Elevated substitution rates estimated from ancient DNA sequences

Ancient DNA sequences are able to offer valuable insights into molecular evolutionary processes, which are not directly accessible via modern DNA. They are particularly suitable for the estimation of substitution rates because their ages provide calibrating information in phylogenetic analyses, circumventing the difficult task of choosing independent calibration points. The substitution rates obtained from such datasets have typically been high, falling between the rates estimated from pedigrees and species phylogenies. Many of these estimates have been made using a Bayesian phylogenetic method that explicitly accommodates heterochronous data. Stimulated by recent criticism of this method, we present a comprehensive simulation study that validates its performance. For datasets of moderate size, it produces accurate estimates of rates, while appearing robust to assumptions about demographic history. We then analyse a large collection of 749 ancient and 727 modern DNA sequences from 19 species of animals, plants and bacteria. Our new estimates confirm that the substitution rates estimated from ancient DNA sequences are elevated above long-term phylogenetic levels.     

Elevated substitution rate estimates from ancient DNA: model violation and bias of Bayesian methods

The increasing ability to extract and sequence DNA from noncontemporaneous tissue offers biologists the opportunity to analyse ancient DNA (aDNA) together with modern DNA (mDNA) to address the taxonomy of extinct species, evolutionary origins, historical phylogeography and biogeography. Perhaps more exciting are recent developments in coalescence-based Bayesian inference that offer the potential to use temporal information from aDNA and mDNA for the estimation of substitution rates and divergence dates as an alternative to fossil and geological calibration. This comes at a time of growing interest in the possibility of time dependency for molecular rate estimates. In this study, we provide a critical assessment of Bayesian Markov chain Monte Carlo (MCMC) analysis for the estimation of substitution rate using simulated samples of aDNA and mDNA. We conclude that the current models and priors employed in Bayesian MCMC analysis of heterochronous mtDNA are susceptible to an upward bias in the estimation of substitution rates because of model misspecification when the data come from populations with less than simple demographic histories, including sudden short-lived population bottlenecks or pronounced population structure. However, when model misspecification is only mild, then the 95% highest posterior density intervals provide adequate frequentist coverage of the true rates.     

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation

Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. AVAILABILITY: The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc.     

Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0)

Background  

Approximate Bayesian computation (ABC) is a recent flexible class of Monte-Carlo algorithms increasingly used to make model-based inference on complex evolutionary scenarios that have acted on natural populations. The software DIYABC offers a user-friendly interface allowing non-expert users to consider population histories involving any combination of population divergences, admixtures and population size changes. We here describe and illustrate new developments of this software that mainly include (i) inference from DNA sequence data in addition or separately to microsatellite data, (ii) the possibility to analyze five categories of loci considering balanced or non balanced sex ratios: autosomal diploid, autosomal haploid, X-linked, Y-linked and mitochondrial, and (iii) the possibility to perform model checking computation to assess the "goodness-of-fit" of a model, a feature of ABC analysis that has been so far neglected.     

Test for simultaneous divergence using approximate Bayesian computation

Comparative phylogeographic studies often reveal disparate levels of sequence divergence between lineages spanning a common geographic barrier, leading to the conclusion that isolation was nonsynchronous. However, only rarely do researchers account for the expected variance associated with ancestral coalescence and among-taxon variation in demographic history. We introduce a flexible approximate Bayesian computational (ABC) framework that can test for simultaneous divergence (TSD) using a hierarchical model that incorporates idiosyncratic differences in demographic history across taxon pairs. The method is tested across a range of conditions and is shown to be accurate even with single-locus mitochondrial DNA (mtDNA) data. We apply this method to a landmark dataset of putative simultaneous vicariance, eight geminate echinoid taxon pairs thought to have been split by the Isthmus of Panama 3.1 million years ago. The ABC posterior estimates are not consistent with a history of simultaneous vicariance given these data. Subsequent ABC estimates under a constrained model that assumes two divergence times across the eight taxon pairs suggests simultaneous divergence 3.1 million years ago in seven of the taxon pairs and a more recent divergence in the remaining taxon pair. These ABC estimates on the simultaneous divergence of the seven taxon pairs correspond to a DNA substitution rate of approximately 1.59% per lineage per million years at the mtDNA cytochrome oxidase I gene. This ABC framework can easily be modified to analyze single taxon-pair datasets and/or be expanded to include multiple loci, migration, recombination, and other idiosyncratic demographic histories. The flexible aspect of ABC and its built-in evaluation of estimator bias and statistical power has the potential to greatly enhance statistical rigor in phylogeographic studies.     

Combining contemporary and ancient DNA in population genetic and phylogeographical studies

The analysis of ancient DNA in a population genetic or phylogeographical framework is an emerging field, as traditional analytical tools were largely developed for the purpose of analysing data sampled from a single time point. Markov chain Monte Carlo approaches have been successfully developed for the analysis of heterochronous sequence data from closed panmictic populations. However, attributing genetic differences between temporal samples to mutational events between time points requires the consideration of other factors that may also result in genetic differentiation. Geographical effects are an obvious factor for species exhibiting geographical structuring of genetic variation. The departure from a closed panmictic model require researchers to either exploit software developed for the analysis of isochronous data, take advantage of simulation approaches using algorithms developed for heterochronous data, or explore approximate Bayesian computation. Here, we review statistical approaches employed and available software for the joint analysis of ancient and modern DNA, and where appropriate we suggest how these may be further developed.     

iPhy: an integrated phylogenetic workbench for supermatrix analyses

Background  

The increasing availability of molecular sequence data means that the accuracy of future phylogenetic studies is likely to by limited by systematic bias and taxon choice rather than by data. In order to take advantage of increasing datasets, user-friendly tools are required to facilitate phylogenetic analyses and to reduce duplication of dataset assembly efforts. Current phylogenetic pipelines are dependency-heavy and have significant technical barriers to use.     

ABC as a flexible framework to estimate demography over space and time: some cons,many pros

The analysis of genetic variation to estimate demographic and historical parameters and to quantitatively compare alternative scenarios recently gained a powerful and flexible approach: the Approximate Bayesian Computation (ABC). The likelihood functions does not need to be theoretically specified, but posterior distributions can be approximated by simulation even assuming very complex population models including both natural and human‐induced processes. Prior information can be easily incorporated and the quality of the results can be analysed with rather limited additional effort. ABC is not a statistical analysis per se, but rather a statistical framework and any specific application is a sort of hybrid between a simulation and a data‐analysis study. Complete software packages performing the necessary steps under a set of models and for specific genetic markers are already available, but the flexibility of the method is better exploited combining different programs. Many questions relevant in ecology can be addressed using ABC, but adequate amount of time should be dedicated to decide among alternative options and to evaluate the results. In this paper we will describe and critically comment on the different steps of an ABC analysis, analyse some of the published applications of ABC and provide user guidelines.     

Reconstruction of a beech population bottleneck using archival demographic information and Bayesian analysis of genetic data

Range expansion and contraction has occurred in the history of most species and can seriously impact patterns of genetic diversity. Historical data about range change are rare and generally appropriate for studies at large scales, whereas the individual pollen and seed dispersal events that form the basis of geneflow and colonization generally occur at a local scale. In this study, we investigated range change in Fagus sylvatica on Mont Ventoux, France, using historical data from 1838 to the present and approximate Bayesian computation (ABC) analyses of genetic data. From the historical data, we identified a population minimum in 1845 and located remnant populations at least 200 years old. The ABC analysis selected a demographic scenario with three populations, corresponding to two remnant populations and one area of recent expansion. It also identified expansion from a smaller ancestral population but did not find that this expansion followed a population bottleneck, as suggested by the historical data. Despite a strong support to the selected scenario for our data set, the ABC approach showed a low power to discriminate among scenarios on average and a low ability to accurately estimate effective population sizes and divergence dates, probably due to the temporal scale of the study. This study provides an unusual opportunity to test ABC analysis in a system with a well-documented demographic history and identify discrepancies between the results of historical, classical population genetic and ABC analyses. The results also provide valuable insights into genetic processes at work at a fine spatial and temporal scale in range change and colonization.     

