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Demographic inference using skyline plots on approximate bayesian computation

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HAL Id: hal-01267944

https://hal.archives-ouvertes.fr/hal-01267944

Submitted on 3 Mar 2021

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Demographic inference using skyline plots on approximate bayesian computation

Miguel Navascués, Concetta Burgarella

To cite this version:

Miguel Navascués, Concetta Burgarella. Demographic inference using skyline plots on approximate bayesian computation. Annual Meeting of the Society for Molecular biology et Evolution, Jun 2012, Dublin, Ireland. 2012, Posters abstracts. �hal-01267944�

(2)

Demographic Inference Using Skyline Plots

on Approximate Bayesian Computation

Miguel Navascués & Concetta Burgarella

H IG H L O W MEDIAN 95%HPD TRUE DEMOGRAPHY Relative RMSE Relative BIAS

P(true value outside 95%HPD)

AGRES Sampled populations PINETA TREVENQUE ESTENA REDEMUÑA Known distribution

Application to European Yew (Taxus baccata)

Burgarella et al. (2012) Mol Ecol 21:3006–21

Distribution in the Iberian Peninsula

ABC Skyline Plots from seven microsatellite markers (model with 3 periods)

probability density

Bayesian Skyline Plots are representations of the posterior probability density of the effective population size in function of time. A model with

several periods of constant population size is used to explore the demographies that best explain the data. Instead of estimating the posterior probability density for parameters of the model (i.e. size at each interval) the probability density of population size in function of time (i.e. size at each generation) is estimated. This method is currently implemented in BEAST (Drummond & Rambaut 2007 BMC Evol Biol), which uses an

MCMC estimation of likelihood under the coalescent.

We have implemented the same approach under an Approximate Bayesian Computation analysis. The purpose of this is to facilitate analysis of dataset were the MCMC approach is inappropriate, such as analysis of DNA sequences with intragenic recombination. The performance of

the method has been evaluated with simulated datasets (see table). Simulations for the ABC procedure were performed with FASTSIMCOAL (Excoffier & Foll 2011 Bioinformatics), using a model with 10 periods of constant size. Sample were of 50 diploid individuals typed at 100

microsatellite loci. Summary statistics were calculated with ARLSUMSTATS (from Arlequin). Relative root of mean squared error, relative bias and proportion of times the true value fall outside the 95%HPD interval were estimated from 50 simulations of target data.

UMR CBGP, INRA Montpellier, [email protected] UMR AGAP, INRA Montpellier, [email protected]

PRIOR

(1 random simulation)

POSTERIOR

POSTERIOR

(50 simulations)

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