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Approved research

Evaluation of genetic and non-genetic risk factors for degenerative rotator cuff disease.

Principal Investigator: Dr Elizabeth Yanik
Approved Research ID: 27034
Approval date: September 15th 2017

Lay summary

Aim 1: We will test associations of genetic variants with degenerative rotator cuff disease risk through a genome-wide association study (GWAS). Aim 2: We will estimate associations of non-genetic risk factors, such as age, smoking, and occupational upper extremity demands, and build a predictive model of degenerative rotator cuff disease including all risk factors. Aim 3: We will evaluate interactions between genetic and non-genetic risk factors for degenerative rotator cuff disease. There is growing evidence that familial predisposition increases rotator cuff disease risk. However, few studies have evaluated associations between specific genes and rotator cuff disease. The only prior GWAS had limited statistical power with <350 rotator cuff disease patients. Furthermore, the influence of genetic risk on effects of non-genetic risk factors is unknown. Results from this study are expected to be useful in identifying individuals who are at high-risk for rotator cuff disease. These individuals might benefit from preventative strategies and early treatment of rotator cuff tears and interventions aimed at modifiable risk factors, such as smoking cessation. Individuals with rotator cuff disease will be identified using hospital ICD-9/10 codes and compared to individuals without rotator cuff disease. We will conduct a GWAS to identify genetic markers significantly associated with rotator cuff disease after adjusting for multiple comparisons. Non-genetic risk factor (ex. age, occupational demand, and smoking) associations with rotator cuff disease will be estimated. Prediction models will be developed. To evaluate interactions between genetic and non-genetic factors, we will test product interaction terms in our models and evaluate risk within strata defined by genetic and non-genetic risk factors. We propose to use the full cohort with genetic information.