A machine learning approach for estimating Eastern Asian origins from massive screening of Y chromosomal short tandem repeats polymorphisms

Abstract

Inferring the ancestral origin of DNA evidence recovered from crime scenes is crucial in forensic investigations, especially in the absence of a direct suspect match. Ancestry informative markers (AIMs) have been widely researched and commercially developed into panels targeting multiple continental regions. However, existing forensic ancestry inference panels typically group East Asian individuals into a homogenous category without further differentiation. In this study, we screened Y chromosomal short tandem repeat (Y-STR) haplotypes from 10,154 Asian individuals to explore their genetic structure and generate an ancestry inference tool through a machine learning (ML) approach. Our research identified distinct genetic separations between East Asians and their neighboring Southwest Asians, with tendencies of northern and southern differentiation observed within East Asian populations. All machine learning models developed in this study demonstrated high accuracy, with the Asian classification model achieving an optimal performance of 82.92% and the East Asian classification model reaching 84.98% accuracy. This work not only deepens the understanding of genetic substructures within Asian populations but also showcases the potential of ML in forensic ancestry inference using extensive Y-STR data. By employing computational methods to analyze intricate genetic datasets, we can enhance the resolution of ancestry in forensic contexts involving Asian populations.

Keywords: Biogeographical origin; East Asia; Machine learning; Short tandem repeat; Y chromosome.

Fig. 1

Confusion matrices of the best-performing models for Asian classification: (a) OAO-SVM, (b) OAA-SVM, (c) XGBoost, and (d) Random Forest. * Darker shades represent higher counts

Fig. 2

F1-score of each geographical group in Asian classification models

Fig. 3

Feature importance of the best-performing random forest models for Asian classification

Fig. 4

Confusion matrices of the best-performing models for East Asian classification: (a) SVM, (b) XGBoost, and (c) Random Forest. * Darker shades represent higher counts

Fig. 5

F1-score of each geographical group in East Asian classification models

Fig. 6

Feature importance of the best-performing random forest models for East Asian classification