Estimating demographic parameters from large-scale population genomic data using Approximate Bayesian Computation

Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models

Statistical estimation of parameters in large models of evolutionary processes is often too computationally inefficient to pursue using exact model likelihoods, even with single-nucleotide polymorphism (SNP) data, which offers a way to reduce the size of genetic data while retaining relevant information. Approximate Bayesian Computation (ABC) to perform statistical inference about parameters of large models takes the advantage of simulations to bypass direct evaluation of model likelihoods. We develop a mechanistic model to simulate forward-in-time divergent selection with variable migration rates, modes of reproduction (sexual, asexual), length and number of migration-selection cycles. We investigate the computational feasibility of ABC to perform statistical inference and study the quality of estimates on the position of loci under selection and the strength of selection. To expand the parameter space of positions under selection, we enhance the model by implementing an outlier scan on summarized observed data. We evaluate the usefulness of summary statistics well-known to capture the strength of selection, and assess their informativeness under divergent selection. We also evaluate the effect of genetic drift with respect to an idealized deterministic model with single-locus selection. We discuss the role of the recombination rate as a confounding factor in estimating the strength of divergent selection, and emphasize its importance in break down of linkage disequilibrium (LD). We answer the question for which part of the parameter space of the model we recover strong signal for estimating the selection, and determine whether population differentiation-based summary statistics or LD-based summary statistics perform well in estimating selection.

Revealing the Genetic Structure and Differentiation in Endangered Pinus bungeana by Genome-Wide SNP Markers

Understanding the genetic variation and differentiation of natural populations is essential for their protection, specifically if the species status is endangered as with Pinus bungeana. Here, we used 346,840 high density and strong specificity SNP loci to carry out genetic analyses (i.e., genetic diversity, genetic structure, phylogeny, and geographical differentiation) on 52 P. bungeana individuals from 5 populations (4 natural and one artificial) representing the main regions of the species distribution in China. Genetic diversity assessment indicated a trend of genetic diversity gradual decrease from west to east across the species distribution areas. Population genetic structure, PCA and phylogenetic analyses consistently indicated that populations in the central and eastern regions were clustered together, while those from the western regions were separated. Mantel test values indicated the presence of geographic isolation among populations, an important factor contributing to the observed genetic differentiation. The maximum likelihood tree and potential migration events inferred from TreeMix analysis indicated the presence of historical genetic exchanges between the west of Qinling Mountains and the Lvliang Mountains populations. Based on the generated genetic information, in situ and ex situ conservation strategies for P.bungeana germplasm resources are proposed, these strategies could be valuable for the conservation, protection and genetic improvement of this endangered species.

The genomic history of the Aegean palatial civilizations

The Cycladic, the Minoan, and the Helladic (Mycenaean) cultures define the Bronze Age (BA) of Greece. Urbanism, complex social structures, craft and agricultural specialization, and the earliest forms of writing characterize this iconic period. We sequenced six Early to Middle BA whole genomes, along with 11 mitochondrial genomes, sampled from the three BA cultures of the Aegean Sea. The Early BA (EBA) genomes are homogeneous and derive most of their ancestry from Neolithic Aegeans, contrary to earlier hypotheses that the Neolithic-EBA cultural transition was due to massive population turnover. EBA Aegeans were shaped by relatively small-scale migration from East of the Aegean, as evidenced by the Caucasus-related ancestry also detected in Anatolians. In contrast, Middle BA (MBA) individuals of northern Greece differ from EBA populations in showing ∼50% Pontic-Caspian Steppe-related ancestry, dated at ca. 2,600-2,000 BCE. Such gene flow events during the MBA contributed toward shaping present-day Greek genomes.

