Single-Nucleotide Polymorphisms Related to Leprosy Risk and Clinical Phenotypes Among Chinese Population.

BACKGROUND: Genome-wide association studies (GWASs) have identified some immune-related single-nucleotide polymorphisms (SNPs) to be associated with leprosy. METHODS: This study investigated the association of 17 SNPs based on previously published GWAS studies with susceptibility to leprosy, different polar forms and immune states of leprosy in a case-control study from southwestern China, including 1344 leprosy patients and 2732 household contacts (HHCs) (1908 relatives and 824 genetically unrelated contact individuals). The differences of allele distributions were analyzed using chi-squared analysis and logistic regression. RESULTS: After adjusting covariate factors, rs780668 and rs3764147 polymorphisms influenced susceptibilities to genetically related or unrelated leprosy contact individuals. rs142179458 was associated with onset early cases, rs73058713 A allele and rs3764147 A allele increased the risk of reversal reaction, while rs3764147 G allele had higher risk to present lepromatous leprosy and erythema nodosum leprosum. CONCLUSION: Our results demonstrated that genetic variants in the LACC1, HIF1A, SLC29A3 and CDH18 genes were positively correlated with the occurrence of leprosy and leprosy clinical phenotypes, providing new insights into the immunogenetics of the disease.

Predictive nomogram for leprosy using genetic and epidemiological risk factors in Southwestern China: Case-control and prospective analyses.

BACKGROUND: There is a high incidence of leprosy among house-contacts compared with the general population. We aimed to establish a predictive model using these genetic factors along with epidemiological factors to predict leprosy risk of leprosy household contacts (HHCs). METHODS: Weighted genetic risk score (wGRS) encompassing genome wide association studies (GWAS) variants and five non-genetic factors were examined in a case-control design associated with leprosy risk including 589 cases and 647 controls from leprosy HHCs. We constructed a risk prediction nomogram and evaluated its performance by concordance index (C-index) and calibration curve. The results were validated using bootstrap resampling with 1000 resamples and a prospective design including 1100 HHCs of leprosy patients. FINDING: The C-index for the risk model was 0·792 (95% confidence interval [CI] 0·768-0·817), and was confirmed to be 0·780 through bootstrapping validation. The calibration curve for the probability of leprosy showed good agreement between the prediction of the nomogram and actual observation. HHCs were then divided into the low-risk group (nomogram score ≤ 81) and the high-risk group (nomogram score > 81). In prospective analysis, 12 of 1100 participants had leprosy during 63 months' follow-up. We generated the nomogram for leprosy in the validation cohort (C-index 0·773 [95%CI 0·658-0·888], sensitivity75·0%, specificity 66·8%). Interpretation The nomogram achieved an effective prediction of leprosy in HHCs. Using the model, the risk of an individual contact developing leprosy can be determined, which can lead to a rational preventive choice for tracing higher-risk leprosy contacts. FUNDING: The ministry of health of China, ministry of science and technology of China, Chinese academy of medical sciences, Jiangsu provincial department of science and technology, Nanjing municipal science and technology bureau.

Single-Nucleotide Polymorphisms in Genes Predisposing to Leprosy in Leprosy Household Contacts in Zhejiang Province, China.

PURPOSE: Genome-wide association studies (GWAS) have identified multiple genetic variants associated with leprosy. To investigate the single and combined associations between single-nucleotide polymorphisms (SNPs) and the development of leprosy, we therefore performed generalized multi-analytical (GMDR) analysis in Chinese leprosy household contacts and constructed a risk prediction model. PATIENTS AND METHODS: This case-control study included 229 leprosy cases and 233 healthy household contacts in Zhejiang province, China. Participants were genotyped for 17 polymorphisms selected from GWAS. The Pearson χ2 test, logistic regression and GMDR analysis were performed to investigate gene-gene interactions and construct a risk prediction model for leprosy. RESULTS: The genotype and the allele distributions of rs142179458, rs2275606, rs663743 and rs73058713 were significantly different between patients and controls. rs2275606, rs6478108, rs663743 and rs73058713 showed an association after adjusting for sex and age in the logistic regression. A five-way interaction model consisting of rs2058660, rs2275606, rs4720118, rs6478108 and rs780668 was chosen as the optimal model for determining leprosy susceptibility. The model classified 237 (51.3%) into the low-risk group and 225 (48.7%) individuals into the high-risk group. The area under the curve (AUC) of this model was 0.757 (95% CI: 0.712-0.803), and the odds ratio for leprosy between the high- and low-risk groups was 9.733 (95% CI: 6.384-14.960; P<0.001). The sensitivity and specificity of the model were observed to be 74.7% and 76.8%, respectively. CONCLUSION: Our results suggest that rs2058660, rs2275606, rs4720118, rs6478108 and rs780668, five SNPs with a significant sole effect on leprosy, interact to confer a higher risk for the disease in leprosy household contacts (HHCs).