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Single-Nucleotide Polymorphisms in Genes Predisposing to Leprosy in Leprosy Household Contacts in Zhejiang Province, China.
Shen, Yun-Liang; Long, Si-Yu; Kong, Wen-Ming; Wu, Li-Mei; Fei, Li-Juan; Yao, Qiang; Wang, Hong-Sheng.
Affiliation
  • Shen YL; Department of Leprosy Control, Zhejiang Provincial Institute of Dermatology, Huzhou, People's Republic of China.
  • Long SY; 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.
  • Kong WM; Department of Leprosy Control, Zhejiang Provincial Institute of Dermatology, Huzhou, People's Republic of China.
  • Wu LM; Department of Leprosy Control, Zhejiang Provincial Institute of Dermatology, Huzhou, People's Republic of China.
  • Fei LJ; Department of Leprosy Control, Zhejiang Provincial Institute of Dermatology, Huzhou, People's Republic of China.
  • Yao Q; Department of Leprosy Control, Zhejiang Provincial Institute of Dermatology, Huzhou, People's Republic of China.
  • Wang HS; 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 ; 13: 767-773, 2020.
Article in En | MEDLINE | ID: mdl-33376384
ABSTRACT

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).
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Pharmgenomics Pers Med Year: 2020 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Pharmgenomics Pers Med Year: 2020 Document type: Article
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