Exploring Approximate Bayesian Computation for inferring recent demographic history with genomic markers in nonmodel species

Approximate Bayesian computation (ABC) is widely used to infer demographic history of populations and species using DNA markers. Genomic markers can now be developed for nonmodel 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 two‐population models of recent spatial expansion with varying numbers of unknown parameters. Performing ABC on simulated data sets with known parameter values, we found that the timing of a recent spatial expansion event could be precisely estimated in a three‐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 data sets can be sufficient to precisely infer simple demographic models, and discuss possible improvements for the use of ABC with genomic data.     

Cas9-chromatin binding information enables more accurate CRISPR off-target prediction

The CRISPR system has become a powerful biological tool with a wide range of applications. However, improving targeting specificity and accurately predicting potential off-targets remains a significant goal. Here, we introduce a web-based CRISPR/Cas9 Off-target Prediction and Identification Tool (CROP-IT) that performs improved off-target binding and cleavage site predictions. Unlike existing prediction programs that solely use DNA sequence information; CROP-IT integrates whole genome level biological information from existing Cas9 binding and cleavage data sets. Utilizing whole-genome chromatin state information from 125 human cell types further enhances its computational prediction power. Comparative analyses on experimentally validated datasets show that CROP-IT outperforms existing computational algorithms in predicting both Cas9 binding as well as cleavage sites. With a user-friendly web-interface, CROP-IT outputs scored and ranked list of potential off-targets that enables improved guide RNA design and more accurate prediction of Cas9 binding or cleavage sites.     

msABC: a modification of Hudson's ms to facilitate multi-locus ABC analysis

With the availability of whole-genome sequence data biologists are able to test hypotheses regarding the demography of populations. Furthermore, the advancement of the Approximate Bayesian Computation (ABC) methodology allows the demographic inference to be performed in a simple framework using summary statistics. We present here msABC, a coalescent-based software that facilitates the simulation of multi-locus data, suitable for an ABC analysis. msABC is based on Hudson's ms algorithm, which is used extensively for simulating neutral demographic histories of populations. The flexibility of the original algorithm has been extended so that sample size may vary among loci, missing data can be incorporated in simulations and calculations, and a multitude of summary statistics for single or multiple populations is generated. The source code of msABC is available at http://bio.lmu.de/~pavlidis/msabc or upon request from the authors.     

ABCtoolbox: a versatile toolkit for approximate Bayesian computations

Background  

The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations.     

Geography best explains global patterns of genetic diversity and postglacial co‐expansion in marine turtles

For many species, climate oscillations drove cycles of population contraction during cool glacial periods followed by expansion during interglacials. Some groups, however, show evidence of uniform and synchronous expansion, while others display differences in the timing and extent of demographic change. We compared demographic histories inferred from genetic data across marine turtle species to identify responses to postglacial warming shared across taxa and to examine drivers of past demographic change at the global scale. Using coalescent simulations and approximate Bayesian computation (ABC), we estimated demographic parameters, including the likelihood of past population expansion, from a mitochondrial data set encompassing 23 previously identified lineages from all seven marine turtle species. For lineages with a high posterior probability of expansion, we conducted a hierarchical ABC analysis to estimate the proportion of lineages expanding synchronously and the timing of synchronous expansion. We used Bayesian model averaging to identify variables associated with expansion and genetic diversity. Approximately 60% of extant marine turtle lineages showed evidence of expansion, with the rest mainly exhibiting patterns of genetic diversity most consistent with population stability. For lineages showing expansion, there was a strong signal of synchronous expansion after the Last Glacial Maximum. Expansion and genetic diversity were best explained by ocean basin and the degree of endemism for a given lineage. Geographic differences in sensitivity to climate change have implications for prioritizing conservation actions in marine turtles as well as for identifying areas of past demographic stability and potential resilience to future climate change for broadly distributed taxa.     

