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Prediction of leprosy in the Chinese population based on a weighted genetic risk score.
- Source :
-
PLoS Neglected Tropical Diseases . 9/19/2018, Vol. 12 Issue 9, p1-12. 12p. - Publication Year :
- 2018
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19352727
- Volume :
- 12
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- PLoS Neglected Tropical Diseases
- Publication Type :
- Academic Journal
- Accession number :
- 131845315
- Full Text :
- https://doi.org/10.1371/journal.pntd.0006789