Applying machine learning algorithms to a real forensic case to predict Y-SNP haplogroup based on Y-STR haplotype. Issue 1 (December 2019)

• Record Type: Journal Article
• Authors: Song, Mengyuan; Zhao, Chenxi; Wang, Zheng; Hou, Yiping
• Is Part Of: Forensic science international. Volume 7:Issue 1(2019)
• Page Start: 637
• Page End: 638
• Publication Date: 2019-12
• Subjects: Y-chromosome single nucleotide polymorphisms (Y-SNPs) -- Y-chromosome short tandem repeats (Y-STRs) -- Machine learning -- Haplogroup prediction; Forensic genetics -- Periodicals; Forensic Genetics -- Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.fsigss.2019.10.120
• Languages: English
• ISSNs: 1875-1768

Abstract

Highlights: the Y chromosomal haplogroups of two samples were predicted. high-resolution haplogroups were applied in the dataset. machine learning methods show high capability in the prediction. Abstract: Y-chromosome single nucleotide polymorphisms (Y-SNPs) have lower mutation rate compared with Y-chromosome short tandem repeats (Y-STRs), thus more informative in paternal lineage identification. Here we present a case about the personal identification of an unidentified cadaver using machine learning methods to determine Y-SNP haplogroup by Y-STR haplotype. Two possible haplotypes from two different male lineages were found after searching national Y-STR databases. Six methods, k-Nearest Neighbor, Naive Bayesian Model, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest were used to predict the haplogroup based on Y-STR haplotype. These two haplotypes are predicted into two different haplogroups, O2a2b1a2a1 and O2a2b1a2a1a3. The predicted results were further verified by Y-SNP genotyping. It indicates that the mismatch of the two haplotypes may not originate from mutation, but due to different lineages. In this case, machine learning algorithms, especially Support Vector Machine and Random Forest show the potential of discriminating different lineages.