A haplogroup-based methodology for assigning individuals to geographical regions using Y-STR data

Abstract

Y chromosome markers are essential tools in forensic genetics, offering valuable insights for genetic identification. This study seeks to develop a forensic prediction model using machine learning techniques to improve the efficiency of genetic identification processes. Specifically, the model aims to predict an individual's nearest geographical area of residence based on Y chromosome marker analysis. The methodology involved four key steps: haplogroup determination, primary branch identification, geographical region assignment, model stratification, and fine-tuning. Once developed, the model can be integrated into decision support systems, providing forensic geneticists with a reliable knowledge source to enhance decision-making during investigations.

Keywords: Forensic genetics; Haplogroup; Iranian population; Machine learning; Phylogenetic tree; Prediction model; Y chromosome; Y haplotype; Y-STR.