Prediction of leprosy in the Chinese population based on a weighted genetic risk score.

• Main Authors: Na Wang (Author), Zhenzhen Wang (Author), Chuan Wang (Author), Xi'an Fu (Author), Gongqi Yu (Author), Zhenhua Yue (Author), Tingting Liu (Author), Huimin Zhang (Author), Lulu Li (Author), Mingfei Chen (Author), Honglei Wang (Author), Guiye Niu (Author), Dan Liu (Author), Mingkai Zhang (Author), Yuanyuan Xu (Author), Yan Zhang (Author), Jinghui Li (Author), Zhen Li (Author), Jiabao You (Author), Tongsheng Chu (Author), Furong Li (Author), Dianchang Liu (Author), Hong Liu (Author), Furen Zhang (Author)
• Format: Book
• Published: Public Library of Science (PLoS), 2018-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1935-2727; 1935-2735; 10.1371/journal.pntd.0006789
• Keywords: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270
• Source: PLoS Neglected Tropical Diseases, Vol 12, Iss 9, p e0006789 (2018)

Genome wide association studies (GWASs) have revealed multiple genetic variants associated with leprosy in the Chinese population. The aim of our study was to utilize the genetic variants to construct a risk prediction model through a weighted genetic risk score (GRS) in a Chinese set and to further assess the performance of the model in identifying higher-risk contact individuals in an independent set. The highest prediction accuracy, with an area under the curve (AUC) of 0.743 (95% confidence interval (CI): 0.729-0.757), was achieved with a GRS encompassing 25 GWAS variants in a discovery set that included 2,144 people affected by leprosy and 2,671 controls. Individuals in the high-risk group, based on genetic factors (GRS > 28.06), have a 24.65 higher odds ratio (OR) for developing leprosy relative to those in the low-risk group (GRS≤18.17). The model was then applied to a validation set consisting of 1,385 people affected by leprosy and 7,541 individuals in contact with leprosy, which yielded a discriminatory ability with an AUC of 0.707 (95% CI: 0.691-0.723). When a GRS cut-off value of 22.38 was selected with the optimal sensitivity and specificity, it was found that 39.31% of high risk contact individuals should be screened in order to detect leprosy in 64.9% of those people affected by leprosy. In summary, we developed and validated a risk model for the prediction of leprosy that showed good discrimination capabilities, which may help physicians in the identification of patients coming into contact with leprosy and are at a higher-risk of developing this condition.