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 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 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 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.

scholarly journals Parapatric speciation with recurrent gene flow of two sexual dichromatic pheasants

2021 ◽  
Author(s):  
Zheng Li ◽  
Jie Zhou ◽  
Minzhi Gao ◽  
Wei Liang ◽  
Lu Dong
Keyword(s):  
Sexual Selection ◽  
Gene Flow ◽  
Late Pleistocene ◽  
Approximate Bayesian Computation ◽  
De Novo ◽  
Demographic History ◽  
Population Expansion ◽  
Secondary Contact ◽  
Bayesian Computation ◽  
Approximate Bayesian

Background: Understanding speciation has long been a fundamental goal of evolutionary biology. It is widely accepted that speciation requires an interruption of gene flow to generate strong reproductive isolation between species, in which sexual selection may play an important role by generating and maintaining sexual dimorphism. The mechanism of how sexual selection operated in speciation with gene flow remains an open question and the subject of many research. Two species in genus Chrysolophus, Golden pheasant (C. pictus) and Lady Amherst's pheasant (C. amherstiae), which both exhibit significant plumage dichromatism, are currently parapatric in the southwest China with several hybrid recordings in field. Methods: In this research, we estimated the pattern of gene flow during the speciation of two pheasants using the Approximate Bayesian Computation (ABC) method based on the multiple genes data. With a new assembled de novo genome of Lady Amherst's pheasant and resequencing of widely distributed individuals, we reconstructed the demographic history of the two pheasants by pairwise sequentially Markovian coalescent (PSMC). Results: The results provide clear evidence that the gene flow between the two pheasants were consistent with the prediction of isolation with migration model for allopatric populations, indicating that there was long-term gene flow after the initially divergence (ca. 2.2 million years ago), and further support the secondary contact when included the parapatric populations since around 30 ka ongoing gene flow to now, which might be induced by the population expansion of the Golden pheasant in late Pleistocene. Conclusions: The results of the study support the scenario of speciation between Golden pheasant (C. pictus) and Lady Amherst's pheasant (C. amherstiae) with cycles of mixing-isolation-mixing due to the dynamics of natural selection and sexual selection in late Pleistocene that provide a good research system as evolutionary model to test reinforcement selection in speciation. Keywords: Golden pheasant (Chrysolophus pictus), Lady Amherst's pheasant (Chrysolophus amherstiae), speciation, gene flow, Approximate Bayesian Computation (ABC), Pairwise Sequentially Markovian coalescent (PSMC).

scholarly journals Distinguishing among complex evolutionary models using unphased whole-genome data through Approximate Bayesian Computation

2018 ◽  
Author(s):  
Silvia Ghirotto ◽  
Maria Teresa Vizzari ◽  
Francesca Tassi ◽  
Guido Barbujani ◽  
Andrea Benazzo
Keyword(s):  
Approximate Bayesian Computation ◽  
Simulated Data ◽  
Computational Effort ◽  
Bayesian Computation ◽  
Experimental Conditions ◽  
Genome Data ◽  
Complete Genomes ◽  
Anatomically Modern Humans ◽  
Demographic Processes ◽  
Approximate Bayesian

AbstractInferring past demographic histories is crucial in population genetics, and the amount of complete genomes now available should in principle facilitate this inference. In practice, however, the available inferential methods suffer from severe limitations. Although hundreds complete genomes can be simultaneously analyzed, complex demographic processes can easily exceed computational constraints, and the procedures to evaluate the reliability of the estimates contribute to increase the computational effort. Here we present an Approximate Bayesian Computation (ABC) framework, based on the Random Forest algorithm, to infer complex past population processes using complete genomes. To do this, we propose to summarize the data by the full genomic distribution of the four mutually exclusive categories of segregating sites (FDSS), a statistic fast to compute from unphased genome data. We constructed an efficient ABC pipeline and tested how accurately it allows one to recognize the true model among models of increasing complexity, using simulated data and taking into account different sampling strategies in terms of number of individuals analyzed, number and size of the genetic loci considered. We tested the power of the FDSS to be informative about even complex evolutionary histories and compared the results with those obtained summarizing the data through the unfolded Site Frequency Spectrum, thus highlighting for both statistics the experimental conditions maximizing the inferential power. Finally, we analyzed two datasets, testing models (a) on the dispersal of anatomically modern humans out of Africa and (b) the evolutionary relationships of the three species of Orangutan inhabiting Borneo and Sumatra.

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