An ABC Method for Whole-Genome Sequence Data: Inferring Paleolithic and Neolithic Human Expansions

Species generally undergo a complex demographic history consisting, in particular, of multiple changes in population size. Genome-wide sequencing data are potentially highly informative for reconstructing this demographic history. A crucial point is to extract the relevant information from these very large data sets. Here, we design an approach for inferring past demographic events from a moderate number of fully sequenced genomes. Our new approach uses Approximate Bayesian Computation, a simulation-based statistical framework that allows 1) identifying the best demographic scenario among several competing scenarios and 2) estimating the best-fitting parameters under the chosen scenario. Approximate Bayesian Computation relies on the computation of summary statistics. Using a cross-validation approach, we show that statistics such as the lengths of haplotypes shared between individuals, or the decay of linkage disequilibrium with distance, can be combined with classical statistics (e.g., heterozygosity and Tajima's D) to accurately infer complex demographic scenarios including bottlenecks and expansion periods. We also demonstrate the importance of simultaneously estimating the genotyping error rate. Applying our method on genome-wide human-sequence databases, we finally show that a model consisting in a bottleneck followed by a Paleolithic and a Neolithic expansion is the most relevant for Eurasian populations.

Genomic evidence for shared common ancestry of East African hunting-gathering populations and insights into local adaptation

Anatomically modern humans arose in Africa ∼300,000 years ago, but the demographic and adaptive histories of African populations are not well-characterized. Here, we have generated a genome-wide dataset from 840 Africans, residing in western, eastern, southern, and northern Africa, belonging to 50 ethnicities, and speaking languages belonging to four language families. In addition to agriculturalists and pastoralists, our study includes 16 populations that practice, or until recently have practiced, a hunting-gathering (HG) lifestyle. We observe that genetic structure in Africa is broadly correlated not only with geography, but to a lesser extent, with linguistic affiliation and subsistence strategy. Four East African HG (EHG) populations that are geographically distant from each other show evidence of common ancestry: the Hadza and Sandawe in Tanzania, who speak languages with clicks classified as Khoisan; the Dahalo in Kenya, whose language has remnant clicks; and the Sabue in Ethiopia, who speak an unclassified language. Additionally, we observed common ancestry between central African rainforest HGs and southern African San, the latter of whom speak languages with clicks classified as Khoisan. With the exception of the EHG, central African rainforest HGs, and San, other HG groups in Africa appear genetically similar to neighboring agriculturalist or pastoralist populations. We additionally demonstrate that infectious disease, immune response, and diet have played important roles in the adaptive landscape of African history. However, while the broad biological processes involved in recent human adaptation in Africa are often consistent across populations, the specific loci affected by selective pressures more often vary across populations.

The divergence history of European blue mussel species reconstructed from Approximate Bayesian Computation: the effects of sequencing techniques and sampling strategies

Genome-scale diversity data are increasingly available in a variety of biological systems, and can be used to reconstruct the past evolutionary history of species divergence. However, extracting the full demographic information from these data is not trivial, and requires inferential methods that account for the diversity of coalescent histories throughout the genome. Here, we evaluate the potential and limitations of one such approach. We reexamine a well-known system of mussel sister species, using the joint site frequency spectrum (jSFS) of synonymous mutations computed either from exome capture or RNA-seq, in an Approximate Bayesian Computation (ABC) framework. We first assess the best sampling strategy (number of: individuals, loci, and bins in the jSFS), and show that model selection is robust to variation in the number of individuals and loci. In contrast, different binning choices when summarizing the jSFS, strongly affect the results: including classes of low and high frequency shared polymorphisms can more effectively reveal recent migration events. We then take advantage of the flexibility of ABC to compare more realistic models of speciation, including variation in migration rates through time (i.e., periodic connectivity) and across genes (i.e., genome-wide heterogeneity in migration rates). We show that these models were consistently selected as the most probable, suggesting that mussels have experienced a complex history of gene flow during divergence and that the species boundary is semi-permeable. Our work provides a comprehensive evaluation of ABC demographic inference in mussels based on the coding jSFS, and supplies guidelines for employing different sequencing techniques and sampling strategies. We emphasize, perhaps surprisingly, that inferences are less limited by the volume of data, than by the way in which they are analyzed.

Fine-tuning of Approximate Bayesian Computation for human population genomics

Approximate Bayesian Computation (ABC) is a flexible statistical tool widely applied to addressing a variety of questions regarding the origin and evolution of humans. The significant growth of genomic scale data from diverse geographic populations has facilitated the use of ABC in modelling the complex processes that underlie human demography and local adaptation. However, a fundamental issue still remains in how to efficiently capture patterns of genetic variation with a set of summary statistics in order to achieve better approximation of Bayesian inference. Here, we review recent advances in ABC methodology and its applications for human population genomics, with a particular focus on optimal tuning of ABC approaches for different types of genetic data and different sets of evolutionary parameters.

Matriclans shape populations: Insights from the Angolan Namib Desert into the maternal genetic history of southern Africa

OBJECTIVES

Southern Angola is a poorly studied region, inhabited by populations that have been associated with different migratory movements into southern Africa. Apart from Kx'a-speaking San foragers and Bantu-speaking pastoralists, ethnographic and linguistic studies have suggested the existence of an enigmatic array of pre-Bantu communities, like the Kwepe (formerly Khoe-Kwadi speakers), Twa and Kwisi. Here, we evaluate previous peopling hypotheses by assessing the relationships between different southern Angolan populations, based on newly collected linguistic data and complete mtDNA genomes.

MATERIALS AND METHODS

We analyzed 295 complete mtDNA genomes and linguistic data from seven groups from the Namib Desert (Himba, Kuvale, Tjimba, Twa, Kwisi, Kwepe) and Kunene Province (!Xun), placing special emphasis on the evaluation of the genealogical consistency of the matriclanic system that characterizes most of these groups.

RESULTS

We found that the maternal genetic structure of all groups from the Namib Desert was strongly shaped by the consistency of their matriclanic system. The tracking of the maternal heritage enhanced population differentiation by genetic drift and is likely to have caused the divergent mtDNA profiles of the Kwepe, Twa, and Kwisi, who probably formed a single population within the spectrum of Bantu genetic variation. Model-based analyses further suggest that the dominant pastoral groups Kuvale and Himba may be grouped into a Bantu proto-population which also included the ancestors of present-day Tjimba and Herero, as well as the Khoe-Kwadi speaking Damara foragers from Namibia.

DISCUSSION

The view from southwestern Angola offers a new perspective on the populating history of southern Africa and the Bantu expansions by showing that social stratification and different subsistence patterns are not always indicative of remnant groups, but may reflect Bantu-internal variation and ethnogenesis.

Population genomics of C. melanopterus using target gene capture data: demographic inferences and conservation perspectives

Population genetics studies on non-model organisms typically involve sampling few markers from multiple individuals. Next-generation sequencing approaches open up the possibility of sampling many more markers from fewer individuals to address the same questions. Here, we applied a target gene capture method to deep sequence ~1000 independent autosomal regions of a non-model organism, the blacktip reef shark (Carcharhinus melanopterus). We devised a sampling scheme based on the predictions of theoretical studies of metapopulations to show that sampling few individuals, but many loci, can be extremely informative to reconstruct the evolutionary history of species. We collected data from a single deme (SID) from Northern Australia and from a scattered sampling representing various locations throughout the Indian Ocean (SCD). We explored the genealogical signature of population dynamics detected from both sampling schemes using an ABC algorithm. We then contrasted these results with those obtained by fitting the data to a non-equilibrium finite island model. Both approaches supported an Nm value ~40, consistent with philopatry in this species. Finally, we demonstrate through simulation that metapopulations exhibit greater resilience to recent changes in effective size compared to unstructured populations. We propose an empirical approach to detect recent bottlenecks based on our sampling scheme.

Genomic Methods Take the Plunge: Recent Advances in High-Throughput Sequencing of Marine Mammals

The dramatic increase in the application of genomic techniques to non-model organisms (NMOs) over the past decade has yielded numerous valuable contributions to evolutionary biology and ecology, many of which would not have been possible with traditional genetic markers. We review this recent progression with a particular focus on genomic studies of marine mammals, a group of taxa that represent key macroevolutionary transitions from terrestrial to marine environments and for which available genomic resources have recently undergone notable rapid growth. Genomic studies of NMOs utilize an expanding range of approaches, including whole genome sequencing, restriction site-associated DNA sequencing, array-based sequencing of single nucleotide polymorphisms and target sequence probes (e.g., exomes), and transcriptome sequencing. These approaches generate different types and quantities of data, and many can be applied with limited or no prior genomic resources, thus overcoming one traditional limitation of research on NMOs. Within marine mammals, such studies have thus far yielded significant contributions to the fields of phylogenomics and comparative genomics, as well as enabled investigations of fitness, demography, and population structure. Here we review the primary options for generating genomic data, introduce several emerging techniques, and discuss the suitability of each approach for different applications in the study of NMOs.

Target gene enrichment in the cyclophyllidean cestodes, the most diverse group of tapeworms

The Cyclophyllidea is the most diverse order of tapeworms, encompassing species that infect all classes of terrestrial tetrapods including humans and domesticated animals. Available phylogenetic reconstructions based either on morphology or molecular data lack the resolution to allow scientists to either propose a solid taxonomy or infer evolutionary associations. Molecular markers available for the Cyclophyllidea mostly include ribosomal DNA and mitochondrial loci. In this study, we identified 3641 single-copy nuclear coding loci by comparing the genomes of Hymenolepis microstoma, Echinococcus granulosus and Taenia solium. We designed RNA baits based on the sequence of H. microstoma, and applied target enrichment and Illumina sequencing to test the utility of those baits to recover loci useful for phylogenetic analyses. We captured DNA from five species of tapeworms representing two families of cyclophyllideans. We obtained an average of 3284 (90%) of the targets from the test samples and then used captured sequences (2 181 361 bp in total; fragment size ranging from 301 to 6969 bp) to reconstruct a phylogeny for the five test species plus the three species for which genomic data are available. The results were consistent with the current consensus regarding cyclophyllidean relationships. To assess the potential for our method to yield informative genetic variation at intraspecific scales, we extracted 14 074 single nucleotide polymorphisms (SNPs) from alignments of four Arostrilepis macrocirrosa and two A. cooki and successfully inferred their relationships. The results showed that our target gene tools yield data sets that provide robust inferences at a range of taxonomic scales in the Cyclophyllidea.

Inferring Population Size History from Large Samples of Genome-Wide Molecular Data - An Approximate Bayesian Computation Approach

Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey), PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles.

Molecular data sampled from extant individuals contains considerable information about their demographic history. In particular, one classical question in population genetics is to reconstruct past population size changes from such data. Relating these changes to various climatic, geological or anthropogenic events allows characterizing the main factors driving genetic diversity and can have major outcomes for conservation. Until recently, mostly very simple histories, including one or two population size changes, could be estimated from genetic data. This has changed with the sequencing of entire genomes in many species, and several methods allow now inferring complex histories consisting of several tens of population size changes. However, analyzing entire genomes, while accounting for recombination, remains a statistical and numerical challenge. These methods, therefore, can only be applied to small samples with a few diploid genomes. We overcome this limitation by using an approximate estimation approach, where observed genomes are summarized using a small number of statistics related to allele frequencies and linkage disequilibrium. In contrast to previous approaches, we show that our method allows us to reconstruct also the most recent part (the last 100 generations) of the population size history. As an illustration, we apply it to large samples of whole-genome sequences in four cattle breeds.

