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

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.Author summary: Despite elimination efforts, the reported number of new leprosy patients has been relatively stable during the past decade throughout the world. Solid evidence exists that individuals living in close proximity to patients are at an increased risk of developing leprosy, thus identifying the contact individuals who are at a higher risk of developing leprosy is an important aspect of disease control. In the last decade, genome-wide association studies (GWASs) have identified multiple genetic variants associated with leprosy in the Chinese population, however, the combined impact of these variants for leprosy risk prediction remains unclear. The goal of our study was to utilize the genetic variants to construct a risk prediction model in a Chinese set, and further assess the performance of the model in identifying higher-risk contact subjects in an independent set. We developed risk prediction models for leprosy based on GRS encompassing 25 GWAS-derived variants with good discriminatory capability (AUC = 0.743). When compared to the individuals in the high-risk group (GRS > 28.06) and low-risk group (GRS≤18.17), the former had a 24.65 times higher risk for developing leprosy than the latter, which demonstrated a considerable value for risk stratification in leprosy. Our results may assist physicians to identify higher-risk leprosy contact subjects for disease interventions.