Shared Genetic Architecture Between Rheumatoid Arthritis and Varying Osteoporotic Phenotypes
Bibliographic record
Abstract
Rheumatoid arthritis (RA) and low bone mineral density (BMD), an indicator of osteoporosis (OP), appear epidemiologically associated. Shared genetic factors may explain this association. This study aimed to investigate the presence of pleiotropy to clarify the potential genetic association between RA and OP. We examined BMDs at varying skeletal sites reported in UK Biobank as well as OP fracture acquired from the Genetic Factors for Osteoporosis (GEFOS) Consortium and the TwinsUK study. PRSice-2 was used to assess the potential shared genetic overlap between RA and OP. The presence of pleiotropy was examined using colocalization analysis. PRSice-2 revealed that RA was significantly associated with OP fracture (β = 351.6 ± 83.9, p value = 2.76E-05), total BMD (β = -1763.5 ± 612.8, p = 4.00E-03), spine BMD (β = -919.8 ± 264.6, p value = 5.09E-04), and forearm BMD (β = -66.09 ± 31.40, p value = 3.53E-02). Through colocalization analysis, the same causal genetic variants, associated with both RA and OP, were apparent in 12 genes: PLCL1, BOLL, AC011997.1, TNFAIP3, RP11-158I9.1, CDK6, CHCHD4P2, RP11-505C13.1, PHF19, TRAF1, C5, and C11orf49 with moderate posterior probabilities (>50%). Pleiotropy is involved in the association between RA and OP phenotypes. These findings contribute to the understanding of disease mechanisms and provide insight into possible therapeutic advancements and enhanced screening measures. © 2021 American Society for Bone and Mineral Research (ASBMR).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".