The 82nd Annual Meeting of the American Association of Physical Anthropologists (2013)


Multiple, distinct biological populations in Iron Age Mongolia: The Xiongnu elite cemetery of Borkhan Tolgoi (Egiin Gol valley) reveals an ancestral Turkish component

RYAN W. SCHMIDT.

Anthropology, University of Montana

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Building on previous research, this study explores the prehistory of Mongolia during a time when nomadic tribes created the world’s first steppe empire in Inner Asia. These aggregated tribes, known as Xiongnu, ruled from the 3rd century BCE to the 2nd century CE. They came to define steppe polity construction later used by the Mongol Empire. These nomads moved extensively over the eastern steppe and interacted with peoples from Siberia to Xinjiang. However, the Xiongnu as a people are relatively unknown to scholars and their origins remain obscure.

This study attempts to elucidate questions of the Xiongnu’s population history and structure by examining craniofacial diversity using geometric morphometrics. Using a suite of multivariate statistical analyses, this study explains the origins of the Xiongnu in a biological context and makes inferences about genetic exchanges. A quantitative genetic model (Relethford-Blangero) is used to test group relationships and infer levels of gene flow between groups.

Results indicate the Xiongnu were composed of at least two biologically distinct groups. One sample from an elite cemetery in northern Mongolia (Borkhan Tolgoi) shares their ancestry with a Bronze Age population from Mongolia (Chandman), and possibly, to a later migration of Turks. The Xiongnu also evidence biological similarity with nomads who composed the Mongol Empire, modern-day Mongolians, and Siberian groups. These results are similar to ancient genetic studies while also achieving consensus with models of steppe polity formation proposed by archaeologists. The Xiongnu nomads are a significant part of Mongolia’s past with links to its modern peoples.

This work was partially supported through a National Science Foundation Doctoral Dissertation Improvement Grant (BCS #1028773).

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