Biases in Demographic Modeling Affect Our Understanding of Recent Divergence.

Testing among competing demographic models of divergence has become an important component of evolutionary research in model and non-model organisms. However, the effect of unaccounted demographic events on model choice and parameter estimation remains largely unexplored. Using extensive simulations, we demonstrate that under realistic divergence scenarios, failure to account for population size (Ne) changes in daughter and ancestral populations leads to strong biases in divergence time estimates as well as model choice. We illustrate these issues reconstructing the recent demographic history of North Sea and Baltic Sea turbots (Scophthalmus maximus) by testing 16 isolation with migration (IM) and 16 secondary contact (SC) scenarios, modeling changes in Ne as well as the effects of linked selection and barrier loci. Failure to account for changes in Ne resulted in selecting SC models with long periods of strict isolation and divergence times preceding the formation of the Baltic Sea. In contrast, models accounting for Ne changes suggest recent (<6 kya) divergence with constant gene flow. We further show how interpreting genomic landscapes of differentiation can help discerning among competing models. For example, in the turbot data, islands of differentiation show signatures of recent selective sweeps, rather than old divergence resisting secondary introgression. The results have broad implications for the study of population divergence by highlighting the potential effects of unmodeled changes in Ne on demographic inference. Tested models should aim at representing realistic divergence scenarios for the target taxa, and extreme caution should always be exercised when interpreting results of demographic modeling.

Genomic inference of complex domestication histories in three Solanaceae species.

Domestication is a human-induced selection process that imprints the genomes of domesticated populations over a short evolutionary time scale and that occurs in a given demographic context. Reconstructing historical gene flow, effective population size changes and their timing is therefore of fundamental interest to understand how plant demography and human selection jointly shape genomic divergence during domestication. Yet, the comparison under a single statistical framework of independent domestication histories across different crop species has been little evaluated so far. Thus, it is unclear whether domestication leads to convergent demographic changes that similarly affect crop genomes. To address this question, we used existing and new transcriptome data on three crop species of Solanaceae (eggplant, pepper and tomato), together with their close wild relatives. We fitted twelve demographic models of increasing complexity on the unfolded joint allele frequency spectrum for each wild/crop pair, and we found evidence for both shared and species-specific demographic processes between species. A convergent history of domestication with gene flow was inferred for all three species, along with evidence of strong reduction in the effective population size during the cultivation stage of tomato and pepper. The absence of any reduction in size of the crop in eggplant stands out from the classical view of the domestication process; as does the existence of a "protracted period" of management before cultivation. Our results also suggest divergent management strategies of modern cultivars among species as their current demography substantially differs. Finally, the timing of domestication is species-specific and supported by the few historical records available.

Speciation history of European (Anguilla anguilla) and American eel (A. rostrata), analysed using genomic data.

Speciation in the ocean could differ from terrestrial environments due to fewer barriers to gene flow. Hence, sympatric speciation might be common, with American and European eel being candidates for exemplifying this. They show disjunct continental distributions on both sides of the Atlantic, but spawn in overlapping regions of the Sargasso Sea from where juveniles are advected to North American, European and North African coasts. Hybridization and introgression are known to occur, with hybrids almost exclusively observed in Iceland. Different speciation scenarios have been suggested, involving either vicariance or sympatric ecological speciation. Using RAD sequencing and whole-genome sequencing data from parental species and F1 hybrids, we analysed speciation history based on the joint allele frequency spectrum (JAFS) and pairwise sequentially Markovian coalescent (PSMC) plots. JAFS supported a model involving a split without gene flow 150,000-160,000 generations ago, followed by secondary contact 87,000-92,000 generations ago, with 64% of the genome experiencing restricted gene flow. This supports vicariance rather than sympatric speciation, likely associated with Pleistocene glaciation cycles and ocean current changes. Whole-genome PSMC analysis of F1 hybrids from Iceland suggested divergence 200,000 generations ago and indicated subsequent gene flow rather than strict isolation. Finally, simulations showed that results from both approaches (JAFS and PSMC) were congruent. Hence, there is strong evidence against sympatric speciation in North Atlantic eels. These results reiterate the need for careful consideration of cases of possible sympatric speciation, as even in seemingly barrier-free oceanic environments palaeoceanographic factors may have promoted vicariance and allopatric speciation.

Allele frequency spectra in structured populations: Novel-allele probabilities under the labelled coalescent.

