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Application of machine learning for ancestry inference using multi-InDel markers.

Kuan Sun¹, Yining Yao², Libing Yun³

¹Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai, China; Department of Fetal Medicine and Prenatal Diagnosis Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, 2699 West Gaoke Rd, Shanghai 201204, China; Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China.

Forensic Science International. Genetics

|April 4, 2022

Summary

Forensic DNA analysis can infer ancestry using multi-insertion/deletion (multi-InDel) markers. Extreme Gradient Boosting (XGBoost) machine learning proved most effective for population stratification and ancestry inference in admixed populations.

Keywords:

Ancestry inference Asian population Dimensionality reduction Machine learning Multi-InDel markers

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Area of Science:

Forensic Genetics
Population Genetics
Computational Biology

Background:

Population stratification and ancestry inference are crucial in forensic science for criminal investigations.
Current ancestry inference methods primarily rely on ancestry-informative markers.
Multi-insertion/deletion (multi-InDel) markers show promise for complex ancestral classification, particularly in Asian subpopulations.

Purpose of the Study:

To explore and evaluate analytical methods for reliable forensic ancestry inference using multi-InDel markers.
To assess the effectiveness of machine learning algorithms for population stratification and ancestry prediction with multi-InDel data.

Main Methods:

Assessed multi-InDel markers using classical population genetics methods (FST analysis, Multidimensional Scaling, STRUCTURE).
Applied dimensionality reduction and serial reduction strategies for data visualization.
Evaluated machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost).

Main Results:

Classical methods confirmed the utility of multi-InDel markers for population genetics analysis.
Machine learning approaches, particularly XGBoost with one-hot encoding, demonstrated superior performance.
XGBoost effectively handled population stratification and ancestry inference in challenging admixed populations.

Conclusions:

Multi-InDel markers are valuable for forensic ancestry inference, especially in complex ancestral classifications.
Machine learning, specifically XGBoost, offers a powerful and effective analytical approach for utilizing multi-InDel data.
The developed methods enhance the reliability of forensic DNA evidence interpretation for admixed populations.