Eight challenges in phylodynamic inference

Cecile Viboud,Simon D.W Frost,Sebastian Bonhoeffer,Trevor Bedford,Oliver G Pybus,Julia R Gog

doi:10.1016/j.epidem.2014.09.001

Abstract

The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.

Highlights

The vast majority of phylodynamic studies assume a timevarying coalescent model (Pybus and Rambaut, 2009; Volz et al, 2013) that specifies that changes at the population level are deterministic, which have demonstrated a variety of dynamic patterns for different viral systems (Table 1 in Frost and Volz (2010))
Standard phylogenetic approaches are more likely, under many circumstances, to reconstruct a ‘star-like’ tree when applied to sequence data affected by recombination, which resembles the pattern produced under exponential growth
Development of algorithms that can, for example, improve mixing of Markov chain Monte Carlo approaches commonly used in phylodynamic studies, or at least provide rapid, approximate results for exploratory data analysis would further open up possibilities for the analysis of ‘big data’

Summary

How can we account for sequence sampling patterns?

‘Phylodynamics’ is a term used to describe the ‘melding of immunodynamics, epidemiology, and evolutionary biology’ in order to understand how infectious diseases are transmitted and evolve (Grenfell et al, 2004). Wertheim et al (2012) analysed major subtypes of pandemic HIV-1 group M, which are thought to exemplify closely related lineages with different substitution rates, and found that the times to the most recent common ancestor differed markedly when subtypes were analysed separately compared to jointly This suggests that current models fail to capture higher-order temporal correlations in the evolutionary rate. Recent progress has been made applying non-Kingman coalescent processes, such as the Bolthausen–Sznitman coalescent, to capture some of the broad effects of selection on phylogenetic shape and scale (Neher and Hallatschek, 2013) Even with such coalescent models, there will remain the assumption that the observed phylogeny is independent of the substitution process. This is problematic as a major goal is to link viral mutations to evolutionary outcomes, and identify strains that may have a competitive advantage

What is the role of stochastic effects in phylodynamics?

How can we incorporate recombination and reassortment?

How can we include phenotypic as well as genotypic information?

How can we capture pathogen evolution at both withinand between-host scales?

How can analytical approaches keep up with advances in sequencing?

Discussion