scholarly journals Inference of direction, diversity, and frequency of HIV-1 transmission using approximate Bayesian computation

2016 ◽  
Author(s):  
Ethan O. Romero-Severson ◽  
Ingo Bulla ◽  
Nick Hengartner ◽  
Inês Bártolo ◽  
Ana Abecasis ◽  
...  
Keyword(s):  
Approximate Bayesian Computation ◽  
Bayes Factor ◽  
Heterosexual Transmission ◽  
Transmission Model ◽  
Bayesian Computation ◽  
Founding Population ◽  
Abc Method ◽  
Approximate Bayesian ◽  
Prevention Methods ◽  
Hiv 1

ABSTRACTDiversity of the founding population of Human Immunodeficiency Virus Type 1 (HIV-1) transmissions raises many important biological, clinical, and epidemiological issues. In up to 40% of sexual infections there is clear evidence for multiple founding variants, which can influence the efficacy of putative prevention methods and the reconstruction of epidemiologic histories. To measure the diversity of the founding population and to compute the probability of alternative transmission scenarios, while explicitly taking phylogenetic uncertainty into account, we created an Approximate Bayesian Computation (ABC) method based on a set of statistics measuring phylogenetic topology, branch lengths, and genetic diversity. We applied our method to a heterosexual transmission pair showing a complex paraphyletic-polyphyletic donor-recipient phylogenetic topology. We found evidence identifying the donor that was consistent with the known facts of the case (Bayes factor >20). We also found that while the evidence for ongoing transmission between the pair was as good or better than the singular transmission event model, it was only viable when the rate of ongoing transmission was implausibly high (~1/day). We concluded that the singular transmission model, which was able to estimate the diversity of the founding population (mean 7% substitutions/site), was more biologically plausible. Our study provides a formal inference framework to investigate HIV-1 direction, diversity, and frequency of transmission. The ability to measure the diversity of founding populations in both simple and complex transmission situations is essential to understanding the relationship between the phylogeny and epidemiology of HIV-1 as well as in efforts developing new prevention technologies.

scholarly journals Inference of the worldwide invasion routes of the pinewood nematodeBursaphelenchus xylophilususing approximate Bayesian computation analysis

2018 ◽  
Author(s):  
Sophie Mallez ◽  
Chantal Castagnone ◽  
Eric Lombaert ◽  
Philippe Castagnone-Sereno ◽  
Thomas Guillemaud
Keyword(s):  
Approximate Bayesian Computation ◽  
Unknown Origin ◽  
Bayesian Computation ◽  
Pinewood Nematode ◽  
Invasion Routes ◽  
The Usa ◽  
History Of ◽  
Abc Method ◽  
Approximate Bayesian ◽  
Inference Methods

ABSTRACTPopulation genetics have been greatly beneficial to improve knowledge about biological invasions. Model-based genetic inference methods, such as approximate Bayesian computation (ABC), have brought this improvement to a higher level and are now essential tools to decipher the invasion routes of any invasive species. In this paper, we performed ABC analyses to shed light on the pinewood nematode (PWN) worldwide invasion routes and to identify the source of European populations. Originating from North America, this microscopic worm has been invading Asia since 1905 and Europe since 1999, causing tremendous damage on pine forests. Using microsatellite data, we demonstrated the existence of multiple introduction events in Japan (one involving individuals originating from the USA and one involving individuals with an unknown origin) and China (one involving individuals originating from the USA and one involving individuals originating from Japan). We also found that Portuguese samples had an American origin. Although we observed some discrepancies between descriptive genetic methods and the ABC method, which are worth investigating and are discussed here, the ABC approach definitely helped clarify the worldwide history of the PWN invasion.

scholarly journals Testing hypotheses of diversification in Panamanian frogs and freshwater fishes using hierarchical approximate Bayesian computation with model averaging

2018 ◽  
Author(s):  
Justin C Bagley ◽  
Michael J Hickerson ◽  
Jerald B Johnson
Keyword(s):  
Freshwater Fish ◽  
Dna Sequences ◽  
Approximate Bayesian Computation ◽  
Bayes Factor ◽  
Fish Assemblages ◽  
Model Averaging ◽  
External Information ◽  
Bayesian Computation ◽  
Genetic Structuring ◽  
Approximate Bayesian

