Genealogical trees, coalescent theory and the analysis of genetic polymorphisms

The coalescent in finite populations with arbitrary, fixed structure

The coalescent is a stochastic process representing ancestral lineages in a population undergoing neutral genetic drift. Originally defined for a well-mixed population, the coalescent has been adapted in various ways to accommodate spatial, age, and class structure, along with other features of real-world populations. To further extend the range of population structures to which coalescent theory applies, we formulate a coalescent process for a broad class of neutral drift models with arbitrary - but fixed - spatial, age, sex, and class structure, haploid or diploid genetics, and any fixed mating pattern. Here, the coalescent is represented as a random sequence of mappings [Formula: see text] from a finite set G to itself. The set G represents the "sites" (in individuals, in particular locations and/or classes) at which these alleles can live. The state of the coalescent, Ct:G→G, maps each site g∈G to the site containing g's ancestor, t time-steps into the past. Using this representation, we define and analyze coalescence time, coalescence branch length, mutations prior to coalescence, and stationary probabilities of identity-by-descent and identity-by-state. For low mutation, we provide a recipe for computing identity-by-descent and identity-by-state probabilities via the coalescent. Applying our results to a diploid population with arbitrary sex ratio r, we find that measures of genetic dissimilarity, among any set of sites, are scaled by 4r(1-r) relative to the even sex ratio case.

Revealing gene function with statistical inference at single-cell resolution

Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering a dense versus sparse sampling. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetic changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

Harnessing deep learning for population genetic inference

In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.

The era of the ARG: An introduction to ancestral recombination graphs and their significance in empirical evolutionary genomics

In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. Instead, the genomes are related by a complex, interwoven collection of genealogies formalized in a structure called an ancestral recombination graph (ARG). An ARG extensively encodes the ancestry of the genome(s) and thus is replete with valuable information for addressing diverse questions in evolutionary biology. Despite its potential utility, technological and methodological limitations, along with a lack of approachable literature, have severely restricted awareness and application of ARGs in evolution research. Excitingly, recent progress in ARG reconstruction and simulation have made ARG-based approaches feasible for many questions and systems. In this review, we provide an accessible introduction and exploration of ARGs, survey recent methodological breakthroughs, and describe the potential for ARGs to further existing goals and open avenues of inquiry that were previously inaccessible in evolutionary genomics. Through this discussion, we aim to more widely disseminate the promise of ARGs in evolutionary genomics and encourage the broader development and adoption of ARG-based inference.

Analyses of Nuclear Reads Obtained Using Genome Skimming

In this protocol paper, we review a set of methods developed in recent years for analyzing nuclear reads obtained from genome skimming. As the cost of sequencing drops, genome skimming (low-coverage shotgun sequencing of a sample) becomes increasingly a cost-effective method of measuring biodiversity at high resolution. While most practitioners only use assembled over-represented organelle reads from a genome skim, the vast majority of the reads are nuclear. Using assembly-free and alignment-free methods described in this protocol, we can compare samples to each other and reference genomes to compute distances, characterize underlying genomes, and infer evolutionary relationships.

Tree-based QTL mapping with expected local genetic relatedness matrices

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide association studies (GWASs) are a powerful way to find genetic loci associated with phenotypes. GWASs are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix (local eGRM) given the ARG. Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to analyze two chromosomes containing known body size loci in a sample of Native Hawaiians. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

Tree-based QTL mapping with expected local genetic relatedness matrices

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide Association Studies (GWAS) are a powerful way to find genetic loci associated with phenotypes. GWAS are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix given the ARG (local eGRM). Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to identify a large-effect BMI locus, the CREBRF gene, in a sample of Native Hawaiians in which it was not previously detectable by GWAS because of a lack of population-specific imputation resources. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

