索引
祖先信息标记
推论
人工智能
人口
人口分层
遗传谱系
机器学习
线性判别分析
计算机科学
模式识别(心理学)
支持向量机
生物
Boosting(机器学习)
等位基因频率
遗传学
等位基因
基因型
单核苷酸多态性
基因
人口学
社会学
作者
Kuan Sun,Jinliang Li,Libing Yun,Chen Zhang,Jianhui Xie,Xiaoqin Qian,Qiqun Tang,Luming Sun
标识
DOI:10.1016/j.fsigen.2022.102702
摘要
Ancestry inference through population stratification plays an important role in forensic applications. Specifically, ancestry information inferred from forensic DNA evidence can provide vital clues for criminal investigations. Current advances in ancestry inference mostly focus on ancestry informative markers. Hereinto, multi-InDel was proposed as one of the compound markers performing well in complex ancestral classification in the subpopulation of Asia. However, research on analytical methods necessary to make reliable predictions is lacking. The newly proposed compound markers could be assessed with alternative methods. In this study, promising discriminant methods were explored using multi-InDel markers for forensic ancestry inference. As a prerequisite, the adopted multi-InDel markers were assessed by classical methods for population genetics, such as FST analysis, MDS and STRUCTURE. In addition, dimensionality reduction methods and serial reduction strategies were applied for data visualization. Subsequently, machine learning methods, including logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), were evaluated by diverse approaches. As the result of multifarious analyses through comparisons and estimations, XGBoost with one-hot encoding was shown to be more effective in population stratification and ancestry inference for challenging cases with admixed populations.
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