Integrated genetic and geographic ancestry prediction via large-scale genomic data and machine learning.

ABSTRACT: Understanding fine-scale genetic and geographic ancestry in East and Southeast Asia is difficult due to complex population histories and limited high-resolution genomic data. Here, we introduce a comprehensive framework that combines ancestry-informative single nucleotide polymorphism (AISNP) panels with machine learning to jointly determine genetic ancestry and geographic origins in 1,703 individuals from 67 East and Southeast Asian groups. We developed seven nested AISNP panels, from 50 to 2,000 SNPs, and tested six classification algorithms: logistic regression, support vector machines, k-nearest neighbors, random forest, convolutional neural networks, and eXtreme Gradient Boosting (XGBoost). The best results came from the optimized XGBoost model, which achieved 95.6% accuracy and an AU

SUBMITTER: Chen J 

PROVIDER: S-EPMC12570655 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature