Inference of complex population histories using whole-genome sequences from multiple populations

Steinrücken, Matthias; Kamm, John A.; Song, Yun S.

doi:10.1101/026591

Cited by 26 publications

(25 citation statements)

References 38 publications

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“…However, there is no shortage of complementary methods that are based on LD and haplotype information. Many of these methods were built on coalescent and hidden Markov models [25–29] and others incorporate inference of identity-by-descent (IBD) and identity-by-state (IBS) [30–33] (BOX). …”

Section: Introductionmentioning

confidence: 99%

“…Coupled with SMC model, Palacios et al (2015) [73] developed a Gaussian process-based Bayesian non-parametric method to infer population size history from a set of genealogies, which can be estimated by methods that infer the ancestral recombination graphs such as ARG weaver [74]. Sheehan et al (2013) [26] and then Steinrücken et al (2015) [29] introduced other methods based on sequentially Markov conditional sampling distribution framework (implemented in diCal and diCal2 , respectively). The input to this method is a collection of haplotype sequences.…”

Section: Introductionmentioning

confidence: 99%

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Explosive genetic evidence for explosive human population growth

Gao

Keinan

2016

Current Opinion in Genetics & Development

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The advent of next-generation sequencing technology has allowed the collection of vast amounts of genetic variation data. A recurring discovery from studying larger and larger samples of individuals had been the extreme, previously unexpected, excess of very rare genetic variants, which has been shown to be mostly due to the recent explosive growth of human populations. Here, we review recent literature that inferred recent changes in population size in different human populations and with different methodologies, with many pointing to recent explosive growth, especially in European populations for which more data has been available. We also review the state-of-the-art methods and software for the inference of historical population size changes that lead to these discoveries. Finally, we discuss the implications of recent population growth on personalized genomics, on purifying selection in the non-equilibrium state it entails and, as a consequence, on the genetic architecture underlying complex disease and the performance of mapping methods in discovering rare variants that contribute to complex disease risk.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Explosive genetic evidence for explosive human population growth

Gao

Keinan

2016

Current Opinion in Genetics & Development

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“…Among the methods designed for whole-genome sequence data of only a few individuals are those of Mailund et al (2012), Schiffels and Durbin (2014), and Steinrücken et al (2015). The fact that they are designed for full polymorphism data makes these methods computationally more expensive than JSFS-based methods.…”

mentioning

confidence: 99%

Inference of Gene Flow in the Process of Speciation: An Efficient Maximum-Likelihood Method for the Isolation-with-Initial-Migration Model

Costa

Wilkinson‐Herbots

2017

Genetics

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The isolation-with-migration (IM) model is commonly used to make inferences about gene flow during speciation, using polymorphism data. However, it has been reported that the parameter estimates obtained by fitting the IM model are very sensitive to the model’s assumptions—including the assumption of constant gene flow until the present. This article is concerned with the isolation-with-initial-migration (IIM) model, which drops precisely this assumption. In the IIM model, one ancestral population divides into two descendant subpopulations, between which there is an initial period of gene flow and a subsequent period of isolation. We derive a very fast method of fitting an extended version of the IIM model, which also allows for asymmetric gene flow and unequal population sizes. This is a maximum-likelihood method, applicable to data on the number of segregating sites between pairs of DNA sequences from a large number of independent loci. In addition to obtaining parameter estimates, our method can also be used, by means of likelihood-ratio tests, to distinguish between alternative models representing the following divergence scenarios: (a) divergence with potentially asymmetric gene flow until the present, (b) divergence with potentially asymmetric gene flow until some point in the past and in isolation since then, and (c) divergence in complete isolation. We illustrate the procedure on pairs of Drosophila sequences from ∼30,000 loci. The computing time needed to fit the most complex version of the model to this data set is only a couple of minutes. The R code to fit the IIM model can be found in the supplementary files of this article.

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“…For large sample sizes, approaches that use Monte-Carlo Markov Chain techniques (Rasmussen et al, 2014), suitable composite likelihood frameworks (Sheehan et al, 2013; Steinrücken et al, 2015), or representations of the local genealogical trees by lower-dimensional summaries (Schiffels and Durbin, 2014; Terhorst et al, 2017) have been developed. In the latter, the choice on how to represent the local genealogical trees affects the performance of the inference procedure.…”

Section: Introductionmentioning

confidence: 99%

Computing the joint distribution of the total tree length across loci in populations with variable size

Miroshnikov

Steinrücken

2017

Theoretical Population Biology

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In recent years, a number of methods have been developed to infer complex demographic histories, especially historical population size changes, from genomic sequence data. Coalescent Hidden Markov Models have proven to be particularly useful for this type of inference. Due to the Markovian structure of these models, an essential building block is the joint distribution of local genealogical trees, or statistics of these genealogies, at two neighboring loci in populations of variable size. Here, we present a novel method to compute the marginal and the joint distribution of the total length of the genealogical trees at two loci separated by at most one recombination event for samples of arbitrary size. To our knowledge, no method to compute these distributions has been presented in the literature to date. We show that they can be obtained from the solution of certain hyperbolic systems of partial differential equations. We present a numerical algorithm, based on the method of characteristics, that can be used to efficiently and accurately solve these systems and compute the marginal and the joint distributions. We demonstrate its utility to study properties of the joint distribution. Our flexible method can be straightforwardly extended to handle an arbitrary fixed number of recombination events, to include the distributions of other statistics of the genealogies as well, and can also be applied in structured populations.

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Inference of complex population histories using whole-genome sequences from multiple populations

Cited by 26 publications

References 38 publications

Explosive genetic evidence for explosive human population growth

Explosive genetic evidence for explosive human population growth

Inference of Gene Flow in the Process of Speciation: An Efficient Maximum-Likelihood Method for the Isolation-with-Initial-Migration Model

Computing the joint distribution of the total tree length across loci in populations with variable size

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