We address the effect of population structure on key properties of the Ewens sampling formula. We use our previously-introduced inductive method for determining exact allele frequency spectrum (AFS) probabilities under the infinite-allele model of mutation and population structure for samples of arbitrary size. Fundamental to the sampling distribution is the novel-allele probability, the probability that given the pattern of variation in the present sample, the next gene sampled belongs to an as-yet-unobserved allelic class. Unlike the case for panmictic populations, the novel-allele probability depends on the AFS of the present sample. We derive a recursion that directly provides the marginal novel-allele probability across AFSs, obviating the need first to determine the probability of each AFS. Our explorations suggest that the marginal novel-allele probability tends to be greater for initial samples comprising fewer alleles and for sampling configurations in which the next-observed gene derives from a deme different from that of the majority of the present sample. Comparison to the efficient importance sampling proposals developed by De Iorio and Griffiths and colleagues indicates that their approximation for the novel-allele probability generally agrees with the true marginal, although it may tend to overestimate the marginal in cases in which the novel-allele probability is high and migration rates are low.

Genomic signatures of sympatric speciation with historical and contemporary gene flow in a tropical anthozoan (Hexacorallia: Actiniaria).

Sympatric diversification is recognized to have played an important role in the evolution of biodiversity. However, an in situ sympatric origin for codistributed taxa is difficult to demonstrate because different evolutionary processes can lead to similar biogeographic outcomes, especially in ecosystems that can readily facilitate secondary contact due to a lack of hard barriers to dispersal. Here we use a genomic (ddRADseq), model-based approach to delimit a species complex of tropical sea anemones that are codistributed on coral reefs throughout the Tropical Western Atlantic. We use coalescent simulations in fastsimcoal2 and ordinary differential equations in Moments to test competing diversification scenarios that span the allopatric-sympatric continuum. Our results suggest that the corkscrew sea anemone Bartholomea annulata is a cryptic species complex whose members are codistributed throughout their range. Simulation and model selection analyses from both approaches suggest these lineages experienced historical and contemporary gene flow, supporting a sympatric origin, but an alternative secondary contact model receives appreciable model support in fastsimcoal2. Leveraging the genome of the closely related Exaiptasia diaphana, we identify five loci under divergent selection between cryptic B. annulata lineages that fall within mRNA transcripts or CDS regions. Our study provides a rare empirical, genomic example of sympatric speciation in a tropical anthozoan and the first range-wide molecular study of a tropical sea anemone, underscoring that anemone diversity is under-described in the tropics, and highlighting the need for additional systematic studies into these ecologically and economically important species.

Inductive determination of allele frequency spectrum probabilities in structured populations.

We present a method for inductively determining exact allele frequency spectrum (AFS) probabilities for samples derived from a population comprising two demes under the infinite-allele model of mutation. This method builds on a labeled coalescent argument to extend the Ewens sampling formula (ESF) to structured populations. A key departure from the panmictic case is that the AFS conditioned on the number of alleles in the sample is no longer independent of the scaled mutation rate (θ). In particular, biallelic site frequency spectra, widely-used in explorations of genome-wide patterns of variation, depend on the mutation rate in structured populations. Variation in the rate of substitution across loci and through time may contribute to apparent distortions of site frequency spectra exhibited by samples derived from structured populations.

Do ecological communities disperse across biogeographic barriers as a unit?

Biogeographic barriers have long been implicated as drivers of biological diversification, but how these barriers influence co-occurring taxa can vary depending on factors intrinsic to the organism and in their relationships with other species. Due to the interdependence among taxa, ecological communities present a compelling opportunity to explore how interactions among species may lead to a shared response to historical events. Here we collect single nucleotide polymorphism data from five commensal arthropods associated with the Sarracenia alata carnivorous pitcher plant, and test for codiversification across the Mississippi River, a major biogeographic barrier in the southeastern United States. Population genetic structure in three of the ecologically dependent arthropods mirrors that of the host pitcher plant, with divergence time estimates suggesting two of the species (the pitcher plant moth Exyra semicrocea and a flesh fly Sarcophaga sarraceniae) dispersed synchronously across this barrier along with the pitcher plant. Patterns in population size and genetic diversity suggest the plant and ecologically dependent arthropods dispersed from east to west across the Mississippi River. In contrast, species less dependent on the plant ecologically show discordant phylogeographic patterns. This study demonstrates that ecological relationships may be an important predictor of codiversification, and supports recent suggestions that organismal trait data should be prominently featured in comparative phylogeographic investigations.

