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

By Jonathan Terhorst, John A. Kamm, Yun S. Song. Nature Genetics, 2016

822 citations recorded by the source corpus.

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.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/3510186b8f37f8a4818e5f5a5f8bcbf89d0f26e8

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Efficient ancestry and mutation simulation with msprime 1.0 (2021, methodology): “…interface for msprime is through a thoroughly documented Python API, which has encouraged the development of an ecosystem of downstream tools (Terhorst et al. 2017; Chan et al. 2018; Spence and Song 2019; Adrion et al. 2020a, 2020b; Kamm et al. 2020; McKenzie and Eaton 2020; Montinaro et al.…”