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

Journal article

Demographic events shape a population's genetic diversity, a process described by the coalescent-with-recombination model that relates demography and genetics by an unobserved sequence of genealogies along the genome. As the space of genealogies over genomes is large and complex, inference under this model is challenging. Formulating the coalescent-with-recombination model as a continuous-time and -space Markov jump process, we develop a particle filter for such processes, and use waypoints that under appropriate conditions allow the problem to be reduced to the discrete-time case. To improve inference, we generalise the Auxiliary Particle Filter for discrete-time models, and use Variational Bayes to model the uncertainty in parameter estimates for rare events, avoiding biases seen with Expectation Maximization. Using real and simulated genomes, we show that past population sizes can be accurately inferred over a larger range of epochs than was previously possible, opening the possibility of jointly analyzing multiple genomes under complex demographic models. Code is available at https://github.com/luntergroup/smcsmc.

Authors: Henderson, D; Zhu, S; Cole, CB; Lunter, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS ONE
• Volume: 16
• Issue: 3
• Pages: e0247647-e0247647
• Publication date: 2021-03-02
• DOI: 10.1371/journal.pone.0247647
• EISSN: 1932-6203
• ISSN: 1932-6203
• Language: English
• Keywords: Hidden Markov model; Artificial intelligence; Filter (signal processing); Phylogenetics; Population; Particle filter; Approximate Bayesian computation; Biology; Computer science; Genetics; Coalescent theory; Bayes' theorem; Bayesian probability; Inference