Forward and backward evolutionary processes and allele frequency spectrum in a cancer cell population.

A cancer grows from a single cell, thereby constituting a large cell population. In this work, we are interested in how mutations accumulate in a cancer cell population. We provide a theoretical framework of the stochastic process in a cancer cell population and obtain near exact expressions of allele frequency spectrum or AFS (only continuous approximation is involved) from both forward and backward treatments under a simple setting; all cells undergo cell divisions and die at constant rates, b and d, respectively, such that the entire population grows exponentially. This setting means that once a parental cancer cell is established, in the following growth phase, all mutations are assumed to have no effect on b or d (i.e., neutral or passengers). Our theoretical results show that the difference from organismal population genetics is mainly in the coalescent time scale, and the mutation rate is defined per cell division, not per time unit (e.g., generation). Except for these two factors, the basic logic is very similar between organismal and cancer population genetics, indicating that a number of well established theories of organismal population genetics could be translated to cancer population genetics with simple modifications.

Sequence Capture versus Restriction Site Associated DNA Sequencing for Shallow Systematics.

Sequence capture and restriction site associated DNA sequencing (RAD-Seq) are two genomic enrichment strategies for applying next-generation sequencing technologies to systematics studies. At shallow timescales, such as within species, RAD-Seq has been widely adopted among researchers, although there has been little discussion of the potential limitations and benefits of RAD-Seq and sequence capture. We discuss a series of issues that may impact the utility of sequence capture and RAD-Seq data for shallow systematics in non-model species. We review prior studies that used both methods, and investigate differences between the methods by re-analyzing existing RAD-Seq and sequence capture data sets from a Neotropical bird (Xenops minutus). We suggest that the strengths of RAD-Seq data sets for shallow systematics are the wide dispersion of markers across the genome, the relative ease and cost of laboratory work, the deep coverage and read overlap at recovered loci, and the high overall information that results. Sequence capture's benefits include flexibility and repeatability in the genomic regions targeted, success using low-quality samples, more straightforward read orthology assessment, and higher per-locus information content. The utility of a method in systematics, however, rests not only on its performance within a study, but on the comparability of data sets and inferences with those of prior work. In RAD-Seq data sets, comparability is compromised by low overlap of orthologous markers across species and the sensitivity of genetic diversity in a data set to an interaction between the level of natural heterozygosity in the samples examined and the parameters used for orthology assessment. In contrast, sequence capture of conserved genomic regions permits interrogation of the same loci across divergent species, which is preferable for maintaining comparability among data sets and studies for the purpose of drawing general conclusions about the impact of historical processes across biotas. We argue that sequence capture should be given greater attention as a method of obtaining data for studies in shallow systematics and comparative phylogeography.

The non-equilibrium allele frequency spectrum in a Poisson random field framework.

In population genetic studies, the allele frequency spectrum (AFS) efficiently summarizes genome-wide polymorphism data and shapes a variety of allele frequency-based summary statistics. While existing theory typically features equilibrium conditions, emerging methodology requires an analytical understanding of the build-up of the allele frequencies over time. In this work, we use the framework of Poisson random fields to derive new representations of the non-equilibrium AFS for the case of a Wright-Fisher population model with selection. In our approach, the AFS is a scaling-limit of the expectation of a Poisson stochastic integral and the representation of the non-equilibrium AFS arises in terms of a fixation time probability distribution. The known duality between the Wright-Fisher diffusion process and a birth and death process generalizing Kingman's coalescent yields an additional representation. The results carry over to the setting of a random sample drawn from the population and provide the non-equilibrium behavior of sample statistics. Our findings are consistent with and extend a previous approach where the non-equilibrium AFS solves a partial differential forward equation with a non-traditional boundary condition. Moreover, we provide a bridge to previous coalescent-based work, and hence tie several frameworks together. Since frequency-based summary statistics are widely used in population genetics, for example, to identify candidate loci of adaptive evolution, to infer the demographic history of a population, or to improve our understanding of the underlying mechanics of speciation events, the presented results are potentially useful for a broad range of topics.

Testing neutrality at copy-number-variable loci under the finite-allele and finite-site models.

