Inference of Population Structure from Ancient DNA

  • Tyler A. Joseph
  • Itsik Pe’erEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10812)


Methods for inferring population structure from genetic information traditionally assume samples are contemporary. Yet, the increasing availability of ancient DNA sequences begs revision of this paradigm. We present Dystruct (Dynamic Structure), a framework and toolbox for inference of shared ancestry from data that include ancient DNA. By explicitly modeling population history and genetic drift as a time-series, Dystruct more accurately and realistically discovers shared ancestry from ancient and contemporary samples. Formally, we use a normal approximation of drift, which allows a novel, efficient algorithm for optimizing model parameters using stochastic variational inference. We show that Dystruct outperforms the state of the art when individuals are sampled over time, as is common in ancient DNA datasets. We further demonstrate the utility of our method on a dataset of 92 ancient samples alongside 1941 modern ones genotyped at 222755 loci. Our model tends to present modern samples as the mixtures of ancestral populations they really are, rather than the artifactual converse of presenting ancestral samples as mixtures of contemporary groups.


Population genetics Population structure Ancient DNA Time-series Variational inference Kalman filtering 



This material is based upon work supported by the National Science Foundation (NSF) Graduate Research Fellowship under Grant No. DGE 16-44869, and the NSF under Grant No. DGE-1144854, and Grant No. CCF 1547120. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the NSF.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceColumbia UniversityNew YorkUSA
  2. 2.Department of Systems BiologyColumbia UniversityNew YorkUSA
  3. 3.Data Science InstituteColumbia UniversityNew YorkUSA

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