Population genomics reveals seahorses (Hippocampus erectus) of the western mid-Atlantic coast to be residents rather than vagrants

Understanding population structure and areas of demographic persistence and transients is critical for effective species management. However, direct observational evidence to address the geographic scale and delineation of ephemeral or persistent populations for many marine fishes is limited. The Lined seahorse (Hippocampus erectus) can be commonly found in three western Atlantic zoogeographic provinces, though inhabitants of the temperate northern Virginia Province are often considered tropical vagrants that only arrive during warm seasons from the southern provinces and perish as temperatures decline. Although genetics can locate regions of historical population persistence and isolation, previous evidence of Virginia Province persistence is only provisional due to limited genetic sampling (i.e., mitochondrial DNA and five nuclear loci). To test alternative hypotheses of historical persistence versus the ephemerality of a northern Virginia Province population we used a RADseq generated dataset consisting of 11,708 single nucleotide polymorphisms (SNP) sampled from individuals collected from the eastern Gulf of Mexico to Long Island, NY. Concordant results from genomic analyses all infer three genetically divergent subpopulations, and strongly support Virginia Province inhabitants as a genetically diverged and a historically persistent ancestral gene pool. These results suggest that individuals that emerge in coastal areas during the warm season can be considered "local" and supports offshore migration during the colder months. This research demonstrates how a large number of genes sampled across a geographical range can capture the diversity of coalescent histories (across loci) while inferring population history. Moreover, these results clearly demonstrate the utility of population genomic data to infer peripheral subpopulation persistence in difficult-to-observe species.

Genetic variation reveals large-scale population expansion and migration during the expansion of Bantu-speaking peoples

The majority of sub-Saharan Africans today speak a number of closely related languages collectively referred to as 'Bantu' languages. The current distribution of Bantu-speaking populations has been found to largely be a consequence of the movement of people rather than a diffusion of language alone. Linguistic and single marker genetic studies have generated various hypotheses regarding the timing and the routes of the Bantu expansion, but these hypotheses have not been thoroughly investigated. In this study, we re-analysed microsatellite markers typed for large number of African populations that-owing to their fast mutation rates-capture signatures of recent population history. We confirm the spread of west African people across most of sub-Saharan Africa and estimated the expansion of Bantu-speaking groups, using a Bayesian approach, to around 5600 years ago. We tested four different divergence models for Bantu-speaking populations with a distribution comprising three geographical regions in Africa. We found that the most likely model for the movement of the eastern branch of Bantu-speakers involves migration of Bantu-speaking groups to the east followed by migration to the south. This model, however, is only marginally more likely than other models, which might indicate direct movement from the west and/or significant gene flow with the western Branch of Bantu-speakers. Our study use multi-loci genetic data to explicitly investigate the timing and mode of the Bantu expansion and it demonstrates that west African groups rapidly expanded both in numbers and over a large geographical area, affirming the fact that the Bantu expansion was one of the most dramatic demographic events in human history.

ABC inference of multi-population divergence with admixture from unphased population genomic data

Rapidly developing sequencing technologies and declining costs have made it possible to collect genome-scale data from population-level samples in nonmodel systems. Inferential tools for historical demography given these data sets are, at present, underdeveloped. In particular, approximate Bayesian computation (ABC) has yet to be widely embraced by researchers generating these data. Here, we demonstrate the promise of ABC for analysis of the large data sets that are now attainable from nonmodel taxa through current genomic sequencing technologies. We develop and test an ABC framework for model selection and parameter estimation, given histories of three-population divergence with admixture. We then explore different sampling regimes to illustrate how sampling more loci, longer loci or more individuals affects the quality of model selection and parameter estimation in this ABC framework. Our results show that inferences improved substantially with increases in the number and/or length of sequenced loci, while less benefit was gained by sampling large numbers of individuals. Optimal sampling strategies given our inferential models included at least 2000 loci, each approximately 2 kb in length, sampled from five diploid individuals per population, although specific strategies are model and question dependent. We tested our ABC approach through simulation-based cross-validations and illustrate its application using previously analysed data from the oak gall wasp, Biorhiza pallida.

