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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Bibliographic Details
Published in:Molecular ecology resources Vol. 17; no. 6; pp. e94 - e110
Main Authors: Cabrera, Andrea A., Palsbøll, Per J.
Format: Journal Article
Language:English
Published: England Wiley Subscription Services, Inc 01.11.2017
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1755-098X
1755-0998
1755-0998
DOI:10.1111/1755-0998.12696