Phenotypic Prediction of Male-pattern Baldness in Chinese Han Population Based on DNA Variants

Objective Male pattern baldness(MPB),or androgenetic alopecia(AGA),is a common type of hairloss in men, with an estimation that approximately80% of the phenotypic variance can be explained by geneticfactors. Most prediction models were developed in European and few MPB associated(single nucleotidepolymorphisms, SNPs)have been validated in East Asian population. In this study, MPB associated SNPs in European were verified in Chinese population, and MPB risk prediction models were built based on those SNP data. Methods We examined486genetic variants previously reported associated with MPB, and assessed their impacts on hair loss in312Chinese individuals. Different sets of SNPs were selected by stepwise regression and Lasso regression.Logistic regression algorithm was used to construct the prediction models and the evaluationswere conducted by the method of10-fold cross validation.We further compared the prediction accuracy amonglogistic regression,k-nearest neighbor classifier,random forest and support vector machine.Results174SNPsdemonstrated significant associations with MPB(P<0.05).Among those SNP markers,22SNPs and25SNPswere selected by different screening methods. Two logistic regression model considering the genotypes of22and25SNPs demonstrate that the risk of MPB were predictable at AUC(area under curve)level of0.85and0.84.Prediction accuracy was slightly reduced after performing10-fold cross validation,0.81and0.77respectively.Moreover,theAUCof both models reaches maximum(0.89)when age was added as a predictive factor. From the running results, the logistic regression prediction model had obvious advantages. Conclusion Overall, although the accuracy obtained here has not reached a clinically desired level, our model still has great potential for genetic prediction of MPB, which may assist decision making on early MPB intervention actions and in forensic investigations