Editorial: Statistical methods for genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) and their applications
Bibliographic record
Abstract
The Genome-Wide Association Study (GWAS) has proven highly successful in identifying millions of risk loci associated with various diseases in the past 15 years (1). With the rapid accumulation of GWAS summary-level data, biologists now have expanded opportunities to uncover new diseaseassociated variants and gain insights into the mechanisms underlying complex human diseases(2-4). While GWAS is a powerful tool, it faces challenges in pinpointing candidate disease risk genes. For instance, many disease-associated variants reside in non-coding regions, complicating the Another critical aspect of identifying disease-associated genes involves prioritizing trait-specific 57 tissues, which may lead to differences in gene expression and variant regulation. To address this, 58Ghaffar and Nyholt developed a method called genome-wide imputed differential expression 59 enrichment (GIDEE). GIDEE prioritizes pathogenic tissues by analyzing the enrichment of 60 differentially expressed genes in each tissue. Additionally, the relationship between diseases plays a 61 key role in identifying variants shared across multiple traits or diseases.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.014 | 0.029 |
| Insufficient payload (model declined to judge) | 0.023 | 0.024 |
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".