Accurate cross-platform GWAS analysis via two-stage imputation
Bibliographic record
Abstract
Abstract In genome-wide association studies (GWAS), combining independent case-control cohorts has been successful in increasing power for meta and joint analyses. This success sparked interest in extending this strategy to GWAS of rare and common diseases using existing cases and external controls. However, heterogeneous genotyping data can cause spurious results. To harmonize data, we propose a new method, two-stage imputation (TSIM), where cohorts are imputed separately, merged on intersecting high-quality variants, and imputed again. We show that TSIM controls imputation-derived errors and type I error. Merging arthritis cases and UK Biobank controls using TSIM, we replicated known associations without introducing false positives. Furthermore, GWAS using TSIM performed comparably to the meta-analysis of nephrotic syndrome cohorts genotyped on five different platforms, demonstrating TSIM’s ability to harmonize heterogeneous genotyping data. With the plethora of publicly available genotypes, TSIM provides a GWAS framework that harmonizes heterogeneous data, enabling analysis of small and case-only cohorts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".