Locus category based analysis of a large genome-wide association study of rheumatoid arthritis
Bibliographic record
Abstract
To pinpoint true positive single-nucleotide polymorphism (SNP) associations in a genome-wide association study (GWAS) of rheumatoid arthritis (RA), we categorize genetic loci by external knowledge. We test both the 'enrichment of associated loci' in a locus category and the 'combined association' of a locus category. The former is quantified by the odds ratio for the presence of SNP associations at the loci of a category, whereas the latter is quantified by the number of loci in a category that have SNP associations. These measures are compared with their expected values as obtained from the permutation of the affection status. To account for linkage disequilibrium (LD) among SNPs, we view each LD block as a genetic locus. Positional candidates were defined as loci implicated by earlier GWAS results, whereas functional candidates were defined by annotations regarding the molecular roles of genes, such as gene ontology categories. As expected, immune-related categories show the largest enrichment signal, although it is not very strong. The intersection of positional and functional candidate information predicts novel RA loci near the genes TEC/TXK, MBL2 and PIK3R1/CD180. Notably, a combined association signal is not only produced by immune-related categories, but also by most other categories and even randomly defined categories. The unspecific quality of these signals limits the possible conclusions from combined association tests. It also reduces the magnitude of enrichment test results. These unspecific signals might result from common variants of small effect and hardly concentrated in candidate categories, or an inflated size of associated regions from weak LD with infrequent mutations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".