Integrative Analyses for Functional Mechanisms Underlying Associations for Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Extensive association analyses including genome-wide association studies (GWAS) and powerful metaanalysis studies have identified a long list of loci associated with rheumatoid arthritis (RA) in very large populations, but most of them established statistical associations of genetic markers and RA only at the DNA level, without supporting evidence of functional relevance. Our study serves as a trial to detect the functional mechanisms underlying associations for RA by searching publicly available datasets and results. METHODS: Based on publicly available datasets and results, we performed integrative analyses (gene relationships across implicated loci analysis, differential gene expression analysis, and functional annotation clustering analysis) and combined them with the expression quantitative trait locus (eQTL) results to dissect functional mechanisms underlying the associations for RA. RESULTS: By searching 2 GWAS, Integrator and PheGenI, we selected 98 RA association results (p < 10(-5)). Among these associations, we found that 8 single-nucleotide polymorphisms (SNP; rs1600249, rs2736340, rs3093023, rs3093024, rs4810485, rs615672, rs660895, and rs9272219) serve as cis-effect regulators of the corresponding eQTL genes (BLK and CD4 in non-HLA region; CCR6, HLA-DQA1, and HLA-DQB1 in HLA region) that also were differentially expressed in RA-related cell groups. These 5 genes are closely related with immune response in function. CONCLUSION: Our results showed the functional mechanisms underlying the associations of 8 SNP and the corresponding genes. This study is an example of mining publicly available datasets and results in validation of significant disease-association results. Using public data resources for integrative analyses may provide insights into the molecular genetic mechanisms underlying human diseases.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".