Spatial and temporal simulation of human evolution. Methods, frameworks and applications

Analyses of human evolution are fundamental to understand the current gradients of human diversity. In this concern, genetic samples collected from current populations together with archaeological data are the most important resources to study human evolution. However, they are often insufficient to properly evaluate a variety of evolutionary scenarios, leading to continuous debates and discussions. A commonly applied strategy consists of the use of computer simulations based on, as realistic as possible, evolutionary models, to evaluate alternative evolutionary scenarios through statistical correlations with the real data. Computer simulations can also be applied to estimate evolutionary parameters or to study the role of each parameter on the evolutionary process. Here we review the mainly used methods and evolutionary frameworks to perform realistic spatially explicit computer simulations of human evolution. Although we focus on human evolution, most of the methods and software we describe can also be used to study other species. We also describe the importance of considering spatially explicit models to better mimic human evolutionary scenarios based on a variety of phenomena such as range expansions, range shifts, range contractions, sex-biased dispersal, long-distance dispersal or admixtures of populations. We finally discuss future implementations to improve current spatially explicit simulations and their derived applications in human evolution.

An approximate Bayesian computation approach to parameter estimation in a stochastic stage-structured population model

Complex population processes may require equally complex models, which can lead to analytically intractable estimation problems. Approximate Bayesian computation (ABC) is a computational tool for parameter estimation in situations where likelihoods cannot be computed. Instead of using likelihoods, ABC methods quantify the similarities between an observed data set and repeated simulations from a model. A practical obstacle to implementing an ABC algorithm is selecting summary statistics and distance metrics that accurately capture the main features of the data. We demonstrate the application of a sequential Monte Carlo ABC sampler (ABC SMC) to parameter estimation of a general stochastic stage-structured population model with ongoing reproduction and heterogeneity in development and mortality. Individual variation in demographic traits has considerable consequences for population dynamics in many systems, but including it in a population model by explicitly allowing stage durations to follow a realistic distribution creates a complex model. We applied the ABC SMC to fit the model to a simulated representative data set with known underlying parameters to evaluate the performance of the algorithm. We also introduced a systematic method for selecting summary statistics and distance metrics, using simulated data and receiver operating characteristic (ROC) curves from classification theory. Evaluations suggest that the approach is promising for model inference in our example of realistic stage-structured population models.

Amount of information needed for model choice in Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its flexibility. The statistical inference framework has benefited from significant progress in recent years. In population genetics, however, its outcome depends heavily on the amount of information in the dataset, whether that be the level of genetic variation or the number of samples and loci. Here we look at the power to reject a simple constant population size coalescent model in favor of a bottleneck model in datasets of varying quality. Not only is this power dependent on the number of samples and loci, but it also depends strongly on the level of nucleotide diversity in the observed dataset. Whilst overall model choice in an ABC setting is fairly powerful and quite conservative with regard to false positives, detecting weaker bottlenecks is problematic in smaller or less genetically diverse datasets and limits the inferences possible in non-model organism where the amount of information regarding the two models is often limited. Our results show it is important to consider these limitations when performing an ABC analysis and that studies should perform simulations based on the size and nature of the dataset in order to fully assess the power of the study.

