Multivariate genome-wide association analysis identifies novel and relevant variants associated with anterior cruciate ligament rupture risk in the dog model
Bibliographic record
Abstract
BACKGROUND: Anterior cruciate ligament rupture (ACLR) is a debilitating and potentially life-changing condition in humans, as there is a high prevalence of early-onset osteoarthritis after injury. Identification of high-risk individuals before they become patients is important, as post-treatment lifetime burden of ACLR in the USA ranges from $7.6 to $17.7 billion annually. ACLR is a complex disease with multiple risk factors including genetic predisposition. Naturally occurring ACLR in the dog is an excellent model for human ACLR, as risk factors and disease characteristics in humans and dogs are similar. In a univariate genome-wide association study (GWAS) of 237 Labrador Retrievers, we identified 99 ACLR candidate loci. It is likely that additional variants remain to be identified. Joint analysis of multiple correlated phenotypes is an underutilized technique that increases statistical power, even when only one phenotype is associated with the trait. Proximal tibial morphology has been shown to affect ACLR risk in both humans and dogs. In the present study, tibial plateau angle (TPA) and relative tibial tuberosity width (rTTW) were measured on bilateral radiographs from purebred Labrador Retrievers that were recruited to our initial GWAS. We performed a multivariate genome wide association analysis of ACLR status, TPA, and rTTW. RESULTS: Our analysis identified 3 loci with moderate evidence of association that were not previously associated with ACLR. A locus on Chr1 associated with both ACLR and rTTW is located within ROR2, a gene important for cartilage and bone development. A locus on Chr4 associated with both ACLR and TPA resides within DOCK2, a gene that has been shown to promote immune cell migration and invasion in synovitis, an important predictor of ACLR. A third locus on Chr23 associated with only ACLR is located near a long non-coding RNA (lncRNA). LncRNA's are important for regulation of gene transcription and translation. CONCLUSIONS: These results did not overlap with our previous GWAS, which is reflective of the different methods used, and supports the need for further work. The results of the present study are highly relevant to ACLR pathogenesis, and identify potential drug targets for medical treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".