BACKGROUND: A genetic predisposition has been suggested to contribute to the risk for development of rotator cuff disease on the basis of observed family clusters of close relatives. We used a population-based resource combining genealogical data for Utah with clinical diagnosis data from a large Utah hospital to test the hypothesis of excess familial clustering for rotator cuff disease. METHODS: The Utah Population Database contains combined health and genealogical data on over two million Utah residents. Current Procedural Terminology, Fourth Revision, codes (29827, 23412, 23410, and 23420) and International Classification of Diseases, Ninth Revision, codes (726.1, 727.61, and 840.4) entered in patient records were used to identify patients with rotator cuff disease. We tested the hypothesis of excess familial clustering using two well-established methods (the Genealogical Index of Familiality test and the estimation of relative risks in relatives) in the overall study group (3091 patients) and a subgroup of the study group diagnosed before the age of forty years (652 patients). RESULTS: The Genealogical Index of Familiality test in patients diagnosed before the age of forty years showed significant excess relatedness for individuals with rotator cuff disease in close and distant relationships (as distant as third cousins) (p = 0.001). The relative risk of rotator cuff disease in the relatives of patients diagnosed before the age of forty years was significantly elevated for second degree (relative risk = 3.66, p = 0.0076) and third degree (relative risk = 1.81, p = 0.0479) relatives. CONCLUSIONS: We analyzed a set of patients with diagnosed rotator cuff disease and a known genealogy to describe the familial clustering of affected individuals. The observations of significant excess relatedness of patients and the significantly elevated risks to both close and distant relatives of patients strongly support a heritable predisposition to rotator cuff disease.
BACKGROUND: A genetic predisposition has been suggested to contribute to the risk for development of rotator cuff disease on the basis of observed family clusters of close relatives. We used a population-based resource combining genealogical data for Utah with clinical diagnosis data from a large Utah hospital to test the hypothesis of excess familial clustering for rotator cuff disease. METHODS: The Utah Population Database contains combined health and genealogical data on over two million Utah residents. Current Procedural Terminology, Fourth Revision, codes (29827, 23412, 23410, and 23420) and International Classification of Diseases, Ninth Revision, codes (726.1, 727.61, and 840.4) entered in patient records were used to identify patients with rotator cuff disease. We tested the hypothesis of excess familial clustering using two well-established methods (the Genealogical Index of Familiality test and the estimation of relative risks in relatives) in the overall study group (3091 patients) and a subgroup of the study group diagnosed before the age of forty years (652 patients). RESULTS: The Genealogical Index of Familiality test in patients diagnosed before the age of forty years showed significant excess relatedness for individuals with rotator cuff disease in close and distant relationships (as distant as third cousins) (p = 0.001). The relative risk of rotator cuff disease in the relatives of patients diagnosed before the age of forty years was significantly elevated for second degree (relative risk = 3.66, p = 0.0076) and third degree (relative risk = 1.81, p = 0.0479) relatives. CONCLUSIONS: We analyzed a set of patients with diagnosed rotator cuff disease and a known genealogy to describe the familial clustering of affected individuals. The observations of significant excess relatedness of patients and the significantly elevated risks to both close and distant relatives of patients strongly support a heritable predisposition to rotator cuff disease.
Authors: L A Cannon-Albright; D E Goldgar; L J Meyer; C M Lewis; D E Anderson; J W Fountain; M E Hegi; R W Wiseman; E M Petty; A E Bale Journal: Science Date: 1992-11-13 Impact factor: 47.728
Authors: A Kamb; D Shattuck-Eidens; R Eeles; Q Liu; N A Gruis; W Ding; C Hussey; T Tran; Y Miki; J Weaver-Feldhaus Journal: Nat Genet Date: 1994-09 Impact factor: 38.330
Authors: Frederick Albright; Kathleen Light; Alan Light; Lucinda Bateman; Lisa A Cannon-Albright Journal: BMC Neurol Date: 2011-05-27 Impact factor: 2.474
Authors: Alpesh A Patel; William Ryan Spiker; Michael Daubs; Darrel Brodke; Lisa A Cannon-Albright Journal: J Bone Joint Surg Am Date: 2011-02-02 Impact factor: 5.284