A reference cell tree will serve science better than a reference cell atlas

Single-cell biology is facing a crisis of sorts. Vast numbers of single-cell molecular profiles are being generated, clustered and annotated. However, this is overwhelmingly ad hoc, and we continue to lack a principled, unified, and well-moored system for defining, naming, and organizing cell types. In this perspective, we argue against an atlas or periodic table-like discretization as the right metaphor for a reference taxonomy of cell types. In its place, we advocate for a data-driven, tree-based nomenclature that is rooted in a "consensus ontogeny" spanning the life cycle of a given species. We explore how such a reference cell tree, inclusive of both lineage histories and molecular states, could be constructed, represented, and segmented in practice. Analogous to the taxonomic classification of species, a consensus ontogeny would provide a universal, stable, and extendable framework for precise scientific communication, both contemporaneously and across the ages.

Modelling speciation: Problems and implications

Darwin's and Wallace's 1859 explanation that novel speciation resulted from natural variants that had been subjected to selection was refined over the next 150 years as genetic inheritance and the importance of mutation-induced change were discovered, the quantitative theory of evolutionary population genetics was produced, the speed of genetic change in small populations became apparent and the ramifications of the DNA revolution became clear. This paper first discusses the modern view of speciation in its historical context. It then uses systems-biology approaches to consider the many complex processes that underpin the production of a new species; these extend in scale from genes to populations with the processes of variation, selection and speciation being affected by factors that range from mutation to climate change. Here, events at a particular scale level (e.g. protein network activity) are activated by the output of the level immediately below (i.e. gene expression) and generate a new output that activates the layer above (e.g. embryological development), with this change often being modulated by feedback from higher and lower levels. The analysis shows that activity at each level in the evolution of a new species is marked by stochastic activity, with mutation of course being the key step for variation. The paper examines events at each of these scale levels and particularly considers how the pathway by which mutation leads to phenotypic variants and the wide range of factors that drive selection can be investigated computationally. It concludes that, such is the complexity of speciation, most steps in the process are currently difficult to model and that predictions about future speciation will, apart from a few special cases, be hard to make. The corollary is that opportunities for novel variants to form are maximised.

Deep Genotypic Species Delimitation of Aspergillus Section Flavi Isolated from Brazilian Foodstuffs and the Description of Aspergillus annui sp. nov. and Aspergillus saccharicola sp. nov

Aspergillus section Flavi is a fungal group that is important in food because it contains spoilage and potentially aflatoxigenic species. Aflatoxins are metabolites that are harmful to human and animal health and have been recognized as the primary natural contaminant in food. Therefore, recognizing the biodiversity of this group in food is necessary to reduce risks to public health. Our study aimed to investigate the diversity of Aspergillus section Flavi isolated from Brazilian foodstuffs such as cassava, sugarcane, black pepper, paprika, Brazil nuts, yerba-mate, peanuts, rice, and corn. A polyphasic approach integrating phenotypic data and multilocus genotypic analyses (CaM, BenA, and RPB2) was performed for 396 strains. Two new species in the Aspergillus subgenus Circumdati section Flavi are proposed using maximum-likelihood analysis, Bayesian inference, and coalescence-based methods: Aspergillus saccharicola sp. nov. and Aspergillus annui sp. nov. A. saccharicola sp. nov. belongs to the series Flavi, is a potentially aflatoxigenic species (B1, B2, G1, and G2), closely related to Aspergillus arachidicola, and was found mostly in sugarcane. A. annui sp. nov. was isolated from samples of sweet paprika. To accommodate A. annui sp. nov., a new series Annuorum was proposed.