Most Neotropical frog and freshwater fish species sampled to date show phylogeographic breaks along the Pacific coast of the Isthmus of Panama, with lineages in Costa Rica and western Panama isolated from central Panama. We examine temporal patterns of diversification of taxa across this ‘western Panama isthmus’ (WPI) break to test hypotheses about the origin of species geographical distributions and genetic structuring in this region. We tested for synchronous diversification of four codistributed frog taxon-pairs and three fish taxon-pairs sharing the WPI break using hierarchical approximate Bayesian computation with model averaging based on mitochondrial DNA sequences. We also estimated lineage divergence times using full-Bayesian models. Several of our results supported synchronous divergences within the frog and freshwater fish assemblages; however, Bayes factor support was equivocal for or against synchronous or asynchronous diversification. Nevertheless, we infer that frog populations were likely isolated by one or multiple Pliocene–Pleistocene events more recently than predicted by previous models, while fish genetic diversity was structured by Pleistocene events. By integrating our results with external information from geology and elevational sea level modeling, we discuss the implications of our findings for understanding the biogeographical scenario of the diversification of Panamanian frogs and fishes. Consistent with the ‘Bermingham/Martin model’ (Mol. Ecol. 1998, 7: 499-517), we conclude that the regional fish assemblage was fractured by processes shaping isthmian landscapes during the Pleistocene glaciations, including drainage basin isolation during lowered sea levels.

scholarly journals Testing Hypotheses of Diversification in Panamanian Frogs and Freshwater Fishes Using Hierarchical Approximate Bayesian Computation with Model Averaging

Diversity ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 120 ◽  
Author(s):  
Justin Bagley ◽  
Michael Hickerson ◽  
Jerald Johnson
Keyword(s):  
Freshwater Fish ◽  
Dna Sequences ◽  
Approximate Bayesian Computation ◽  
Bayes Factor ◽  
Fish Assemblages ◽  
Model Averaging ◽  
External Information ◽  
Bayesian Computation ◽  
Genetic Structuring ◽  
Approximate Bayesian

Most Neotropical frog and freshwater fish species sampled to date show phylogeographic breaks along the Pacific coast of the Isthmus of Panama, with lineages in Costa Rica and western Panama isolated from central Panama. We examine temporal patterns of diversification of taxa across this ‘western Panama isthmus’ (WPI) break to test hypotheses about the origin of species geographical distributions and genetic structuring in this region. We tested for synchronous diversification of four codistributed frog taxon-pairs and three fish taxon-pairs sharing the WPI break using hierarchical approximate Bayesian computation with model averaging based on mitochondrial DNA sequences. We also estimated lineage divergence times using full-Bayesian models. Several of our results supported synchronous divergences within the frog and freshwater fish assemblages; however, Bayes factor support was equivocal for or against synchronous or asynchronous diversification. Nevertheless, we infer that frog populations were likely isolated by one or multiple Pliocene–Pleistocene events more recently than predicted by previous models, while fish genetic diversity was structured by Pleistocene events. By integrating our results with external information from geology and elevational sea level modeling, we discuss the implications of our findings for understanding the biogeographical scenario of the diversification of Panamanian frogs and fishes. Consistent with the ‘Bermingham/Martin model’ (Molecular Ecology 1998, 7, 499–517), we conclude that the regional fish assemblage was fractured by processes shaping isthmian landscapes during the Pleistocene glaciations, including drainage basin isolation during lowered sea levels.

scholarly journals Testing hypotheses of diversification in Panamanian frogs and freshwater fishes using hierarchical approximate Bayesian computation with model averaging

2018 ◽  
Author(s):  
Justin C Bagley ◽  
Michael J Hickerson ◽  
Jerald B Johnson
Keyword(s):  
Freshwater Fish ◽  
Dna Sequences ◽  
Approximate Bayesian Computation ◽  
Bayes Factor ◽  
Fish Assemblages ◽  
Model Averaging ◽  
External Information ◽  
Bayesian Computation ◽  
Genetic Structuring ◽  
Approximate Bayesian

