Modelling tree shape and structure in viral phylodynamics

Epidemiological models have highlighted the importance of population structure in the transmission dynamics of infectious diseases. Using HIV-1 as an example of a model evolutionary system, we consider how population structure affects the shape and the structure of a viral phylogeny in the absence of strong selection at the population level. For structured populations, the number of lineages as a function of time is insufficient to describe the shape of the phylogeny. We develop deterministic approximations for the dynamics of tips of the phylogeny over evolutionary time, the number of ‘cherries’, tips that share a direct common ancestor, and Sackin's index, a commonly used measure of phylogenetic imbalance or asymmetry. We employ cherries both as a measure of asymmetry of the tree as well as a measure of the association between sequences from different groups. We consider heterogeneity in infectiousness associated with different stages of HIV infection, and in contact rates between groups of individuals. In the absence of selection, we find that population structure may have relatively little impact on the overall asymmetry of a tree, especially when only a small fraction of infected individuals is sampled, but may have marked effects on how sequences from different subpopulations cluster and co-cluster.

[1]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  J. Robins,et al.  Generation interval contraction and epidemic data analysis. , 2007, Mathematical biosciences.

[3]  Gavin J. D. Smith,et al.  Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic , 2009, Nature.

[4]  Martine Peeters,et al.  Unprecedented Degree of Human Immunodeficiency Virus Type 1 (HIV-1) Group M Genetic Diversity in the Democratic Republic of Congo Suggests that the HIV-1 Pandemic Originated in Central Africa , 2000, Journal of Virology.

[5]  M. J. Sackin,et al.  “Good” and “Bad” Phenograms , 1972 .

[6]  S. Sampling theory for neutral alleles in a varying environment , 2003 .

[7]  M. Steel,et al.  Distributions of cherries for two models of trees. , 2000, Mathematical biosciences.

[8]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[9]  K. Strimmer,et al.  Exploring the demographic history of DNA sequences using the generalized skyline plot. , 2001, Molecular biology and evolution.

[10]  O. Pybus,et al.  An integrated framework for the inference of viral population history from reconstructed genealogies. , 2000, Genetics.

[11]  M. Niu,et al.  Nevirapine, Zidovudine, and Didanosine Compared with Zidovudine and Didanosine in Patients with HIV-1 Infection , 1996, Annals of Internal Medicine.

[12]  J Theiler,et al.  Using human immunodeficiency virus type 1 sequences to infer historical features of the acquired immune deficiency syndrome epidemic and human immunodeficiency virus evolution. , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[13]  Marc A Suchard,et al.  Three roads diverged? Routes to phylogeographic inference. , 2010, Trends in ecology & evolution.

[14]  M. Slatkin,et al.  SEARCHING FOR EVOLUTIONARY PATTERNS IN THE SHAPE OF A PHYLOGENETIC TREE , 1993, Evolution; international journal of organic evolution.

[15]  Katia Koelle,et al.  Rates of coalescence for common epidemiological models at equilibrium , 2012, Journal of The Royal Society Interface.

[16]  Imperfect Information and the Balance of Cladograms and Phenograms , 1996 .

[17]  G. Yule,et al.  A Mathematical Theory of Evolution, Based on the Conclusions of Dr. J. C. Willis, F.R.S. , 1925 .

[18]  E. Wiley Phylogenetics: The Theory and Practice of Phylogenetic Systematics , 1981 .

[19]  Erik M. Volz,et al.  Complex Population Dynamics and the Coalescent Under Neutrality , 2012, Genetics.

[20]  Leonard J. Biallas Searching for “IT” , 1971 .

[21]  M. Suchard,et al.  Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. , 2008, Molecular biology and evolution.

[22]  Andrew Rambaut,et al.  Evolutionary analysis of the dynamics of viral infectious disease , 2009, Nature Reviews Genetics.

[23]  Thomas Petzoldt,et al.  simecol : An Object-Oriented Framework for Ecological Modeling in R , 2007 .

[24]  Vladimir N. Minin,et al.  Integrated Nested Laplace Approximation for Bayesian Nonparametric Phylodynamics , 2012 .

[25]  Erik M. Volz,et al.  Viral phylodynamics and the search for an ‘effective number of infections’ , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[26]  M. Beaumont,et al.  ABC: a useful Bayesian tool for the analysis of population data. , 2010, Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases.

[27]  C. Viboud,et al.  Explorer The genomic and epidemiological dynamics of human influenza A virus , 2016 .

[28]  Thomas R. Riley,et al.  A Randomized Double-blind Placebo-controlled Trial , 2004 .

[29]  David A. Rasmussen,et al.  Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series , 2011, PLoS Comput. Biol..

[30]  S. Frost,et al.  Comparative Study of Methods for Detecting Sequence Compartmentalization in Human Immunodeficiency Virus Type 1 Samples of Viral Populations Are Collected Either by Examining Brain Tissue from Infected Individuals Post Mortem or by Drawing Samples From , 2006 .

[31]  Lisa Sattenspiel,et al.  Modeling and analyzing HIV transmission: the effect of contact patterns , 1988 .

[32]  Sergei L. Kosakovsky Pond,et al.  Phylodynamics of Infectious Disease Epidemics , 2009, Genetics.

[33]  Huldrych F. Günthard,et al.  Inferring Epidemic Contact Structure from Phylogenetic Trees , 2012, PLoS Comput. Biol..

[34]  Christopher D Pilcher,et al.  Brief but efficient: acute HIV infection and the sexual transmission of HIV. , 2004, The Journal of infectious diseases.

[35]  Erik M. Volz,et al.  Simple Epidemiological Dynamics Explain Phylogenetic Clustering of HIV from Patients with Recent Infection , 2012, PLoS Comput. Biol..

[36]  D. Aldous Stochastic models and descriptive statistics for phylogenetic trees, from Yule to today , 2001 .

[37]  G. Garnett,et al.  Is HIV out of control in the UK? An example of analysing patterns of HIV spreading using incidence-to-prevalence ratios , 2006, AIDS.

[38]  M. Pascual,et al.  Global Migration Dynamics Underlie Evolution and Persistence of Human Influenza A (H3N2) , 2010, PLoS pathogens.

[39]  Stéphane Hué,et al.  Genetic analysis reveals the complex structure of HIV-1 transmission within defined risk groups. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[40]  Andy Purvis,et al.  Power of eight tree shape statistics to detect nonrandom diversification: a comparison by simulation of two models of cladogenesis. , 2002, Systematic biology.

[41]  A. Rambaut,et al.  Episodic Sexual Transmission of HIV Revealed by Molecular Phylodynamics , 2008, PLoS medicine.

[42]  Ả. Svensson A note on generation times in epidemic models. , 2007, Mathematical Biosciences.

[43]  J A Jacquez,et al.  The stochastic SI model with recruitment and deaths. I. Comparison with the closed SIS model. , 1993, Mathematical biosciences.

[44]  Arne Ø. Mooers,et al.  Inferring Evolutionary Process from Phylogenetic Tree Shape , 1997, The Quarterly Review of Biology.

[45]  O. Pybus,et al.  Unifying the Epidemiological and Evolutionary Dynamics of Pathogens , 2004, Science.