Inferring past demographic changes from contemporary genetic data: A simulation‐based evaluation of the ABC methods implemented in diyabc

Inferring the demographic history of species and their populations is crucial to understand their contemporary distribution, abundance and adaptations. The high computational overhead of likelihood‐based inference approaches severely restricts their applicability to large data sets or complex models...

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Vydané v:	Molecular ecology resources Ročník 17; číslo 6; s. e94 - e110
Hlavní autori:	Cabrera, Andrea A., Palsbøll, Per J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Wiley Subscription Services, Inc 01.11.2017
Predmet:	Accuracy Adaptation Animals approximate Bayesian computation Approximation Bayesian analysis Bayesian theory Computer applications Computer Simulation computer software data collection Data recovery Datasets demographic inference Demographics Deoxyribonucleic acid DNA effective population size Gene sequencing Genetics, Population - methods Genotype Genotypes Likelihood Functions Mammals Mathematical models Microsatellite Repeats Mitochondrial DNA model selection Models, Genetic Nucleotide sequence Polymorphism, Single Nucleotide Population genetics Population number Populations Sequence Analysis, DNA Single-nucleotide polymorphism model selection population genetics demographic inference approximate Bayesian computation
ISSN:	1755-098X, 1755-0998, 1755-0998
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Shrnutí:	Inferring the demographic history of species and their populations is crucial to understand their contemporary distribution, abundance and adaptations. The high computational overhead of likelihood‐based inference approaches severely restricts their applicability to large data sets or complex models. In response to these restrictions, approximate Bayesian computation (ABC) methods have been developed to infer the demographic past of populations and species. Here, we present the results of an evaluation of the ABC‐based approach implemented in the popular software package diyabc using simulated data sets (mitochondrial DNA sequences, microsatellite genotypes and single nucleotide polymorphisms). We simulated population genetic data under five different simple, single‐population models to assess the model recovery rates as well as the bias and error of the parameter estimates. The ability of diyabc to recover the correct model was relatively low (0.49): 0.6 for the simplest models and 0.3 for the more complex models. The recovery rate improved significantly when reducing the number of candidate models from five to three (from 0.57 to 0.71). Among the parameters of interest, the effective population size was estimated at a higher accuracy compared to the timing of events. Increased amounts of genetic data did not significantly improve the accuracy of the parameter estimates. Some gains in accuracy and decreases in error were observed for scaled parameters (e.g., Neμ) compared to unscaled parameters (e.g., Ne and μ). We concluded that diyabc‐based assessments are not suited to capture a detailed demographic history, but might be efficient at capturing simple, major demographic changes.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1755-098X 1755-0998 1755-0998
DOI:	10.1111/1755-0998.12696