<i>HiMWA</i> : A Hierarchical Multiple-wave Admixture Model for Reconstructing Complex Population Admixture Histories

Population admixture is a pivotal evolutionary process that has profoundly shaped genetic diversity and population structure in modern human populations. However, most existing methods for inferring admixture history rely on simplified assumptions, such as strictly sequential contributions from ancestral populations, thereby limiting their applicability to realistic scenarios. Here, we introduce HiMWA, a computational framework based on a hierarchical multiple-wave admixture model for reconstructing complex admixture histories involving multiple ancestral populations. HiMWA characterizes both hierarchical admixture, in which ancestral populations first admix to form intermediate populations, and subsequent multiple-wave admixture that shapes the final admixed population. The framework integrates model selection based on ancestry switch counts with parameter estimation using the length distribution of ancestral tracts. Extensive simulations demonstrate that HiMWA is accurate and robust across diverse admixture scenarios, including those affected by genetic drift and local ancestry inference errors. Applying HiMWA to Kazakhs and Uyghurs revealed a shared hierarchical admixture structure. In both populations, West European and South Asian ancestries first admixed to form a West Eurasian intermediate population, while East Asian and Siberian ancestries formed an East Eurasian intermediate population. These two intermediates subsequently contributed to present-day populations through multiple waves of admixture. Our results highlight the prevalence of hierarchical multiple-wave admixture in Central Asia and provide insights into the region's complex demographic history. HiMWA offers a powerful and flexible framework for disentangling complex admixture histories and reconstructing realistic population genetic histories from genomic data. The HiMWA software, documentation, and example datasets are publicly available at https://github.com/Shuhua-Group/HiMWA and https://ngdc.cncb.ac.cn/biocode/tool/BT008069.