Rapid increase in southern elephant seal genetic diversity after a founder event

Genetic diversity provides the raw material for populations to respond to changing environmental conditions. The evolution of diversity within populations is based on the accumulation of mutations and their retention or loss through selection and genetic drift, while migration can also introduce new variation. However, the extent to which population growth and sustained large population size can lead to rapid and significant increases in diversity has not been widely investigated. Here, we assess this empirically by applying approximate Bayesian computation to a novel ancient DNA dataset that spans the life of a southern elephant seal (Mirounga leonina) population, from initial founding approximately 7000 years ago to eventual extinction within the past millennium. We find that rapid population growth and sustained large population size can explain substantial increases in population genetic diversity over a period of several hundred generations, subsequently lost when the population went to extinction. Results suggest that the impact of diversity introduced through migration was relatively minor. We thus demonstrate, by examining genetic diversity across the life of a population, that environmental change could generate the raw material for adaptive evolution over a very short evolutionary time scale through rapid establishment of a large, stable population.

Origin and demographic history of the endemic Taiwan spruce (Picea morrisonicola)

Taiwan spruce (Picea morrisonicola) is a vulnerable conifer species endemic to the island of Taiwan. A warming climate and competition from subtropical tree species has limited the range of Taiwan spruce to the higher altitudes of the island. Using seeds sampled from an area in the central mountain range of Taiwan, 15 nuclear loci were sequenced in order to measure genetic variation and to assess the long-term genetic stability of the species. Genetic diversity is low and comparable to other spruce species with limited ranges such as Picea breweriana, Picea chihuahuana, and Picea schrenkiana. Importantly, analysis using approximate Bayesian computation (ABC) provides evidence for a drastic decline in the effective population size approximately 0.3–0.5 million years ago (mya). We used simulations to show that this is unlikely to be a false-positive result due to the limited sample used here. To investigate the phylogenetic origin of Taiwan spruce, additional sequencing was performed in the Chinese spruce Picea wilsonii and combined with previously published data for three other mainland China species, Picea purpurea, Picea likiangensis, and P. schrenkiana. Analysis of population structure revealed that P. morrisonicola clusters most closely with P. wilsonii, and coalescent analyses using the program MIMAR dated the split to 4–8 mya, coincidental to the formation of Taiwan. Considering the population decrease that occurred after the split, however, led to a much more recent origin.

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.

Robust demographic inference from genomic and SNP data

We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with ∂a∂i, the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets.

On the structural plasticity of the human genome: chromosomal inversions revisited

With the aid of novel and powerful molecular biology techniques, recent years have witnessed a dramatic increase in the number of studies reporting the involvement of complex structural variants in several genomic disorders. In fact, with the discovery of Copy Number Variants (CNVs) and other forms of unbalanced structural variation, much attention has been directed to the detection and characterization of such rearrangements, as well as the identification of the mechanisms involved in their formation. However, it has long been appreciated that chromosomes can undergo other forms of structural changes - balanced rearrangements - that do not involve quantitative variation of genetic material. Indeed, a particular subtype of balanced rearrangement – inversions – was recently found to be far more common than had been predicted from traditional cytogenetics. Chromosomal inversions alter the orientation of a specific genomic sequence and, unless involving breaks in coding or regulatory regions (and, disregarding complex trans effects, in their close vicinity), appear to be phenotypically silent. Such a surprising finding, which is difficult to reconcile with the classical interpretation of inversions as a mechanism causing subfertility (and ultimately reproductive isolation), motivated a new series of theoretical and empirical studies dedicated to understand their role in human genome evolution and to explore their possible association to complex genetic disorders. With this review, we attempt to describe the latest methodological improvements to inversions detection at a genome wide level, while exploring some of the possible implications of inversion rearrangements on the evolution of the human genome.

Understanding the origin of species with genome-scale data: modelling gene flow

As it becomes easier to sequence multiple genomes from closely related species, evolutionary biologists working on speciation are struggling to get the most out of very large population genomic data sets. Such data hold the potential to resolve long-standing questions in evolutionary biology about the role of gene exchange in species formation. In principle, the new population genomic data can be used to disentangle the conflicting roles of natural selection and gene flow during the divergence process. However, there are great challenges in taking full advantage of such data, especially with regard to including recombination in genetic models of the divergence process. Current data, models, methods and the potential pitfalls in using them will be considered here.