abc: an R package for approximate Bayesian computation (ABC)

Csilléry, Katalin; Ochsenbein, F.; Blum, Michaël G. B.

doi:10.1111/j.2041-210x.2011.00179.x

Cited by 681 publications

(810 citation statements)

References 36 publications

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“…Recently there has been a great increase in the application of ABC methods for problems in population genetics, and this has largely been driven by the availability of useful software packages (for example, ABCtoolbox package (Wegmann et al, 2009), ABC package (Csillery et al, 2012) or DIY-ABC (Cornuet et al, 2008)). These authors have developed a number of important enhancements to the original rejection and regression algorithms (reviewed in Beaumont (2010)).…”

Section: Abc Approach Based On Rejection/regression Algorithmmentioning

confidence: 99%

Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation

et al. 2013

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The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets.

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Section: Abc Approach Based On Rejection/regression Algorithmmentioning

confidence: 99%

Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation

et al. 2013

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“…First, we simulated population samples experiencing no selection and asked how well we could recover the true parameters of the model using diffusion approximations to the SFS via the @a@i software package (Gutenkunst et al 2009), or with a set of commonly used summary statistics (see Methods) via ABC (Thornton 2009;Csilléry et al 2012); we address the effects of selection on PSMC later, as this method requires a different sampling scheme. Briefly, we used both of these methods to fit the focal demographic model to data sampled from 500 unlinked simulated loci and repeated this process on 100 replicate simulated "genomes" (Methods).…”

Section: Demographic Parameter Estimates Are Biased By Positive Selecmentioning

confidence: 99%

“…For the constant-size model, we constructed a new sampling set of 5.0 3 10 5 simulated data sets, and for the three variable-size models, we used the same sampling sets generated for parameter estimation. We used the R package abc to conduct model choice, performing logistic regression-based estimation (using the "mnlogistic" method) of the posterior probabilities of a model (Csilléry et al 2012). For this procedure, we set the tolerance parameter to 0.1 for our single-sweep data sets.…”

Section: Parameter Estimation and Model Selection With ›A›imentioning

confidence: 99%

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

2016

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The availability of large-scale population genomic sequence data has resulted in an explosion in efforts to infer the demographic histories of natural populations across a broad range of organisms. As demographic events alter coalescent genealogies, they leave detectable signatures in patterns of genetic variation within and between populations. Accordingly, a variety of approaches have been designed to leverage population genetic data to uncover the footprints of demographic change in the genome. The vast majority of these methods make the simplifying assumption that the measures of genetic variation used as their input are unaffected by natural selection. However, natural selection can dramatically skew patterns of variation not only at selected sites, but at linked, neutral loci as well. Here we assess the impact of recent positive selection on demographic inference by characterizing the performance of three popular methods through extensive simulation of data sets with varying numbers of linked selective sweeps. In particular, we examined three different demographic models relevant to a number of species, finding that positive selection can bias parameter estimates of each of these models-often severely. We find that selection can lead to incorrect inferences of population size changes when none have occurred. Moreover, we show that linked selection can lead to incorrect demographic model selection, when multiple demographic scenarios are compared. We argue that natural populations may experience the amount of recent positive selection required to skew inferences. These results suggest that demographic studies conducted in many species to date may have exaggerated the extent and frequency of population size changes.KEYWORDS population genetics; positive selection; demographic inference T HE widespread availability of population genomic data has spurred a new generation of studies aimed at understanding the histories of natural populations from a host of model and nonmodel organisms alike. In particular, genomescale variation data allows for inference of demographic factors such as population size changes, the timing and ordering of population splits, migration rates between populations, and the founding of admixed populations (Pool et al. 2010;Pickrell and Pritchard 2012;Sousa and Hey 2013). Such efforts can refine our picture of demographic events inferred from the archaeological record (e.g., Fagundes et al. 2007;Goebel et al. 2008), or reveal such events in species where no archaeological data are available, and can aid conservation efforts by complementing census data (e.g., Hájková et al. 2007;Garrick et al. 2015).Population genomic data sets are well suited for this task simply because demographic changes leave their mark on patterns of genetic variation. Recent population growth, for example, will result in an excess of rare variation compared to equilibrium expectations (Fu 1997), while population contraction will result in an excess of intermediate frequency alleles (Maruyama and Fuerst 198...

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“…The 5% of the simulations having the smallest Euclidean distances with the observed empirical data were retained to perform the model choice step. We applied the weighted multinomial logistic regression postsampling adjustment (Beaumont, 2008) implemented in the 'abc' package (Csilléry et al, 2012). We validated our model selection procedure (independently for each population) by using pseudo-observed data sets (PODS) (Peter et al, 2010).…”

Section: Population Genetics and Abc-based Analysesmentioning

confidence: 99%

Insights into the origin and distribution of biodiversity in the Brazilian Atlantic forest hot spot: a statistical phylogeographic study using a low-dispersal organism

et al. 2014

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The relative importance of the processes that generate and maintain biodiversity is a major and controversial topic in evolutionary biology with large implications for conservation management. The Atlantic Forest of Brazil, one of the world's richest biodiversity hot spots, is severely damaged by human activities. To formulate an efficient conservation policy, a good understanding of spatial and temporal biodiversity patterns and their underlying evolutionary mechanisms is required. With this aim, we performed a comprehensive phylogeographic study using a low-dispersal organism, the land planarian species Cephaloflexa bergi (Platyhelminthes, Tricladida). Analysing multi-locus DNA sequence variation under the Approximate Bayesian Computation framework, we evaluated two scenarios proposed to explain the diversity of Southern Atlantic Forest (SAF) region. We found that most sampled localities harbour high levels of genetic diversity, with lineages sharing common ancestors that predate the Pleistocene. Remarkably, we detected the molecular hallmark of the isolation-by-distance effect and little evidence of a recent colonization of SAF localities; nevertheless, some populations might result from very recent secondary contacts. We conclude that extant SAF biodiversity originated and has been shaped by complex interactions between ancient geological events and more recent evolutionary processes, whereas Pleistocene climate changes had a minor influence in generating present-day diversity. We also demonstrate that land planarians are an advantageous biological model for making phylogeographic and, particularly, fine-scale evolutionary inferences, and propose appropriate conservation policies.

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abc: an R package for approximate Bayesian computation (ABC)

Cited by 681 publications

References 36 publications

Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation

Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

Insights into the origin and distribution of biodiversity in the Brazilian Atlantic forest hot spot: a statistical phylogeographic study using a low-dispersal organism

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