The Structured Coalescent

1997 ◽  
pp. 231-255 ◽  
Author(s):  
Hilde M. Herbots
Keyword(s):  
Structured Coalescent

scholarly journals Correction to: The IICR and the non-stationary structured coalescent: towards demographic inference with arbitrary changes in population structure

Heredity ◽  
2021 ◽  
Vol 126 (4) ◽  
pp. 706-706
Author(s):  
Willy Rodríguez ◽  
Olivier Mazet ◽  
Simona Grusea ◽  
Armando Arredondo ◽  
Josué M. Corujo ◽  
...  
Keyword(s):  
Population Structure ◽  
Structured Coalescent ◽  
Demographic Inference

scholarly journals SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent

2016 ◽  
Vol 12 (9) ◽  
pp. e1005130 ◽  
Author(s):  
Nicola De Maio ◽  
Chieh-Hsi Wu ◽  
Daniel J Wilson
Keyword(s):  
Structured Coalescent

scholarly journals Structured Coalescent Processes on Different Time Scales

Genetics ◽  
1997 ◽  
Vol 146 (4) ◽  
pp. 1501-1514 ◽  
Author(s):  
Magnus Nordborg
Keyword(s):  
Local Adaptation ◽  
Time Scales ◽  
Balancing Selection ◽  
Background Selection ◽  
Selfing Rate ◽  
Local Selection ◽  
Structured Coalescent ◽  
High Degree ◽  
Different Time Scales ◽  
Coalescence Times

It is demonstrated that the structured coalescent model can readily be extended to include phenomena such as partial selfing and background selection through the use of an approximation based on separation of time scales. A model that includes these phenomena, as well as geographic subdivision and linkage to a polymorphism maintained either by local adaptation or by balancing selection, is derived, and the expected coalescence time for a pair of genes is calculated. It is found that background selection reduces coalescence times within subpopulations and allelic classes, leading to a high degree of apparent differentiation. Extremely high levels of subpopulation differentiation are also expected for regions of the genome surrounding loci important in local adaptation. These regions will be wider the stronger the local selection, and the higher the selfing rate.

scholarly journals Bayesian phylodynamic inference with complex models

2018 ◽  
Author(s):  
Erik M. Volz ◽  
Igor Siveroni
Keyword(s):  
Population Genetic ◽  
Influenza A ◽  
Genetic Model ◽  
Sequence Data ◽  
Genetic Data ◽  
Simultaneous Estimation ◽  
Model Parameters ◽  
Epidemiological Models ◽  
Structured Coalescent ◽  
Speed Accuracy

AbstractPopulation genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry.The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST phylogenetics platform.

scholarly journals Population structure and coalescence in pedigrees: Comparisons to the structured coalescent and a framework for inference

2017 ◽  
Vol 115 ◽  
pp. 1-12 ◽  
Author(s):  
Peter R. Wilton ◽  
Pierre Baduel ◽  
Matthieu M. Landon ◽  
John Wakeley
Keyword(s):  
Population Structure ◽  
Structured Coalescent

Coalescence times and F ST values in subdivided populations with symmetric structure

2003 ◽  
Vol 35 (03) ◽  
pp. 665-690
Author(s):  
Hilde M. Wilkinson-Herbots
Keyword(s):  
Migration Rate ◽  
Recent Common Ancestor ◽  
Regularity Conditions ◽  
Simple Relationship ◽  
Total Size ◽  
Graph Theoretic ◽  
Most Recent Common Ancestor ◽  
Subdivided Populations ◽  
Structured Coalescent ◽  
Infinite Alleles Model

The structured coalescent is a continuous-time Markov chain which describes the genealogy of a sample of homologous genes from a subdivided population. Assuming this model, some results are proved relating to the genealogy of a pair of genes and the extent of subpopulation differentiation, which are valid under certain graph-theoretic symmetry and regularity conditions on the structure of the population. We first review and extend earlier results stating conditions under which the mean time since the most recent common ancestor of a pair of genes from any single subpopulation is independent of the migration rate and equal to that of two genes from an unstructured population of the same total size. Assuming the infinite alleles model of neutral mutation with a small mutation rate, we then prove a simple relationship between the migration rate and the value of Wright's coefficient F ST for a pair of neighbouring subpopulations, which does not depend on the precise structure of the population provided that this is sufficiently symmetric.

scholarly journals MERS-CoV spillover at the camel-human interface

2017 ◽  
Author(s):  
Gytis Dudas ◽  
Luiz Max Carvalho ◽  
Andrew Rambaut ◽  
Trevor Bedford ◽  
Ali M. Somily ◽  
...  
Keyword(s):  
Arabian Peninsula ◽  
Sequence Data ◽  
Full Potential ◽  
Viral Sequence ◽  
Mortality And Morbidity ◽  
Host Tropism ◽  
Human Interface ◽  
Structured Coalescent ◽  
Significant Mortality

AbstractMiddle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic virus from camels causing significant mortality and morbidity in humans in the Arabian Peninsula. The epidemiology of the virus remains poorly understood, and while case-based and seroepidemiological studies have been employed extensively throughout the epidemic, viral sequence data have not been utilised to their full potential. Here we use existing MERS-CoV sequence data to explore its phylodynamics in two of its known major hosts, humans and camels. We employ structured coalescent models to show that long-term MERS-CoV evolution occurs exclusively in camels, whereas humans act as a transient, and ultimately terminal host. By analysing the distribution of human outbreak cluster sizes and zoonotic introduction times we show that human outbreaks in the Arabian peninsula are driven by seasonally varying zoonotic transfer of viruses from camels. Without heretofore unseen evolution of host tropism, MERS-CoV is unlikely to become endemic in humans.

scholarly journals Inferring environmental transmission using phylodynamics: a case-study using simulated evolution of an enteric pathogen

2021 ◽  
Vol 18 (179) ◽  
pp. 20210041
Author(s):  
Daniel Dawson ◽  
David Rasmussen ◽  
Xinxia Peng ◽  
Cristina Lanzas
Keyword(s):  
Population Structure ◽  
Spatial Information ◽  
Transmission Model ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Similar Time ◽  
Indirect Transmission ◽  
Tree Inference ◽  
Structured Coalescent ◽  
Transmission Pathways

Indirect (environmental) and direct (host–host) transmission pathways cannot easily be distinguished when they co-occur in epidemics, particularly when they occur on similar time scales. Phylodynamic reconstruction is a potential approach to this problem that combines epidemiological information (temporal, spatial information) with pathogen whole-genome sequencing data to infer transmission trees of epidemics. However, factors such as differences in mutation and transmission rates between host and non-host environments may obscure phylogenetic inference from these methods. In this study, we used a network-based transmission model that explicitly models pathogen evolution to simulate epidemics with both direct and indirect transmission. Epidemics were simulated according to factorial combinations of direct/indirect transmission proportions, host mutation rates and conditions of environmental pathogen growth. Transmission trees were then reconstructed using the phylodynamic approach SCOTTI (structured coalescent transmission tree inference) and evaluated. We found that although insufficient diversity sets a lower bound on when accurate phylodynamic inferences can be made, transmission routes and assumed pathogen lifestyle affected pathogen population structure and subsequently influenced both reconstruction success and the likelihood of direct versus indirect pathways being reconstructed. We conclude that prior knowledge of the likely ecology and population structure of pathogens in host and non-host environments is critical to fully using phylodynamic techniques.

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