Attempt to replicate the published osteoarthritis-associated genetic variants in the Newfoundland & Labrador Population
Bibliographic record
Abstract
Objective: Over 200 genes have been reported to be associated with osteoarthritis (OA), but most of them have not been replicated in an independent sample. Using the newly collected cohort from a genetically isolated population - the Newfoundland and Labrador population, we attempted to replicate 105 previously reported OAassociated SNPs. \n \nMethods: A case-control study design was utilized in this study. Patients undergoing total hip/knee joint replacements due to severe OA were collected as cases. A group of healthy individuals with no evidence of OA was used as control.105 SNPs were genotyped either by Sequenom iPLEX Gold method or Illumina GWAS genotyping platform. The cross-reference was performed on both methods in a subset of samples for genotyping quality control. A logistic regression model was used to test for associations between the SNPs and OA. \n \nResults: A total of 126 cases and 348 healthy controls were \nincluded in the final analysis. OA Patients were on average 9 years older than healthy controls (p<0.0001), but there was no difference in BMI. We were unable to replicate the previously reported associations. Two SNPs, rs2294995 (COL9A3), and rs1049007 (BMP2) showed an association with p<0.05, but the significance did not survive the Bonferroni multiple testing correction. \n \nConclusion: A lack of replication might be due to study design, complexity of OA, method of OA ascertainment, populations studied, or false positives in the original publications. A study with larger sample is needed to confirm the two possible SNPs with OA.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".