Optimization scheme of machine learning model for genetic division between northern Han, southern Han, Korean and Japanese
Received date: 2022-05-03
Revised date: 2022-07-13
Online published: 2022-08-11
Supported by
Supported by the Key Laboratory of Forensic Genetics of China No(2020FGKFKT01);the Graduate Research and Practice Innovation Program of Jiangsu Normal University Nos(KYCX20_2286);the Graduate Research and Practice Innovation Program of Jiangsu Normal University Nos(KYCX21_2597)
Han Chinese, Korean and Japanese are the main populations of East Asia, and Han Chinese presents a gradient admixture from north to south. There are differences among the East Asian populations in genetic structure. To achieve fine-scale genetic classification of southern (S-) and northern (N-) Han Chinese, Korean and Japanese individuals in this study, we collected and analyzed 1185 ancestry informative SNPs (AISNPs) from previous literature reports and our laboratory findings. First, two machine learning algorithms, softmax and randomForest, were used to build genetic classification models. Then, phylogenetic tree, STRUCTURE and principal component analysis were used to evaluate the performance of classification for different AISNP panels. The 234-AISNP panel achieved a fine-scale differentiation among the target populations in four classification schemes. The accuracy of the softmax model was 92%, which realized the accurate classification of the S-Han, N-Han, Korean and Japanese individuals. The two machine learning models tested in this study provided important references for the high-resolution discrimination of close-range populations and will be useful tools to optimize marker panels for developing forensic DNA ancestry inference systems.
Yongqiang Kong, Jinkai Liu, Jiaqi Gu, Jingyi Xu, Yunuo Zheng, Yiliang Wei, Shaoyuan Wu . Optimization scheme of machine learning model for genetic division between northern Han, southern Han, Korean and Japanese[J]. Hereditas(Beijing), 2022 , 44(11) : 1028 -1043 . DOI: 10.16288/j.yczz.22-073
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