VGsim: Scalable viral genealogy simulator for global pandemic

Accurate simulation of complex biological processes is an essential component of developing and validating new technologies and inference approaches. As an effort to help contain the COVID-19 pandemic, large numbers of SARS-CoV-2 genomes have been sequenced from most regions in the world. More than 5.5 million viral sequences are publicly available as of November 2021. Many studies estimate viral genealogies from these sequences, as these can provide valuable information about the spread of the pandemic across time and space. Additionally such data are a rich source of information about molecular evolutionary processes including natural selection, for example allowing the identification of new variants with transmissibility and immunity evasion advantages. To our knowledge, there is no framework that is both efficient and flexible enough to simulate the pandemic to approximate world-scale scenarios and generate viral genealogies of millions of samples. Here, we introduce a new fast simulator VGsim which addresses the problem of simulation genealogies under epidemiological models. The simulation process is split into two phases. During the forward run the algorithm generates a chain of population-level events reflecting the dynamics of the pandemic using an hierarchical version of the Gillespie algorithm. During the backward run a coalescent-like approach generates a tree genealogy of samples conditioning on the population-level events chain generated during the forward run. Our software can model complex population structure, epistasis and immunity escape.

We develop a fast and flexible simulation software package VGsim for modeling epidemiological processes and generating genealogies of large pathogen samples. The software takes into account host population structure, pathogen evolution, host immunity and some other epidemiological aspects. The computational efficiency of the package allows to simulate genealogies of tens of millions of samples, which is important, e.g., for SARS-CoV-2 genome studies.

Cell types as species: Exploring a metaphor

The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.

Recombination-aware phylogeographic inference using the structured coalescent with ancestral recombination

Movement of individuals between populations or demes is often restricted, especially between geographically isolated populations. The structured coalescent provides an elegant theoretical framework for describing how movement between populations shapes the genealogical history of sampled individuals and thereby structures genetic variation within and between populations. However, in the presence of recombination an individual may inherit different regions of their genome from different parents, resulting in a mosaic of genealogical histories across the genome, which can be represented by an Ancestral Recombination Graph (ARG). In this case, different genomic regions may have different ancestral histories and so different histories of movement between populations. Recombination therefore poses an additional challenge to phylogeographic methods that aim to reconstruct the movement of individuals from genealogies, although also a potential benefit in that different loci may contain additional information about movement. Here, we introduce the Structured Coalescent with Ancestral Recombination (SCAR) model, which builds on recent approximations to the structured coalescent by incorporating recombination into the ancestry of sampled individuals. The SCAR model allows us to infer how the migration history of sampled individuals varies across the genome from ARGs, and improves estimation of key population genetic parameters such as population sizes, recombination rates and migration rates. Using the SCAR model, we explore the potential and limitations of phylogeographic inference using full ARGs. We then apply the SCAR to lineages of the recombining fungus Aspergillus flavus sampled across the United States to explore patterns of recombination and migration across the genome.

Phylogeographic methods are widely used to reconstruct the historical movement of individuals between different populations. When applied to infectious pathogens, these methods are often used to reconstruct the origin or source of novel pathogen lineages. Most existing phylogeographic methods reconstruct movement based on a single phylogenetic tree, which is assumed to reflect the genetic ancestry of all sampled individuals. However in populations undergoing recombination, genetic material can be exchanged between lineages such that individuals may inherit different regions of their genome from different ancestors. In this case, phylogenetic relationships among individuals can only be captured by a reticulated network rather than any single tree. Ancestral Recombination Graphs (ARGs) provide one way of capturing these reticulate relationships and we develop new models that allow for demographic inference of historical population sizes, recombination rates and migration rates between subpopulations from ARGs. By accounting for recombination, our models not only allow for accurate demographic inference, but can take full advantage of the additional information contained in ARGs about how ancestry varies across genomes to more precisely reconstruct the movement of genetic material between populations.