Demographic Divergence History of Pied Flycatcher and Collared Flycatcher Inferred from Whole-Genome Re-sequencing Data

Profound knowledge of demographic history is a prerequisite for the understanding and inference of processes involved in the evolution of population differentiation and speciation. Together with new coalescent-based methods, the recent availability of genome-wide data enables investigation of differentiation and divergence processes at unprecedented depth. We combined two powerful approaches, full Approximate Bayesian Computation analysis (ABC) and pairwise sequentially Markovian coalescent modeling (PSMC), to reconstruct the demographic history of the split between two avian speciation model species, the pied flycatcher and collared flycatcher. Using whole-genome re-sequencing data from 20 individuals, we investigated 15 demographic models including different levels and patterns of gene flow, and changes in effective population size over time. ABC provided high support for recent (mode 0.3 my, range <0.7 my) species divergence, declines in effective population size of both species since their initial divergence, and unidirectional recent gene flow from pied flycatcher into collared flycatcher. The estimated divergence time and population size changes, supported by PSMC results, suggest that the ancestral species persisted through one of the glacial periods of middle Pleistocene and then split into two large populations that first increased in size before going through severe bottlenecks and expanding into their current ranges. Secondary contact appears to have been established after the last glacial maximum. The severity of the bottlenecks at the last glacial maximum is indicated by the discrepancy between current effective population sizes (20,000–80,000) and census sizes (5–50 million birds) of the two species. The recent divergence time challenges the supposition that avian speciation is a relatively slow process with extended times for intrinsic postzygotic reproductive barriers to evolve. Our study emphasizes the importance of using genome-wide data to unravel tangled demographic histories. Moreover, it constitutes one of the first examples of the inference of divergence history from genome-wide data in non-model species.     

On optimal selection of summary statistics for approximate Bayesian computation

How best to summarize large and complex datasets is a problem that arises in many areas of science. We approach it from the point of view of seeking data summaries that minimize the average squared error of the posterior distribution for a parameter of interest under approximate Bayesian computation (ABC). In ABC, simulation under the model replaces computation of the likelihood, which is convenient for many complex models. Simulated and observed datasets are usually compared using summary statistics, typically in practice chosen on the basis of the investigator's intuition and established practice in the field. We propose two algorithms for automated choice of efficient data summaries. Firstly, we motivate minimisation of the estimated entropy of the posterior approximation as a heuristic for the selection of summary statistics. Secondly, we propose a two-stage procedure: the minimum-entropy algorithm is used to identify simulated datasets close to that observed, and these are each successively regarded as observed datasets for which the mean root integrated squared error of the ABC posterior approximation is minimized over sets of summary statistics. In a simulation study, we both singly and jointly inferred the scaled mutation and recombination parameters from a population sample of DNA sequences. The computationally-fast minimum entropy algorithm showed a modest improvement over existing methods while our two-stage procedure showed substantial and highly-significant further improvement for both univariate and bivariate inferences. We found that the optimal set of summary statistics was highly dataset specific, suggesting that more generally there may be no globally-optimal choice, which argues for a new selection for each dataset even if the model and target of inference are unchanged.     

