Genetic Determinants of Radiographic Knee Osteoarthritis in African Americans
Bibliographic record
Abstract
OBJECTIVE: The etiology of knee osteoarthritis (OA), the most common form of arthritis, is complex and may differ by race or ethnicity. In recent years, genetic studies have identified many genetic variants associated with OA, but nearly all the studies were conducted in European whites and Asian Americans. Few studies have focused on the genetics of knee OA in African Americans. METHODS: We performed a genome-wide association study of radiographic knee OA in 1217 African Americans from 2 North American cohort studies: 590 subjects from the Johnston County Osteoarthritis Project and 627 subjects from the Osteoarthritis Initiative. Analyses were conducted in each cohort separately and combined in an inverse variance fixed effects metaanalysis, which were then included in pathway analyses. We additionally tested 12 single-nucleotide polymorphisms robustly associated with OA in European white populations for association in African Americans. RESULTS: ). In pathway analyses, dorsal/ventral neural tube patterning and iron ion transport pathways were significantly associated with knee OA in African Americans (false discovery rate < 0.05). We found no evidence that previously reported OA susceptibility variants in European whites were associated with knee OA in African Americans. CONCLUSION: These results highlight differences in the genetic architecture of knee OA between African American and European whites. This finding underscores the need to include more diverse populations in OA genetics studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".