Alternative modes of introgression-mediated selection shaped crop adaptation to novel climates

Recent plant genomic studies provide fine-grained details on the evolutionary consequences of adaptive introgression during crop domestication. Modern genomic approaches and analytical methods now make it possible to better separate the introgression signal from the demographic signal thus providing a more comprehensive and complex picture of the role of introgression in local adaptation. Adaptive introgression has been fundamental for crop expansion and has involved complex patterns of gene flow. In addition to providing new and more favorable alleles of large effect, introgression during the early stages of domestication also increased allelic diversity at adaptive loci. Previous studies have largely underestimated the effect of such increased diversity following introgression. Recent genomic studies in wheat, potato, maize, grapevine, and ryegrass show that introgression of multiple genes, of as yet unknown effect, increased the effectiveness of purifying selection, and promoted disruptive or fluctuating selection in early cultivars and landraces. Historical selection processes associated with introgression from crop wild relatives provide an instructive analog for adaptation to current climate change and offer new avenues for crop breeding research that are expected to be instrumental for strengthening food security in the coming years.

The bounded coalescent model: Conditioning a genealogy on a minimum root date

The coalescent model represents how individuals sampled from a population may have originated from a last common ancestor. The bounded coalescent model is obtained by conditioning the coalescent model such that the last common ancestor must have existed after a certain date. This conditioned model arises in a variety of applications, such as speciation, horizontal gene transfer or transmission analysis, and yet the bounded coalescent model has not been previously analysed in detail. Here we describe a new algorithm to simulate from this model directly, without resorting to rejection sampling. We show that this direct simulation algorithm is more computationally efficient than the rejection sampling approach. We also show how to calculate the probability of the last common ancestor occurring after a given date, which is required to compute the probability density of realisations under the bounded coalescent model. Our results are applicable in both the isochronous (when all samples have the same date) and heterochronous (where samples can have different dates) settings. We explore the effect of setting a bound on the date of the last common ancestor, and show that it affects a number of properties of the resulting phylogenies. All our methods are implemented in a new R package called BoundedCoalescent which is freely available online.

Epidemiological Inference From Pathogen Genomes: A Review of Phylodynamic Models and Applications

Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.

Eukaryogenesis: The Rise of an Emergent Superorganism

Although it is widely taught that all modern life descended via modification from a last universal common ancestor (LUCA), this dominant paradigm is yet to provide a generally accepted explanation for the chasm in design between prokaryotic and eukaryotic cells. Counter to this dominant paradigm, the viral eukaryogenesis (VE) hypothesis proposes that the eukaryotes originated as an emergent superorganism and thus did not evolve from LUCA via descent with incremental modification. According to the VE hypothesis, the eukaryotic nucleus descends from a viral factory, the mitochondrion descends from an enslaved alpha-proteobacteria and the cytoplasm and plasma membrane descend from an archaeal host. A virus initiated the eukaryogenesis process by colonising an archaeal host to create a virocell that had its metabolism reprogrammed to support the viral factory. Subsequently, viral processes facilitated the entry of a bacterium into the archaeal cytoplasm which was also eventually reprogrammed to support the viral factory. As the viral factory increased control of the consortium, the archaeal genome was lost, the bacterial genome was greatly reduced and the viral factory eventually evolved into the nucleus. It is proposed that the interaction between these three simple components generated a superorganism whose emergent properties allowed the evolution of eukaryotic complexity. If the radical tenets of the VE hypothesis are ultimately accepted, current biological paradigms regarding viruses, cell theory, LUCA and the universal Tree of Life (ToL) should be fundamentally altered or completely abandoned.

Statistical Challenges in Tracking the Evolution of SARS-CoV-2

Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.

Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies

With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.

Bayesian inference of clonal expansions in a dated phylogeny

Microbial population genetics models often assume that all lineages are constrained by the same population size dynamics over time. However, many neutral and selective events can invalidate this assumption and can contribute to the clonal expansion of a specific lineage relative to the rest of the population. Such differential phylodynamic properties between lineages result in asymmetries and imbalances in phylogenetic trees that are sometimes described informally but which are difficult to analyze formally. To this end, we developed a model of how clonal expansions occur and affect the branching patterns of a phylogeny. We show how the parameters of this model can be inferred from a given dated phylogeny using Bayesian statistics, which allows us to assess the probability that one or more clonal expansion events occurred. For each putative clonal expansion event, we estimate its date of emergence and subsequent phylodynamic trajectory, including its long-term evolutionary potential which is important to determine how much effort should be placed on specific control measures. We demonstrate the applicability of our methodology on simulated and real data sets. Inference under our clonal expansion model can reveal important features in the evolution and epidemiology of infectious disease pathogens. [Clonal expansion; genomic epidemiology; microbial population genomics; phylodynamics.]

