Coalescence 2.0: a multiple branching of recent theoretical developments and their applications

Tellier, Aurélien; Lemaire, Christophe

doi:10.1101/001933

Cited by 28 publications

(41 citation statements)

References 81 publications

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“…Berestycki (2009) or, with a biological perspective, Tellier and Lemaire (2014). When Λ is associated with the beta-distribution with parameters 2 − α and α for 1 ≤ α < 2 , these rates can be given explicitly by…”

Section: Effect Of Lumpingmentioning

confidence: 99%

“…Briefly, multiplemerger coalescents may be more appropriate for organisms exhibiting HFSODs than the Kingman coalescent (cf., e.g., Beckenbach 1994;Sargsyan and Wakeley 2008;Hedgecock and Pudovkin 2011) [see also the review by Tellier and Lemaire (2014)]. Recent population growth as well as multiple-merger coalescents may lead to an excess of singletons in the SFS compared with the classical Kingman coalescent-based SFS, which e.g., contributes to shifting Tajima's D values (Tajima 1989b) to the negative.…”

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confidence: 99%

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confidence: 99%

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Can the Site-Frequency Spectrum Distinguish Exponential Population Growth from Multiple-Merger Coalescents?

Birkner²,

et al. 2015

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The ability of the site-frequency spectrum (SFS) to reflect the particularities of gene genealogies exhibiting multiple mergers of ancestral lines as opposed to those obtained in the presence of population growth is our focus. An excess of singletons is a wellknown characteristic of both population growth and multiple mergers. Other aspects of the SFS, in particular, the weight of the right tail, are, however, affected in specific ways by the two model classes. Using an approximate likelihood method and minimum-distance statistics, our estimates of statistical power indicate that exponential and algebraic growth can indeed be distinguished from multiplemerger coalescents, even for moderate sample sizes, if the number of segregating sites is high enough. A normalized version of the SFS (nSFS) is also used as a summary statistic in an approximate Bayesian computation (ABC) approach. The results give further positive evidence as to the general eligibility of the SFS to distinguish between the different histories.KEYWORDS coalescent; multiple mergers; population growth; approximate maximum likelihood test; approximate Bayesian computation; sitefrequency spectrum T HE site-frequency spectrum (SFS) at a given locus is one of the most important and popular statistics based on genetic data sampled from a natural population. In combination with the postulation of the assumptions of the infinitelymany-sites mutation model (Watterson, 1975) and a suitable underlying coalescent framework, the SFS allows one to draw inferences about evolutionary parameters, such as coalescent parameters associated with multiple-merger coalescents or population-growth models.The Kingman coalescent, developed by Kingman (1982a, b,c), Hudson (1983a,b), and Tajima (1983), describing the random ancestral relations among DNA sequences drawn from natural populations, is a prominent and widely used model from which one can make predictions about genetic diversity. Many quantities of interest, such as the expected values and covariances of the SFS associated with the Kingman coalescent, are easily computed thanks to results by Fu (1995). The robustness of the Kingman coalescent is quite remarkable; indeed, a large number of genealogy models can be shown to have the Kingman coalescent or a variant thereof as their limit process (cf., e.g., Möhle 1998). A large volume of work is thus devoted to inference methods based on the Kingman coalescent [see, e.g., Donnelly and Tavaré (1995), Hudson (1990), Nordborg (2001), Hein et al. (2005), and Wakeley (2007) for reviews].However, many evolutionary histories can lead to significant deviations from the Kingman coalescent model. Such deviations can be detected using a variety of statistical tools, such as Tajima's D (Tajima 1989a), Fu and Li's D (Fu and Li 1993), and Fay and Wu 's H (Fay and Wu 2000), which are all functions of the SFS. However, they do not always allow one to identify the actual evolutionary mechanisms leading to such deviations. Developing statistical tools that allow one to dist...

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Section: Effect Of Lumpingmentioning

confidence: 99%

mentioning

confidence: 99%

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Can the Site-Frequency Spectrum Distinguish Exponential Population Growth from Multiple-Merger Coalescents?

