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Nat Genet. 2017 Feb;49(2):303-309. doi: 10.1038/ng.3748. Epub 2016 Dec 26.

Robust and scalable inference of population history from hundreds of unphased whole genomes.

Terhorst J1, Kamm JA1,2, Song YS1,2,3,4.

Author information

1
Department of Statistics, University of California, Berkeley, Berkeley, California, USA.
2
Computer Science Division, University of California, Berkeley, Berkeley, California, USA.
3
Department of Integrative Biology, University of California, Berkeley, Berkeley, California, USA.
4
Departments of Biology and Mathematics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Abstract

It has recently been demonstrated that inference methods based on genealogical processes with recombination can uncover past population history in unprecedented detail. However, these methods scale poorly with sample size, limiting resolution in the recent past, and they require phased genomes, which contain switch errors that can catastrophically distort the inferred history. Here we present SMC++, a new statistical tool capable of analyzing orders of magnitude more samples than existing methods while requiring only unphased genomes (its results are independent of phasing). SMC++ can jointly infer population size histories and split times in diverged populations, and it employs a novel spline regularization scheme that greatly reduces estimation error. We apply SMC++ to analyze sequence data from over a thousand human genomes in Africa and Eurasia, hundreds of genomes from a Drosophila melanogaster population in Africa, and tens of genomes from zebra finch and long-tailed finch populations in Australia.

PMID:
28024154
PMCID:
PMC5470542
DOI:
10.1038/ng.3748
[Indexed for MEDLINE]
Free PMC Article

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