Integrating multiple lines of evidence into historical biogeography hypothesis testing: a Bison bison case study

One of the grand goals of historical biogeography is to understand how and why species'' population sizes and distributions change over time. Multiple types of data drawn from disparate fields, combined into a single modelling framework, are necessary to document changes in a species''s demography and distribution, and to determine the drivers responsible for change. Yet truly integrated approaches are challenging and rarely performed. Here, we discuss a modelling framework that integrates spatio-temporal fossil data, ancient DNA, palaeoclimatological reconstructions, bioclimatic envelope modelling and coalescence models in order to statistically test alternative hypotheses of demographic and potential distributional changes for the iconic American bison (Bison bison). Using different assumptions about the evolution of the bioclimatic niche, we generate hypothetical distributional and demographic histories of the species. We then test these demographic models by comparing the genetic signature predicted by serial coalescence against sequence data derived from subfossils and modern populations. Our results supported demographic models that include both climate and human-associated drivers of population declines. This synthetic approach, integrating palaeoclimatology, bioclimatic envelopes, serial coalescence, spatio-temporal fossil data and heterochronous DNA sequences, improves understanding of species'' historical biogeography by allowing consideration of both abiotic and biotic interactions at the population level.     

Bayesian inference of fine-scale recombination rates using population genomic data

Recently, several statistical methods for estimating fine-scale recombination rates using population samples have been developed. However, currently available methods that can be applied to large-scale data are limited to approximated likelihoods. Here, we developed a full-likelihood Markov chain Monte Carlo method for estimating recombination rate under a Bayesian framework. Genealogies underlying a sampling of chromosomes are effectively modelled by using marginal individual single nucleotide polymorphism genealogies related through an ancestral recombination graph. The method is compared with two existing composite-likelihood methods using simulated data.Simulation studies show that our method performs well for different simulation scenarios. The method is applied to two human population genetic variation datasets that have been studied by sperm typing. Our results are consistent with the estimates from sperm crossover analysis.     

The use of approximate Bayesian computation in conservation genetics and its application in a case study on yellow-eyed penguins

The inference of demographic parameters from genetic data has become an integral part of conservation studies. A group of Bayesian methods developed originally in population genetics, known as approximate Bayesian computation (ABC), has been shown to be particularly useful for the estimation of such parameters. These methods do not need to evaluate likelihood functions analytically and can therefore be used even while assuming complex models. In this paper we describe the ABC approach and identify specific parts of its algorithm that are being the subject of intensive studies in order to further expand its usability. Furthermore, we discuss applications of this Bayesian algorithm in conservation studies, providing insights on the potentialities of these tools. Finally, we present a case study in which we use a simple Isolation-Migration model to estimate a number of demographic parameters of two populations of yellow-eyed penguins (Megadyptes antipodes) in New Zealand. The resulting estimates confirm our current understanding of M. antipodes dynamic, demographic history and provide new insights into the expansion this species has undergone during the last centuries.     

Population structure,connectivity, and demographic history of an apex marine predator,the bull shark Carcharhinus leucas

Knowledge of population structure, connectivity, and effective population size remains limited for many marine apex predators, including the bull shark Carcharhinus leucas. This large‐bodied coastal shark is distributed worldwide in warm temperate and tropical waters, and uses estuaries and rivers as nurseries. As an apex predator, the bull shark likely plays a vital ecological role within marine food webs, but is at risk due to inshore habitat degradation and various fishing pressures. We investigated the bull shark's global population structure and demographic history by analyzing the genetic diversity of 370 individuals from 11 different locations using 25 microsatellite loci and three mitochondrial genes (CR, nd4, and cytb). Both types of markers revealed clustering between sharks from the Western Atlantic and those from the Western Pacific and the Western Indian Ocean, with no contemporary gene flow. Microsatellite data suggested low differentiation between the Western Indian Ocean and the Western Pacific, but substantial differentiation was found using mitochondrial DNA. Integrating information from both types of markers and using Bayesian computation with a random forest procedure (ABC‐RF), this discordance was found to be due to a complete lack of contemporary gene flow. High genetic connectivity was found both within the Western Indian Ocean and within the Western Pacific. In conclusion, these results suggest important structuring of bull shark populations globally with important gene flow occurring along coastlines, highlighting the need for management and conservation plans on regional scales rather than oceanic basin scale.