Application of machine learning for ancestry inference using multi-InDel markers
Application of machine learning for ancestry inference using multi-InDel markers
复制标题
机器学习在使用多 InDel 标记的祖先推断中的应用
DOI：
10.1016/j.fsigen.2022.102702
复制
发表时间：
2022-04-01
影响因子：
3.1
通讯作者：
Sun，Luming
中科院分区：
文献类型：
--
作者：
Sun，Kuan;Yao，Yining;Sun，Luming
通过人口分层进行祖先推断在法医应用中具有重要的作用。具体地说，从法医DNA证据中推断出的血统信息可以为刑事调查提供重要线索。目前关于祖先推断的研究进展主要集中在祖先信息标记上。其中，多个Indel标记被认为是在亚洲亚群复杂祖先分类中表现较好的复合标记之一。然而，对做出可靠预测所需的分析方法的研究还很匮乏。新提出的复合标记可以用其他方法进行评估。在本研究中，探索了利用多个Indel标记进行法医血统推断的有效判别方法。采用群体遗传学的经典方法，如FST分析、MDS分析和结构分析等，对所采用的多INDel标记进行评价。此外，还将降维方法和序列降维策略应用于数据可视化。随后，对机器学习方法，包括Logistic回归(LR)、支持向量机(SVM)、k近邻(KNN)和极值梯度增强(XGBoost)进行了评估。通过比较和估计的各种分析的结果表明，对于具有混合种群的挑战案例，采用一热编码的XGBoost在种群分层和祖先推断方面更有效。
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 FSTanalysis, 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.