Demographic inference from multiple whole genomes using a particle filter for continuous Markov jump processes

Accelerated Bayesian inference of population size history from recombining sequence data

I present PHLASH, a new Bayesian method for inferring population history from whole genome sequence data. PHLASH is population history learning by averaging sampled histories: it works by drawing random, low-dimensional projections of the coalescent intensity function from the posterior distribution of a PSMC-like model, and averaging them together to form an accurate and adaptive size history estimator. On simulated data, PHLASH tends to be faster and have lower error than several competing methods including SMC++, MSMC2, and FITCOAL. Moreover, it provides a full posterior distribution over population size history, leading to automatic uncertainty quantification of the point estimates, as well to new Bayesian testing procedures for detecting population structure and ancient bottlenecks. On the technical side, the key advance is a novel algorithm for computing the score function (gradient of the log-likelihood) of a coalescent hidden Markov model: when there are M hidden states, the algorithm requires 𝒪 M 2 time and 𝒪 1 memory per decoded position, the same cost as evaluating the log-likelihood itself using the naïve forward algorithm. This algorithm is combined with a hand-tuned implementation that fully leverages the power of modern GPU hardware, and the entire method has been released as an easy-to-use Python software package.

A method for genome-wide genealogy estimation for thousands of samples

Knowledge of genome-wide genealogies for thousands of individuals would simplify most evolutionary analyses for humans and other species, but has remained computationally infeasible. We have developed a method, Relate, scaling to >10,000 sequences while simultaneously estimating branch lengths, mutational ages and variable historical population sizes, as well as allowing for data errors. Application to 1,000 Genomes Project haplotypes produces joint genealogical histories for 26 human populations. Highly diverged lineages are present in all groups, but most frequent in Africa. Outside Africa, these mainly reflect ancient introgression from groups related to Neanderthals and Denisovans, while African signals instead reflect unknown events unique to that continent. Our approach allows more powerful inferences of natural selection than has previously been possible. We identify multiple regions under strong positive selection, and multi-allelic traits including hair color, body mass index and blood pressure, showing strong evidence of directional selection, varying among human groups.