Evaluation of 19 Autoimmune Disease-associated Loci with Rheumatoid Arthritis in a Colombian Population: Evidence for Replication and Gene-Gene Interaction
Bibliographic record
Abstract
OBJECTIVE: Recent studies have identified several common genes associated with multiple autoimmune diseases that support the hypothesis of the presence of shared or general autoimmunity genes. However, most of this work has been performed in populations of white origin. The main objectives of this study are to replicate the genotype-phenotype correlation between 19 such variants and rheumatoid arthritis (RA), and to evaluate gene-gene interactions between these genes in individuals from an ethnically homogenous nonwhite Colombian population. METHODS: Nineteen single-nucleotide polymorphisms (SNP) from 16 genes/loci were genotyped in 353 RA cases and 368 controls. For each SNP, allelic and genotype-based association tests were applied to evaluate genotype-phenotype correlation. Permutation-based tests were used to validate the statistical significance. Gene-gene interactions were assessed by logistic regression. RESULTS: We replicated the genetic association with rs13277113 (p = 0.0009, OR 1.46) and rs2736340 (p = 0.0001, OR 1.63) from C8orf13-BLK (8p23.1, associated with RA and systemic lupus erythematosus), and rs763361 (p = 0.03) from CD226 (18q22.3, associated with multiple sclerosis and type 1 diabetes) in the Colombian population. The population-attributable risks were estimated as 27%, 34%, and 16% for rs13277113, rs2736340, and rs763361, respectively. We also detected evidence for gene-gene interaction between SNP in MMEL1 (rs3890745) and C80rf13-BLK (rs13277113; p = 0.0002). CONCLUSION: Our results demonstrate that the IL2/IL21 region, C8orf13-BLK, and CD226 influence RA in Colombians, and RA shares some of the pathogenic mechanisms associated with other autoimmune 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".