, which supports a wide range of potential applications. Availability and implementation RegionScan is freely available for download on GitHub (https://github.com/brossardMyriam/RegionScan).
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it