02521nas a2200409   4500000000100000008004100001100001100042700001100053700001100064700000900075700000900084700001000093700001000103700001200113700000900125700001100134700001100145700001000156700001000166700001200176700000900188700001200197700000900209700000900218700001000227700001000237700000900247700001000256700001000266700001200276245009200288856007900380300001300459490000700472520161800479022001402097       2018                            d1 aWang N1 aWang Z1 aWang C1 aFu X1 aYu G1 aYue Z1 aLiu T1 aZhang H1 aLi L1 aChen M1 aWang H1 aNiu G1 aLiu D1 aZhang M1 aXu Y1 aZhang Y1 aLi J1 aLi Z1 aYou J1 aChu T1 aLi F1 aLiu D1 aLiu H1 aZhang F00aPrediction of leprosy in the Chinese population based on a weighted genetic risk score.  uhttps://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0006789  ae00067890 v123 a<p>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 &gt; 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.</p>
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