The complex history of genome duplication and hybridization in North American gray treefrogs

Polyploid speciation has played an important role in evolutionary history across the tree of life, yet there remain large gaps in our understanding of how polyploid species form and persist. Although systematic studies have been conducted in numerous polyploid complexes, recent advances in sequencing technology have demonstrated that conclusions from data-limited studies may be spurious and misleading. The North American gray treefrog complex, consisting of the diploid Hyla chrysoscelis and the tetraploid H. versicolor, has long been used as a model system in a variety of biological fields, yet all taxonomic studies to date were conducted with only a few loci from nuclear and mitochondrial genomes. Here, we utilized anchored hybrid enrichment and high-throughput sequencing to capture hundreds of loci along with whole mitochondrial genomes to investigate the evolutionary history of this complex. We used several phylogenetic and population genetic methods, including coalescent simulations and testing of polyploid speciation models with approximate Bayesian computation, to determine that H. versicolor was most likely formed via autopolyploidization from a now extinct lineage of H. chrysoscelis. We also uncovered evidence of significant hybridization between diploids and tetraploids where they co-occur, and show that historical hybridization between these groups led to the re-formation of distinct polyploid lineages following the initial whole-genome duplication event. Our study indicates that a wide variety of methods and explicit model testing of polyploid histories can greatly facilitate efforts to uncover the evolutionary history of polyploid complexes.

Phylogeography of a widely distributed plant species reveals cryptic genetic lineages with parallel phenotypic responses to warming and drought conditions

To predict how widely distributed species will perform under future climate change, it is crucial to understand and reveal their underlying phylogenetics. However, detailed information about plant adaptation and its genetic basis and history remains scarce and especially widely distributed species receive little attention despite their putatively high adaptability. To examine the adaptation potential of a widely distributed species, we sampled the model plant Silene vulgaris across Europe. In a greenhouse experiment, we exposed the offspring of these populations to a climate change scenario for central Europe and revealed the population structure through whole-genome sequencing. Plants were grown under two temperatures (18°C and 21°C) and three precipitation regimes (65, 75, and 90 mm) to measure their response in biomass and fecundity-related traits. To reveal the population genetic structure, ddRAD sequencing was employed for a whole-genome approach. We found three major genetic clusters in S. vulgaris from Europe: one cluster comprising Southern European populations, one cluster of Western European populations, and another cluster containing central European populations. Population genetic diversity decreased with increasing latitude, and a Mantel test revealed significant correlations between F ST and geographic distances as well as between genetic and environmental distances. Our trait analysis showed that the genetic clusters significantly differed in biomass-related traits and in the days to flowering. However, half of the traits showed parallel response patterns to the experimental climate change scenario. Due to the differentiated but parallel response patterns, we assume that phenotypic plasticity plays an important role for the adaptation of the widely distributed species S. vulgaris and its intraspecific genetic lineages.

Integrating Sequence Capture and Restriction Site-Associated DNA Sequencing to Resolve Recent Radiations of Pelagic Seabirds