Copy-number variation (CNV) is an important form of DNA structural variation because a certain proportion of genomes in many eukaryotic species can contribute to such variations. Owing to the differences between CNVs and single nucleotide polymorphisms (SNPs) in size, mutation rate and maintaining mechanism, it is more realistic to characterize CNV evolution under the finite-allele and finite-site models. Here, we propose a method to test multiple CNVs neutrality under the finite-allele and finite-site models and the assumption of mutation-drift process. The statistical property of the method is evaluated through Monte Carlo simulations under the effects of the sample size, the scaled mutation rates, the number of CNVs, the population demographic change, and selection. Different from Tajima's D test, a bootstrap or a permutation approach is suggested to conduct a neutrality test. Application of this method is illustrated using the diploid CNV genotypes measured in discrete copy numbers in 11 HapMap phase III populations. The results show that the mutation-drift process can explain the variation of genome-wide CNVs among 1184 individuals (856 CNVs, ∼0.02Mb on average in size), irrespective of the historical demographic changes. Patterns from allele-frequency-spectrum analysis also support the hypothesis of neutral CNVs. Our results suggest that most human chromosomal changes in healthy individuals via unbalanced rearrangements of the segments with certain sizes are neutral.

Modeling biases from low-pass genome sequencing to enable accurate population genetic inferences.

Low-pass genome sequencing is cost-effective and enables analysis of large cohorts. However, it introduces biases by reducing heterozygous genotypes and low-frequency alleles, impacting subsequent analyses such as demographic history inference. We developed a probabilistic model of low-pass biases from the Genome Analysis Toolkit (GATK) multi-sample calling pipeline, and we implemented it in the population genomic inference software dadi. We evaluated the model using simulated low-pass datasets and found that it alleviated low-pass biases in inferred demographic parameters. We further validated the model by downsampling 1000 Genomes Project data, demonstrating its effectiveness on real data. Our model is widely applicable and substantially improves model-based inferences from low-pass population genomic data.

The effect of mutation subtypes on the allele frequency spectrum and population genetics inference.

Population genetics has adapted as technological advances in next-generation sequencing have resulted in an exponential increase of genetic data. A common approach to efficiently analyze genetic variation present in large sequencing data is through the allele frequency spectrum, defined as the distribution of allele frequencies in a sample. While the frequency spectrum serves to summarize patterns of genetic variation, it implicitly assumes mutation types (A→C vs C→T) as interchangeable. However, mutations of different types arise and spread due to spatial and temporal variation in forces such as mutation rate and biased gene conversion that result in heterogeneity in the distribution of allele frequencies across sites. In this work, we explore the impact of this simplification on multiple aspects of population genetic modeling. As a site's mutation rate is strongly affected by flanking nucleotides, we defined a mutation subtype by the base pair change and adjacent nucleotides (e.g. AAA→ATA) and systematically assessed the heterogeneity in the frequency spectrum across 96 distinct 3-mer mutation subtypes using n = 3556 whole-genome sequenced individuals of European ancestry. We observed substantial variation across the subtype-specific frequency spectra, with some of the variation being influenced by molecular factors previously identified for single base mutation types. Estimates of model parameters from demographic inference performed for each mutation subtype's AFS individually varied drastically across the 96 subtypes. In local patterns of variation, a combination of regional subtype composition and local genomic factors shaped the regional frequency spectrum across genomic regions. Our results illustrate how treating variants in large sequencing samples as interchangeable may confound population genetic frameworks and encourages us to consider the unique evolutionary mechanisms of analyzed polymorphisms.

CEGA: a method for inferring natural selection by comparative population genomic analysis across species.

We developed maximum likelihood method for detecting positive selection or balancing selection using multilocus or genomic polymorphism and divergence data from two species. The method is especially useful for investigating natural selection in noncoding regions. Simulations demonstrate that the method outperforms existing methods in detecting both positive and balancing selection. We apply the method to population genomic data from human and chimpanzee. The list of genes identified under selection in the noncoding regions is prominently enriched in pathways related to the brain and nervous system. Therefore, our method will serve as a useful tool for comparative population genomic analysis.

GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data.

BACKGROUND: The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum (AFS), the distribution of allele frequencies in populations. The joint AFS is commonly used to reconstruct the demographic history of multiple populations, and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. RESULTS: Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA). CONCLUSIONS: We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history.

275 years of forestry meets genomics in Pinus sylvestris.