Birkner²,

et al. 2015

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“…Biological factors such as sweepstake reproductive events, population bottlenecks, and recurrent positive selection may lead to skewed distributions in offspring number (Eldon and Wakeley, 2006;Li et al, 2014); examples include various prokaryotes (plague), fungi (Zymoseptoria tritici, Puccinia striiformis, rusts, mildew, oomycetes), plants (Arabidopsis thaliana), marine organisms (sardines, cods, salmon, oysters), crustaceans (Daphnia) and insects (aphids) (reviewed in Tellier and Lemaire, 2014). The resulting skewed offspring distributions can also result in elevated linkage disequilibrium despite frequent recombination, as linkage depends not only on recombination rate, but also on the degree of skewness in offspring distributions (Eldon and Wakeley, 2008;Birkner et al, 2013).…”

Section: Skewed Offspring Distributions and The MMCmentioning

confidence: 99%

On the importance of skewed offspring distributions and background selection in virus population genetics

et al. 2016

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Many features of virus populations make them excellent candidates for population genetic study, including a very high rate of mutation, high levels of nucleotide diversity, exceptionally large census population sizes, and frequent positive selection. However, these attributes also mean that special care must be taken in population genetic inference. For example, highly skewed offspring distributions, frequent and severe population bottleneck events associated with infection and compartmentalization, and strong purifying selection all affect the distribution of genetic variation but are often not taken into account. Here, we draw particular attention to multiple-merger coalescent events and background selection, discuss potential misinference associated with these processes, and highlight potential avenues for better incorporating them into future population genetic analyses.

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“…Many variations of this type of model exist, including the finite island model (Wright, 1943) and one-and two-dimensional stepping stone models (Kimura and Weiss, 1964), that differ in the assumption of how migration between demes occurs. Backward-in-time discrete population structure can be modeled using the structured coalescent that comes in several forms (Tellier and Lemaire, 2014). Such discrete models of population structure have been successfully incorporated into inferential methods such as SPLATCHE (Currat et al, 2004) that explore complex demographic scenarios.…”

Section: Introductionmentioning

confidence: 99%

Demographic inference under a spatially continuous coalescent model

2016

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In contrast with the classical population genetics theory that models population structure as discrete panmictic units connected by migration, many populations exhibit heterogeneous spatial gradients in population connectivity across semi-continuous habitats. The historical dynamics of such spatially structured populations can be captured by a spatially explicit coalescent model recently proposed by Etheridge (2008) and Barton et al. (2010a, b) and whereby allelic lineages are distributed in a twodimensional spatial continuum and move within this continuum based on extinction and coalescent events. Though theoretically rigorous, this model, which we here refer to as the continuum model, has not yet been implemented for demographic inference. To this end, here we introduce and demonstrate a statistical pipeline that couples the coalescent simulator of Kelleher et al. (2014) that simulates genealogies under the continuum model, with an approximate Bayesian computation (ABC) framework for parameter estimation of neighborhood size (that is, the number of locally breeding individuals) and dispersal ability (that is, the distance an offspring can travel within a generation). Using empirically informed simulations and simulation-based ABC crossvalidation, we first show that neighborhood size can be accurately estimated. We then apply our pipeline to the South African endemic shrub species Berkheya cuneata to use the resulting estimates of dispersal ability and neighborhood size to infer the average population density of the species. More generally, we show that spatially explicit coalescent models can be successfully integrated into model-based demographic inference.

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Coalescence 2.0: a multiple branching of recent theoretical developments and their applications

Cited by 28 publications

References 81 publications

Can the Site-Frequency Spectrum Distinguish Exponential Population Growth from Multiple-Merger Coalescents?

Can the Site-Frequency Spectrum Distinguish Exponential Population Growth from Multiple-Merger Coalescents?

On the importance of skewed offspring distributions and background selection in virus population genetics

Demographic inference under a spatially continuous coalescent model

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