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 The divergence history of European blue mussel species reconstructed from Approximate Bayesian Computation: the effects of sequencing techniques and sampling strategies

2018 ◽  
Author(s):  
Christelle Fraïsse ◽  
Camille Roux ◽  
Pierre-Alexandre Gagnaire ◽  
Jonathan Romiguier ◽  
Nicolas Faivre ◽  
...  
Keyword(s):  
Frequency Spectrum ◽  
Approximate Bayesian Computation ◽  
Exome Capture ◽  
Bayesian Computation ◽  
Sampling Strategies ◽  
Migration Rates ◽  
History Of ◽  
Site Frequency Spectrum ◽  
Approximate Bayesian ◽  
Number Of Individuals

AbstractGenome-scale diversity data are increasingly available in a variety of biological systems, and can be used to reconstruct the past evolutionary history of species divergence. However, extracting the full demographic information from these data is not trivial, and requires inferential methods that account for the diversity of coalescent histories throughout the genome. Here, we evaluate the potential and limitations of one such approach. We reexamine a well-known system of mussel sister species, using the joint site frequency spectrum (jSFS) of synonymous mutations computed either from exome capture or RNA-seq, in an Approximate Bayesian Computation (ABC) framework. We first assess the best sampling strategy (number of: individuals, loci, and bins in the jSFS), and show that model selection is robust to variation in the number of individuals and loci. In contrast, different binning choices when summarizing the joint site frequency spectrum, strongly affect the results: including classes of low and high frequency shared polymorphisms can more effectively reveal recent migration events. We then take advantage of the flexibility of ABC to compare more realistic models of speciation, including variation in migration rates through time (i.e. periodic connectivity) and across genes (i.e. genome-wide heterogeneity in migration rates). We show that these models were consistently selected as the most probable, suggesting that mussels have experienced a complex history of gene flow during divergence and that the species boundary is semi-permeable. Our work provides a comprehensive evaluation of ABC demographic inference in mussels based on the coding site frequency spectrum, and supplies guidelines for employing different sequencing techniques and sampling strategies. We emphasize, perhaps surprisingly, that inferences are less limited by the volume of data, than by the way in which they are analyzed.

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 Approximate Bayesian computation untangles signatures of contemporary and historical hybridization between two endangered species

2021 ◽  
Author(s):  
Hannes Dittberner ◽  
Aurelien Tellier ◽  
Juliette de Meaux
Keyword(s):  
Gene Flow ◽  
Endangered Species ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Sympatric Population ◽  
Genome Data ◽  
Contemporary Gene Flow ◽  
History Of ◽  
Low Levels ◽  
Approximate Bayesian

ABSTRACTContemporary gene flow, when resumed after a period of isolation, can have crucial consequences for endangered species, as it can both increase the supply of adaptive alleles and erode local adaptation. Determining the history of gene flow and thus the importance of contemporary hybridization, however, is notoriously difficult. Here, we focus on two endangered plant species, Arabis nemorensis and A. sagittata, which hybridize naturally in a sympatric population located on the banks of the Rhine. Using reduced genome sequencing, we determined the phylogeography of the two taxa but report only a unique sympatric population. Molecular variation in chloroplast DNA indicated that A. sagittata is the principal receiver of gene flow. Applying classical D-statistics and its derivatives to whole-genome data of 35 accessions, we detect gene flow not only in the sympatric population but also among allopatric populations. Using an Approximate Bayesian computation approach, we identify the model that best describes the history of gene flow between these taxa. This model shows that low levels of gene flow have persisted long after speciation. Around 10 000 years ago, gene flow stopped and a period of complete isolation began. Eventually, a hotspot of contemporary hybridization was formed in the unique sympatric population. Occasional sympatry may have helped protect these lineages from extinction in spite of their extremely low diversity.

scholarly journals Reconstructing the demographic history of orang-utans using Approximate Bayesian Computation

2015 ◽  
Vol 24 (2) ◽  
pp. 310-327 ◽  
Author(s):  
Alexander Nater ◽  
Maja P. Greminger ◽  
Natasha Arora ◽  
Carel P. van Schaik ◽  
Benoit Goossens ◽  
...  
Keyword(s):  
Approximate Bayesian Computation ◽  
Demographic History ◽  
Bayesian Computation ◽  
History Of ◽  
Approximate Bayesian

scholarly journals The divergence history of European blue mussel species reconstructed from Approximate Bayesian Computation: the effects of sequencing techniques and sampling strategies

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5198 ◽  
Author(s):  
Christelle Fraïsse ◽  
Camille Roux ◽  
Pierre-Alexandre Gagnaire ◽  
Jonathan Romiguier ◽  
Nicolas Faivre ◽  
...  
Keyword(s):  
Approximate Bayesian Computation ◽  
Blue Mussel ◽  
Exome Capture ◽  
Bayesian Computation ◽  
Sampling Strategies ◽  
Migration Rates ◽  
Synonymous Mutations ◽  
History Of ◽  
Approximate Bayesian ◽  
Number Of Individuals

Genome-scale diversity data are increasingly available in a variety of biological systems, and can be used to reconstruct the past evolutionary history of species divergence. However, extracting the full demographic information from these data is not trivial, and requires inferential methods that account for the diversity of coalescent histories throughout the genome. Here, we evaluate the potential and limitations of one such approach. We reexamine a well-known system of mussel sister species, using the joint site frequency spectrum (jSFS) of synonymous mutations computed either from exome capture or RNA-seq, in an Approximate Bayesian Computation (ABC) framework. We first assess the best sampling strategy (number of: individuals, loci, and bins in the jSFS), and show that model selection is robust to variation in the number of individuals and loci. In contrast, different binning choices when summarizing the jSFS, strongly affect the results: including classes of low and high frequency shared polymorphisms can more effectively reveal recent migration events. We then take advantage of the flexibility of ABC to compare more realistic models of speciation, including variation in migration rates through time (i.e., periodic connectivity) and across genes (i.e., genome-wide heterogeneity in migration rates). We show that these models were consistently selected as the most probable, suggesting that mussels have experienced a complex history of gene flow during divergence and that the species boundary is semi-permeable. Our work provides a comprehensive evaluation of ABC demographic inference in mussels based on the coding jSFS, and supplies guidelines for employing different sequencing techniques and sampling strategies. We emphasize, perhaps surprisingly, that inferences are less limited by the volume of data, than by the way in which they are analyzed.

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