scholarly journals Distinguishing coalescent models - which statistics matter most?

2019 ◽  
Author(s):  
Fabian Freund ◽  
Arno Siri-Jégousse
Keyword(s):  
Genetic Diversity ◽  
Random Forest ◽  
Model Selection ◽  
Exponential Growth ◽  
Approximate Bayesian Computation ◽  
Genetic Data ◽  
Specific Question ◽  
Approximate Bayesian ◽  
Fitting Model ◽  
Kingman’S Coalescent

AbstractModelling genetic diversity needs an underlying genealogy model. To choose a fitting model based on genetic data, one can perform model selection between classes of genealogical trees, e.g. Kingman’s coalescent with exponential growth or multiple merger coalescents. Such selection can be based on many different statistics measuring genetic diversity. A random forest based Approximate Bayesian Computation is used to disentangle the effects of different statistics on distinguishing between various classes of genealogy models. For the specific question of inferring whether genealogies feature multiple mergers, a new statistic, the minimal observable clade size, is introduced. When combined with classical site frequency based statistics, it reduces classification errors considerably.

scholarly journals Inference with selection, varying population size and evolving population structure: Application of ABC to a forward-backward coalescent process with interactions

2019 ◽  
Author(s):  
Clotilde Lepers ◽  
Sylvain Billiard ◽  
Matthieu Porte ◽  
Sylvie Méléard ◽  
Viet Chi Tran
Keyword(s):  
Genetic Diversity ◽  
Population Structure ◽  
A Priori ◽  
Real Data ◽  
Genetic Data ◽  
Accurate Estimate ◽  
Asexual Population ◽  
Y Chromosomes ◽  
Population Structures ◽  
Approximate Bayesian

AbstractGenetic data are often used to infer history, demographic changes or detect genes under selection. Inferential methods are commonly based on models making various strong assumptions: demography and population structures are supposed a priori known, the evolution of the genetic composition of a population does not affect demography nor population structure, and there is no selection nor interaction between and within genetic strains. In this paper, we present a stochastic birth-death model with competitive interaction to describe an asexual population, and we develop an inferential procedure for ecological, demographic and genetic parameters. We first show how genetic diversity and genealogies are related to birth and death rates, and to how individuals compete within and between strains. This leads us to propose an original model of phylogenies, with trait structure and interactions, that allows multiple merging. Second, we develop an Approximate Bayesian Computation framework to use our model for analyzing genetic data. We apply our procedure to simulated and real data. We show that the procedure give accurate estimate of the parameters of the model. We finally carry an illustration on real data and analyze the genetic diversity of microsatellites on Y-chromosomes sampled from Central Asia populations in order to test whether different social organizations show significantly different fertility.

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