Math-Bio seminar: "Robust and scalable inference of population history from hundreds of unphased whole-genomes"

Mon, 12/05/2016 - 16:00 - 17:00
Jonathan Terhorst, University of California, Berkeley

It has recently been demonstrated that inference methods based on genealogical processes with recombination can reveal past population history in unprecedented detail. However, these methods scale poorly with sample size, which limits resolution in the recent past, and they require phased genomes, which contain switch errors that can catastrophically distort the inferred history. I this talk I 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). The key innovation is a novel probabilistic framework which couples the genaelogical process for a given individual with allele frequency information from a large reference panel such as 1000 Genomes.

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 population in Africa, and tens of genomes from zebra finch and long-tailed finch populations in Australia.

318 Carolyn Lynch Laboratory