Most Neotropical frog and freshwater fish species sampled to date show phylogeographic breaks along the Pacific coast of the Isthmus of Panama, with lineages in Costa Rica and western Panama isolated from central Panama. We examine temporal patterns of diversification of taxa across this ‘western Panama isthmus’ (WPI) break to test hypotheses about the origin of species geographical distributions and genetic structuring in this region. We tested for synchronous diversification of four codistributed frog taxon-pairs and three fish taxon-pairs sharing the WPI break using hierarchical approximate Bayesian computation with model averaging based on mitochondrial DNA sequences. We also estimated lineage divergence times using full-Bayesian models. Several of our results supported synchronous divergences within the frog and freshwater fish assemblages; however, Bayes factor support was equivocal for or against synchronous or asynchronous diversification. Nevertheless, we infer that frog populations were likely isolated by one or multiple Pliocene–Pleistocene events more recently than predicted by previous models, while fish genetic diversity was structured by Pleistocene events. By integrating our results with external information from geology and elevational sea level modeling, we discuss the implications of our findings for understanding the biogeographical scenario of the diversification of Panamanian frogs and fishes. Consistent with the ‘Bermingham/Martin model’ (Mol. Ecol. 1998, 7: 499-517), we conclude that the regional fish assemblage was fractured by processes shaping isthmian landscapes during the Pleistocene glaciations, including drainage basin isolation during lowered sea levels.

scholarly journals Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Shuqiang Wang ◽  
Yanyan Shen ◽  
Changhong Shi ◽  
Tao Wang ◽  
Zhiming Wei ◽  
...  
Keyword(s):  
Biological Networks ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Principal Component ◽  
High Sensitivity ◽  
Bayesian Computation ◽  
Abc Method ◽  
Approximate Bayesian ◽  
Low Sensitivity ◽  
Biological Circuits

Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes.

scholarly journals Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

2008 ◽  
Vol 6 (31) ◽  
pp. 187-202 ◽  
Author(s):  
Tina Toni ◽  
David Welch ◽  
Natalja Strelkowa ◽  
Andreas Ipsen ◽  
Michael P.H Stumpf
Keyword(s):  
Model Selection ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Bayesian Computation ◽  
Parameter Inference ◽  
Credible Intervals ◽  
Computation Scheme ◽  
Abc Method ◽  
Approximate Bayesian ◽  
Better Than

Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.

scholarly journals Donor-Recipient Identification in Para- and Poly-phyletic Trees Under Alternative HIV-1 Transmission Hypotheses Using Approximate Bayesian Computation

Genetics ◽  
2017 ◽  
Vol 207 (3) ◽  
pp. 1089-1101 ◽  
Author(s):  
Ethan O. Romero-Severson ◽  
Ingo Bulla ◽  
Nick Hengartner ◽  
Inês Bártolo ◽  
Ana Abecasis ◽  
...  
Keyword(s):  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Approximate Bayesian ◽  
Hiv 1 ◽  
Phyletic Trees

scholarly journals Weighted approximate Bayesian computation via Sanov’s theorem

2021 ◽  
Author(s):  
Cecilia Viscardi ◽  
Michele Boreale ◽  
Fabio Corradi
Keyword(s):  
Large Deviations ◽  
Posterior Distribution ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Information Theoretic ◽  
Discrete Random Variables ◽  
Positive Weights ◽  
Approximate Bayesian ◽  
Information Theoretic Method ◽  
Computational Resources

AbstractWe consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values of the parameters, once given as input to the generative model, rarely lead to simulations resembling the observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all to the representation of the parameter’s posterior distribution. This leads to a very large number of required simulations and/or a waste of computational resources, as well as to distortions in the computed posterior distribution. To mitigate this problem, we propose an algorithm, referred to as the Large Deviations Weighted Approximate Bayesian Computation algorithm, where, via Sanov’s Theorem, strictly positive weights are computed for all proposed parameters, thus avoiding the rejection step altogether. In order to derive a computable asymptotic approximation from Sanov’s result, we adopt the information theoretic “method of types” formulation of the method of Large Deviations, thus restricting our attention to models for i.i.d. discrete random variables. Finally, we experimentally evaluate our method through a proof-of-concept implementation.

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