A Genetic Predictive Model Estimating the Risk of Developing Adolescent Idiopathic Scoliosis

BACKGROUND: Previous GWASs have revealed several susceptible variants associated with adolescent idiopathic scoliosis (AIS). Risk prediction based on these variants can potentially improve disease prognosis. We aimed to evaluate the combined effects of genetic factors on the development of AIS and to further develop a genetic predictive model. METHODS: A total of 914 AIS patients and 1441 normal controls were included in the discovery stage, which was followed by the replication stage composed of 871 patients and 1239 controls. Genotyping assay was performed to analyze 10 previously reported susceptible variants, including rs678741 of LBX1, rs241215 of AJAP1, rs13398147 of PAX3, rs16934784 of BNC2, rs2050157 of GPR126, rs2180439 of PAX1, rs4940576 of BCL2, rs7593846 of MEIS1, rs7633294 of MAGI1 and rs9810566 of TNIK. Logistic regression analysis was performed to generate a risk predictive model. The predicted risk score was calculated for each participant in the replication stage. RESULTS: The association of the 10 variants with AIS was successfully validated. The established model could explain approximately 7.9% of the overall variance. In the replication stage, patients were found to have a remarkably higher risk score as compared to the controls (44.2 ± 14.4 vs. 33.9 ± 12.5, p <0.001). There was a remarkably higher proportion of the risk score i.e. >40 in the patients than in the controls (59% vs. 28.9%, p <0.001). CONCLUSION: Risk predictive model based on the previously reported genetic variants has a remarkable discriminative power. More clinical and genetic factors need to be studied, to further improve the proba-bility to predict the onset of AIS.