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Am J Hum Genet. 2019 Aug 1;105(2):317-333. doi: 10.1016/j.ajhg.2019.06.002. Epub 2019 Jun 27.

Inference of Population Structure from Time-Series Genotype Data.

Author information

1
Department of Computer Science, Columbia University, New York, NY 10027, USA. Electronic address: tjoseph@cs.columbia.edu.
2
Department of Computer Science, Columbia University, New York, NY 10027, USA; Department of Systems Biology, Columbia University, New York, NY 10027, USA; Data Science Institute, Columbia University, New York, NY 10027, USA. Electronic address: itsik@cs.columbia.edu.

Abstract

Sequencing ancient DNA can offer direct probing of population history. Yet, such data are commonly analyzed with standard tools that assume DNA samples are all contemporary. We present DyStruct, a model and inference algorithm for inferring shared ancestry from temporally sampled genotype data. DyStruct explicitly incorporates temporal dynamics by modeling individuals as mixtures of unobserved populations whose allele frequencies drift over time. We develop an efficient inference algorithm for our model using stochastic variational inference. On simulated data, we show that DyStruct outperforms the current state of the art when individuals are sampled over time. Using a dataset of 296 modern and 80 ancient samples, we demonstrate DyStruct is able to capture a well-supported admixture event of steppe ancestry into modern Europe. We further apply DyStruct to a genome-wide dataset of 2,067 modern and 262 ancient samples used to study the origin of farming in the Near East. We show that DyStruct provides new insight into population history when compared with alternate approaches, within feasible run time.

KEYWORDS:

ancient DNA; population structure; time-series; variational inference

PMID:
31256878
PMCID:
PMC6698887
[Available on 2020-02-01]
DOI:
10.1016/j.ajhg.2019.06.002

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