Abstract
Our study applies a systematic computational genomic approach to investigate the complex population dynamics of Southern Slavs in the Hungarian Plain and Avar Khaganate, and their subsequent role in forming the medieval Croatian population. Using a quality-controlled dataset of 1,800 ancient DNA samples, we implemented a comprehensive analytical framework centered on systematic screening of marginal Principal Components to detect cryptic Slavic genetic signatures. This strategic methodological approach addresses the well-documented analytical challenge that Germanic and Slavic populations remain indistinguishable using conventional PC1-2 analysis due to shared Baltic Bronze Age ancestry. Through systematic evaluation of all principal components (PC1-20), we identified PC9 as a reliable indicator of Slavic ancestry within European ancient DNA samples when combined with PC4 and PC3. This approach revealed substantial Baltic genetic components in early Slavic populations (57% in Slovakia/Slovenia) decreasing to 39%-51% in medieval Croatian samples. Statistical modeling demonstrates that contemporary Croatian populations formed through three distinct migration waves, with 50%-60% total Slavic ancestry and 20%-25% pre-Slavic Balkan continuity. Significantly, we identified individuals with Slavic genetic profiles in prestigious Avar burial contexts, questioning established understanding of social hierarchies within the Khaganate. The genomic evidence indicates that key aspects of South Slavic genetic structure emerged through interactions within the Carpathian Basin rather than after Balkan arrival. Our findings demonstrate that Croatian ethnogenesis involved gradual integration rather than population replacement, with the Avar Khaganate serving as a crucial demographic interface where South Slavic genetic structure emerged. Our approach addresses longstanding historical questions regarding Croatian ethnogenesis by identifying specific genetic signatures and quantifying their population-level contributions, demonstrating how application of computational genomics provides unprecedented resolution in studying complex population transformations when traditional historical and archaeological approaches reach interpretive limits.