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

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.

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Peer Review #1 of "The divergence history of European blue mussel species reconstructed from Approximate Bayesian Computation: the effects of sequencing techniques and sampling strategies (v0.2)"

10.7287/peerj.5198v0.2/reviews/1 ◽

2018 ◽

Keyword(s):

Peer Review ◽

Approximate Bayesian Computation ◽

Blue Mussel ◽

Bayesian Computation ◽

Sampling Strategies ◽

Mussel Species ◽

History Of ◽

Approximate Bayesian

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

10.1101/452326 ◽

2018 ◽

Cited By ~ 3

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.

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Exploring Approximate Bayesian Computation for inferring recent demographic history with genomic markers in non-model species

10.1101/252650 ◽

2018 ◽

Author(s):

Joane S. Elleouet ◽

Sally N. Aitken

Keyword(s):

Sample Size ◽

Approximate Bayesian Computation ◽

Demographic History ◽

Initial Population ◽

Bayesian Computation ◽

Large Sample Size ◽

Spatial Expansion ◽

Model Species ◽

Genomic Markers ◽

Approximate Bayesian

AbstractApproximate Bayesian computation (ABC) is widely used to infer demographic history of populations and species using DNA markers. Genomic markers can now be developed for non-model species using reduced representation library (RRL) sequencing methods that select a fraction of the genome using targeted sequence capture or restriction enzymes (genotyping-by-sequencing, GBS). We explored the influence of marker number and length, knowledge of gametic phase, and tradeoffs between sample size and sequencing depth on the quality of demographic inferences performed with ABC. We focused on 2-population models of recent spatial expansion with varying numbers of unknown parameters. Performing ABC on simulated datasets with known parameter values, we found that the timing of a recent spatial expansion event could be precisely estimated in a 3-parameter model. Taking into account uncertainty in parameters such as initial population size and migration rate collectively decreased the precision of inferences dramatically. Phasing haplotypes did not improve results, regardless of sequence length. Numerous short sequences were as valuable as fewer, longer sequences, and performed best when a large sample size was sequenced at low individual depth, even when sequencing errors were added. ABC results were similar to results obtained with an alternative method based on the site frequency spectrum (SFS) when performed with unphased GBS-type markers. We conclude that unphased GBS-type datasets can be sufficient to precisely infer simple demographic models, and discuss possible improvements for the use of ABC with genomic data.

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

10.1101/2021.02.24.432626 ◽

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.

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

PeerJ ◽

10.7717/peerj.5198 ◽

2018 ◽

Vol 6 ◽

pp. e5198 ◽

Cited By ~ 15

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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Investigating speciation in face of polyploidization: what can we learn from approximate Bayesian computation approach?

10.1101/002527 ◽

2014 ◽

Author(s):

Camille Roux ◽

John Pannell

Keyword(s):

Approximate Bayesian Computation ◽

Sequence Data ◽

Demographic History ◽

Genomic Analysis ◽

Bayesian Computation ◽

Evolutionary Analysis ◽

Polyploid Species ◽

Model Species ◽

Karyotypic Analysis ◽

Approximate Bayesian

Despite its importance in the diversification of many eucaryote clades, particularly plants, detailed genomic analysis of polyploid species is still in its infancy, with published analysis of only a handful of model species to date. Fundamental questions concerning the origin of polyploid lineages (e.g., auto- vs. allopolyploidy) and the extent to which polyploid genomes display different modes of inheritance are poorly resolved for most polyploids, not least because they have hitherto required detailed karyotypic analysis or the analysis of allele segregation at multiple loci in pedigrees or artificial crosses, which are often not practical for non-model species. However, the increasing availability of sequence data for non-model species now presents an opportunity to apply established approaches for the evolutionary analysis of genomic data to polyploid species complexes. Here, we ask whether approximate Bayesian computation (ABC), applied to sequence data produced by next-generation sequencing technologies from polyploid taxa, allows correct inference of the evolutionary and demographic history of polyploid lineages and their close relatives. We use simulations to investigate how the number of sampled individuals, the number of surveyed loci and their length affect the accuracy and precision of evolutionary and demographic inferences by ABC, including the mode of polyploidisation, mode of inheritance of polyploid taxa, the relative timing of genome duplication and speciation, and effective populations sizes of contributing lineages. We also apply the ABC framework we develop to sequence data from diploid and polyploidy species of the plant genus Capsella, for which we infer an allopolyploid origin for tetra C. bursa-pastoris ≈ 90,000 years ago. In general, our results indicate that ABC is a promising and powerful method for uncovering the origin and subsequent evolution of polyploid species.

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Parapatric speciation with recurrent gene flow of two sexual dichromatic pheasants

10.1101/2021.11.20.469217 ◽

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).

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