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The polygenic profiles in participants with achilles tendinopathy and controls
  1. M Posthumus1,
  2. C Saunders1,
  3. A V September1,2,
  4. M Collins1,2,3
  1. 1The University of Cape Town, Cape Town, South Africa
  2. 2IOC Research Centre, Cape Town, South Africa
  3. 3The South African Medical Research Council, Cape Town, South Africa


Introduction Achilles tendinopathy (AT) is a multifactorial condition for which various genetic risk factors have been identified. More specifically, five single nucleotide polymorphisms (SNPs) have been associated with and/or suggested to be implicated in AT. These SNPs are (1) the COL5A1 rs12722, (2) the MMP3 rs679620, (3) the GDF5 rs143383, (4) the IL1B −511, and (5) the IL6 −172 polymorphisms.

Objective This study determines the validity of a polygenic profile, as proposed by Williams and Folland, as a discriminating ‘tool’ for identifying individuals at risk of developing AT.

Methods All five polymorphisms was previously genotyped in 69 South African (SA) Caucasian participants with AT and 93 SA Caucasian control participants (CON) with no previous history of AT. Using the model developed by Williams and Folland (Journal of Physiology, 586.1, 113–21:2008), the ‘total genotype score’ (TGS) was calculated from the combination of each of the five SNPs. Individual genotypes were each given a ‘score’ of 2, 1 or 0. The homozygote genotype most closely associated with an increased risk of AT was given the score ‘2’, heterozygote's were scored 1 and the homozygote most closely associated with reduced risk of AT were scored 0. Receiver operating characteristic (ROC) curves were used to evaluate the ability of the TGS to correctly distinguish participants at high and low risk of AT. Significance was accepted when p<0.05.

Results The TGS of the AT participants (TGS=63.6±15.3) was significantly greater (p<0.001) than the CON participants (TGS=54.3±16.3). ROC analysis showed a significant discriminating accuracy of TGS to identify risk of AT in the SA population (AUC=0.64; 95% CI 0.56 to 0.73; p=0.002).

Conclusion The method of using TGSs to discriminate between individuals at risk of AT and not, seems valid. This method may be used in future multifactorial models developed to identify ‘at-risk’ individuals.

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