scholarly journals Peer Review #1 of "Demographic inference through approximate-Bayesian-computation skyline plots (v0.2)"

2017 ◽  
Keyword(s):  
Peer Review ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Demographic Inference ◽  
Approximate Bayesian

scholarly journals Modeling SNP array ascertainment with Approximate Bayesian Computation for demographic inference

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Consuelo D. Quinto-Cortés ◽  
August E. Woerner ◽  
Joseph C. Watkins ◽  
Michael F. Hammer
Keyword(s):  
Approximate Bayesian Computation ◽  
Snp Array ◽  
Bayesian Computation ◽  
Demographic Inference ◽  
Approximate Bayesian

scholarly journals Demographic inference through approximate-Bayesian-computation skyline plots

2017 ◽  
Author(s):  
Miguel Navascués ◽  
Raphaël Leblois ◽  
Concetta Burgarella
Keyword(s):  
Approximate Bayesian Computation ◽  
Graphical Representation ◽  
Natural Populations ◽  
Composite Likelihood ◽  
Bayesian Computation ◽  
Mathematical Function ◽  
Effective Population ◽  
Population Sizes ◽  
Demographic Inference ◽  
Approximate Bayesian

AbstractThe skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. Here, we provide a way to estimate historical effective population sizes using an Approximate Bayesian Computation (ABC) framework. We assess its performance using simulated and actual microsatellite datasets. Our method correctly retrieves the signal of contracting, constant and expanding populations, although the graphical shape of the plot is not always an accurate representation of the true demographic trajectory, particularly for recent changes in size and contracting populations. Because of the flexibility of ABC, similar approaches can be extended to other types of data, to multiple populations, or to other parameters that can change through time, such as the migration rate.

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