%0 Journal Article
%A Steinrücken, Matthias
%A Kamm, Jack
%A Spence, Jeffrey P.
%A Song, Yun S.
%T Inference of complex population histories using whole-genome sequences from multiple populations
%D 2019
%R 10.1073/pnas.1905060116
%J Proceedings of the National Academy of Sciences
%P 17115-17120
%V 116
%N 34
%X An increasing number of population genomic studies now try to infer complex models of population history using a number of whole-genome sequences sampled from multiple populations. A key technical challenge to this effort is to compute model likelihoods, which involves integrating out latent variables (genealogical histories) that live in extremely high dimensions. This is a notoriously difficult computational problem, especially when the sample size is greater than a handful and the underlying population genetic model is complex. Here, we present an efficient, flexible statistical method that can scale to larger sample sizes and more populations than previously possible. Aside from demographic inference, our method can be used in other statistical inference problems in evolutionary biology and human genetics.There has been much interest in analyzing genome-scale DNA sequence data to infer population histories, but inference methods developed hitherto are limited in model complexity and computational scalability. Here we present an efficient, flexible statistical method, diCal2, that can use whole-genome sequence data from multiple populations to infer complex demographic models involving population size changes, population splits, admixture, and migration. Applying our method to data from Australian, East Asian, European, and Papuan populations, we find that the population ancestral to Australians and Papuans started separating from East Asians and Europeans about 100,000 y ago, and that the separation of East Asians and Europeans started about 50,000 y ago, with pervasive gene flow between all pairs of populations.
%U https://www.pnas.org/content/pnas/116/34/17115.full.pdf