Strategies in Global Ancestry and Local Ancestry Inference

Genetic ancestry inference has become essential in population and medical genetics, especially for studies of admixed populations. Accurate determination of both global ancestry (GA) proportions and local ancestry (LA) segmental origins requires careful selection of computational methods and reference panels. Here, we present a practical, protocol‐oriented guide that (i) clarifies key concepts (GA vs. LA, reference panel selection, phasing requirements), (ii) organizes methods into model‐based clustering and dimensionality‐reduction approaches for GA and hidden Markov model–based, window‐based machine learning, and deep learning frameworks for LA and (iii) provides concise guidance on tool selection for GA and LA. Step‐by‐step protocols are provided for a typical ADMIXTURE‐based GA analysis and for a SHAPEIT5 + RFMix LA inference pipeline, with practical considerations for genotype array and whole‐genome sequencing data. We also discuss quality control, method validation, and downstream applications of ancestry inference. Finally, we address current challenges and highlight recent advances, including fast algorithms, deep learning models, improved phasing, and integrative tools. This guide aims to help researchers select and implement appropriate ancestry inference methods for diverse study designs and datasets.