Genomes reveal marked differences in the adaptive evolution between orangutan species

Mattle-Greminger, Maja P.; Sonay, Tugce Bilgin; Nater, Alexander; Pybus, Marc; Desai, Tariq; Valles, Guillem de; Casals, Ferrán; Scally, Aylwyn; Bertranpetit, Jaume; Schaik, Carel P. van; Anisimova, Maria; Krützen, Michael

doi:10.1186/s13059-018-1562-6

Cited by 23 publications

(32 citation statements)

References 92 publications

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“…Recent advances in sequencing technologies enable us to obtain whole genome data for many individuals across several populations, even for non-model species [2][3][4][5]. In particular, inferring demography is of interest in its own right as it allow us to understand the history of existing and/or extinct species (population expansion, colonization of new habitats, past bottlenecks) [5][6][7]. Inferring demography is also necessary to generate null models for outlier scans, e.g.…”

Section: Introductionmentioning

confidence: 99%

Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data

et al. 2020

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Several methods based on the Sequential Markovian coalescence (SMC) have been developed that make use of genome sequence data to uncover population demographic history, which is of interest in its own right and is a key requirement to generate a null model for selection tests. While these methods can be applied to all possible kind of species, the underlying assumptions are sexual reproduction in each generation and nonoverlapping generations. However, in many plants, invertebrates, fungi and other taxa, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates/parameters of dormancy and of self-fertilization, and 2) the populations' past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.87 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations for the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation.

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Section: Introductionmentioning

confidence: 99%

Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data

et al. 2020

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“…The demographic history (the variation of effective population size over time) is linked to environmental and demographic changes that existing and/or extinct species have experienced (population expansion, colonization of new habitats, past bottlenecks) (Bergstrom et al, 2020;Gaut et al, 2018;Palkopoulou, Lipson, et al, 2018). Current statistical tools to estimate the demographic history rely on genomic data (Schraiber and Akey, 2015) and these inferences are often linked to archaeological or climatic data, providing novel insights on the evolutionary history (Barroso et al, Fulgione et al, 2018;Lau et al, 2020;H Li and Durbin, 2011;Mattle-Greminger et al, 2018;Palkopoulou, Mallick, et al, 2015;Yew et al, 2018). From these analyses, evidence for migration events have been uncovered (Browning et al, 2018;H Li and Durbin, 2011), as have genomic consequences of human activities on other species (Choo et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Limits and Convergence properties of the Sequentially Markovian Coalescent

Sellinger

Awad

Tellier

2020

Preprint

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Many methods based on the Sequentially Markovian Coalescent (SMC) have been and are being developed. These methods make use of genome sequence data to uncover population demographic history. More recently, new methods even allow the simultaneous estimation of the demographic history and other biological variables, extending the original theoretical frameworks. Those methods can be applied to many different species, under different model assumptions, in hopes of unlocking the population/species evolutionary history. Although convergence proofs in particular cases have been given using simulated data, a clear outline of the performance limits of these methods is lacking. We here explore the limits of this methodology, as well as present a tool that can be used to help users quantify what information can be confidently retrieved from given datasets. In addition, we study the consequences for inference accuracy of the violation of hypotheses and assumptions of SMC approaches, such as the presence of transposable elements, variable recombination and mutation rates along the sequence and SNP call errors. We also provide a new interpretation of the SMC through the use of the estimated transition matrix and offer recommendations for the most efficient use of these methods under budget constraints, notably through the building of data sets that would be better adapted for the biological question at hand.

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“…Recent advances in sequencing technologies enable us to obtain whole genome data for many individuals across several populations even for non-model species [49, 50, 25, 36]. In particular, inferring demography is of interest in its own right so as to understand the history of existing and/or extinct species (population expansion, colonization of new habitats, past bottlenecks) [58, 28, 36]. Inferring demography is also necessary to generate null models for outlier scans, e.g.…”

Section: Introductionmentioning

confidence: 99%

Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data

Sellinger

Awad

Möst

et al. 2019

Preprint

View full text Add to dashboard Cite

AbstractSeveral methods based on the Sequential Markovian Coalescent (SMC) have been developed to use full genome sequence data to uncover population demographic history, which is of interest in its own right and a key requirement to generate a null model for selection tests. While these methods can be applied to all possible species, the underlying assumptions are sexual reproduction at each generation and no overlap of generations. However, in many plant, invertebrate, fungi and other species, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates of seed/egg-bank and of self-fertilization, and 2) the populations’ past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.8 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations on the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation.

show abstract

Genomes reveal marked differences in the adaptive evolution between orangutan species

Cited by 23 publications

References 92 publications

Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data

Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data

Limits and Convergence properties of the Sequentially Markovian Coalescent

Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data

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