Shen Yun-Liang, Long Si-Yu, Kong Wen-Ming, Wu Li-Mei, Fei Li-Juan, Yao Qiang, Wang Hong-Sheng
Department of Leprosy Control, Zhejiang Provincial Institute of Dermatology, Huzhou, People's Republic of China.
Laboratory of Leprosy and Other Mycobacterial Infections, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People's Republic of China.
Pharmgenomics Pers Med. 2020 Dec 21;13:767-773. doi: 10.2147/PGPM.S286270. eCollection 2020.
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.
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 χ test, logistic regression and GMDR analysis were performed to investigate gene-gene interactions and construct a risk prediction model for leprosy.
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; <0.001). The sensitivity and specificity of the model were observed to be 74.7% and 76.8%, respectively.
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).
全基因组关联研究（GWAS）已鉴定出多个与麻风病相关的基因变异。为了研究单核苷酸多态性（SNP）与麻风病发生之间的单一及联合关联，我们对中国麻风病家庭接触者进行了广义多分析（GMDR）分析，并构建了风险预测模型。
本病例对照研究纳入了中国浙江省的229例麻风病患者和233名健康家庭接触者。对从GWAS中选取的17个多态性进行基因分型。采用Pearson χ检验、逻辑回归和GMDR分析来研究基因-基因相互作用，并构建麻风病风险预测模型。
患者与对照之间rs142179458、rs2275606、rs663743和rs73058713的基因型和等位基因分布存在显著差异。在逻辑回归中对性别和年龄进行校正后，rs2275606、rs6478108、rs663743和rs73058713显示出关联。由rs2058660、rs2275606、rs4720118、rs6478108和rs780668组成的五元相互作用模型被选为确定麻风病易感性的最佳模型。该模型将237名（51.3%）个体分类为低风险组，225名（48.7%）个体分类为高风险组。该模型的曲线下面积（AUC）为0.757（95%CI：0.712 - 0.803），高风险组与低风险组之间麻风病的优势比为9.733（95%CI：6.384 - 14.960；P < 0.001）。观察到该模型的敏感性和特异性分别为74.7%和76.8%。
我们的结果表明，rs2058660、rs2275606、rs4720118、rs6478108和rs780668这五个对麻风病有显著单独作用的SNP相互作用，使麻风病家庭接触者（HHCs）患该病的风险更高。
