Linkage and Association Studies of Joint Morbidity from Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between genetic variations of rheumatoid arthritis (RA) susceptibility in terms of joint morbidity. METHODS: We used data from Genetic Analysis Workshop 15. The Illumina linkage panel IV included 5858 single-nucleotide polymorphisms (SNP), with 5744 SNP passing quality control filters. The phenotypic variables analyzed were the level of rheumatoid factor (RF) and score on the Joint Alignment and Motion (JAM) scale. We modified the scale, dividing by RF values relevant to disease severity. Linkage analysis for affected sibling pairs was done using the MERLIN program, and family-based association tests were carried out using PLINK and FBAT software. RESULTS: We found a high peak (LOD = 3.29; NPL Z = 4.07) near the HLA-DRB1 region on chromosome 6. The linkage at 6p24 at rs1410766 [LOD = 2.66; nonparametric linkage (NPL) Z = 3.23] was statistically significant. Two other regions also showed possible linkage peaks: chromosome 7q30 at rs322812 (LOD = 2.47; NPL Z = 3.39) and chromosome 15p34 at rs347117 (LOD = 1.95; NPL Z = 2.80). For the family-based association study, 7 SNP related to clinical RA severity were detected. CONCLUSION: Genetic variations may lead to an enhanced risk of joint damage and increased levels of RF. Further studies are needed to elucidate the roles of other genes involved in RA and to explore whether the clinical signs of RA are associated with particular genetic variations.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".