Pinus sylvestris has a long history of basic and applied research that is relevant for both forestry and evolutionary studies. Its patterns of adaptive variation and role in forest economic and ecological systems have been studied extensively for nearly 275 years, detailed demography for a 100 years and mating system more than 50 years. However, its reference genome sequence is not yet available and genomic studies have been lagging compared to, for example, Pinus taeda and Picea abies, two other economically important conifers. Despite the lack of reference genome, many modern genomic methods are applicable for a more detailed look at its biological characteristics. For example, RNA-seq has revealed a complex transcriptional landscape and targeted DNA sequencing displays an excess of rare variants and geographically homogenously distributed molecular genetic diversity. Current DNA and RNA resources can be used as a reference for gene expression studies, SNP discovery, and further targeted sequencing. In the future, specific consequences of the large genome size, such as functional effects of regulatory open chromatin regions and transposable elements, should be investigated more carefully. For forest breeding and long-term management purposes, genomic data can help in assessing the genetic basis of inbreeding depression and the application of genomic tools for genomic prediction and relatedness estimates. Given the challenges of breeding (long generation time, no easy vegetative propagation) and the economic importance, application of genomic tools has a potential to have a considerable impact. Here, we explore how genomic characteristics of P. sylvestris, such as rare alleles and the low extent of linkage disequilibrium, impact the applicability and power of the tools.

On the Extent of Linkage Disequilibrium in the Genome of Farm Animals.

Given the importance of linkage disequilibrium (LD) in gene mapping and evolutionary inferences, I characterize in this review the pattern of LD and discuss the influence of human intervention during domestication, breed establishment, and subsequent genetic improvement on shaping the genome of livestock species. To this end, I summarize data on the profile of LD based on array genotypes vs. sequencing data in cattle and chicken, two major livestock species, and compare to the human case. This comparison provides insights into the real dimension of the pairwise allelic correlation and haplo-block structuring. The dependency of LD on allelic frequency is pictured and a recently introduced metric for moderating it is outlined. In the context of the contact farm animals had with human, the impact of genetic forces including admixture, mutation, recombination rate, selection, and effective population size on LD is discussed. The review further highlights the interplay of LD with runs of homozygosity and concludes with the operational implications of the widely used association and selection mapping studies in relation to LD.

A Computational Approach for Modeling the Allele Frequency Spectrum of Populations with Arbitrarily Varying Size.

The allele frequency spectrum (AFS), or site frequency spectrum, is commonly used to summarize the genomic polymorphism pattern of a sample, which is informative for inferring population history and detecting natural selection. In 2013, Chen and Chen developed a method for analytically deriving the AFS for populations with temporally varying size through the coalescence time-scaling function. However, their approach is only applicable to population history scenarios in which the analytical form of the time-scaling function is tractable. In this paper, we propose a computational approach to extend the method to populations with arbitrary complex varying size by numerically approximating the time-scaling function. We demonstrate the performance of the approach by constructing the AFS for two population history scenarios: the logistic growth model and the Gompertz growth model, for which the AFS are unavailable with existing approaches. Software for implementing the algorithm can be downloaded at http://chenlab.big.ac.cn/software/.

Inferring the Joint Demographic History of Multiple Populations: Beyond the Diffusion Approximation.

Understanding variation in allele frequencies across populations is a central goal of population genetics. Classical models for the distribution of allele frequencies, using forward simulation, coalescent theory, or the diffusion approximation, have been applied extensively for demographic inference, medical study design, and evolutionary studies. Here we propose a tractable model of ordinary differential equations for the evolution of allele frequencies that is closely related to the diffusion approximation but avoids many of its limitations and approximations. We show that the approach is typically faster, more numerically stable, and more easily generalizable than the state-of-the-art software implementation of the diffusion approximation. We present a number of applications to human sequence data, including demographic inference with a five-population joint frequency spectrum and a discussion of the robustness of the out-of-Africa model inference to the choice of modern population.

Population genetic studies in the genomic sequencing era.

Recent advances in high-throughput sequencing technologies have revolutionized the field of population genetics. Data now routinely contain genomic level polymorphism information, and the low cost of DNA sequencing enables researchers to investigate tens of thousands of subjects at a time. This provides an unprecedented opportunity to address fundamental evolutionary questions, while posing challenges on traditional population genetic theories and methods. This review provides an overview of the recent methodological developments in the field of population genetics, specifically methods used to infer ancient population history and investigate natural selection using large-sample, large-scale genetic data. Several open questions are also discussed at the end of the review.