Accurate and flexible estimation of effective population size history

Current methods for inferring historical population sizes from DNA sequences often impose a heavy computational burden, or relieve that burden by imposing a fixed parametric form. In addition, they can be marred by sequencing errors or uncertainty about recombination rates, and the quality of inference is often poor in the recent past. We propose “InferNo” for flexible, nonparametric inference of effective population sizes. It requires modest computing resources and little prior knowledge of the recombination and mutation maps, and is robust to sequencing error and gene conversion. We illustrate the statistical and computational advantages of InferNo over previous approaches using a range of simulation scenarios. In particular, we demonstrate the ability of InferNo to exploit biobank-scale datasets for accurate inference of rapid population size changes in the recent past. We also apply InferNo to worldwide human data, finding remarkable similarities in inferences from different populations in the same region. Unlike previous studies, we show two historic bottlenecks for most of the non-African populations.