The diversification of modern birds has been shaped by a number of radiations. Rapid diversification events make reconstructing the evolutionary relationships among taxa challenging due to the convoluted effects of incomplete lineage sorting (ILS) and introgression. Phylogenomic data sets have the potential to detect patterns of phylogenetic incongruence, and to address their causes. However, the footprints of ILS and introgression on sequence data can vary between different phylogenomic markers at different phylogenetic scales depending on factors such as their evolutionary rates or their selection pressures. We show that combining phylogenomic markers that evolve at different rates, such as paired-end double-digest restriction site-associated DNA (PE-ddRAD) and ultraconserved elements (UCEs), allows a comprehensive exploration of the causes of phylogenetic discordance associated with short internodes at different timescales. We used thousands of UCE and PE-ddRAD markers to produce the first well-resolved phylogeny of shearwaters, a group of medium-sized pelagic seabirds that are among the most phylogenetically controversial and endangered bird groups. We found that phylogenomic conflict was mainly derived from high levels of ILS due to rapid speciation events. We also documented a case of introgression, despite the high philopatry of shearwaters to their breeding sites, which typically limits gene flow. We integrated state-of-the-art concatenated and coalescent-based approaches to expand on previous comparisons of UCE and RAD-Seq data sets for phylogenetics, divergence time estimation, and inference of introgression, and we propose a strategy to optimize RAD-Seq data for phylogenetic analyses. Our results highlight the usefulness of combining phylogenomic markers evolving at different rates to understand the causes of phylogenetic discordance at different timescales. [Aves; incomplete lineage sorting; introgression; PE-ddRAD-Seq; phylogenomics; radiations; shearwaters; UCEs.].

The evolution of comparative phylogeography: putting the geography (and more) into comparative population genomics

Comparative population genomics is an ascendant field using genomic comparisons between species to draw inferences about forces regulating genetic variation. Comparative phylogeography, by contrast, focuses on the shared lineage histories of species codistributed geographically and is decidedly organismal in perspective. Comparative phylogeography is approximately 35 years old, and, by some metrics, is showing signs of reduced growth. Here, we contrast the goals and methods of comparative population genomics and comparative phylogeography and argue that comparative phylogeography offers an important perspective on evolutionary history that succeeds in integrating genomics with landscape evolution in ways that complement the suprageographic perspective of comparative population genomics. Focusing primarily on terrestrial vertebrates, we review the history of comparative phylogeography, its milestones and ongoing conceptual innovations, its increasingly global focus, and its status as a bridge between landscape genomics and the process of speciation. We also argue that, as a science with a strong “sense of place,” comparative phylogeography offers abundant “place-based” educational opportunities with its focus on geography and natural history, as well as opportunities for collaboration with local communities and indigenous peoples. Although comparative phylogeography does not yet require whole-genome sequencing for many of its goals, we conclude that it nonetheless plays an important role in grounding our interpretation of genetic variation in the fundamentals of geography and Earth history.

Phylodynamic analysis in the understanding of the current COVID-19 pandemic and its utility in vaccine and antiviral design and assessment

Over the last decades, the use of phylogenetic methods in the study of emerging infectious diseases has gained considerable traction in public health. Particularly, the integration of phylogenetic analyses with the understanding of the pathogen dynamics at the population level has provided powerful tools for epidemiological surveillance systems. In the same way, the development of statistical methods and theory, as well as improvement of computational efficiency for evolutionary analysis, has expanded the use of these tools for vaccine and antiviral development. Today with the Coronavirus Disease 2019 (COVID-19), this seems to be critical. In this article, we discuss how the application of phylodynamic analysis can improve the understanding of current pandemic dynamics as well as the design, selection, and evaluation of vaccine candidates and antivirals.

Addressing alpine plant phylogeography using integrative distributional, demographic and coalescent modeling

Phylogeographic studies of alpine plants have evolved considerably in the last two decades from ad hoc interpretations of genetic data to statistical model-based approaches. In this review we outline the developments in alpine plant phylogeography focusing on the recent approach of integrative distributional, demographic and coalescent (iDDC) modeling. By integrating distributional data with spatially explicit demographic modeling and subsequent coalescent simulations, the history of alpine species can be inferred and long-standing hypotheses, such as species-specific responses to climate change or survival on nunataks during the last glacial maximum, can be efficiently tested as exemplified by available case studies. We also discuss future prospects and improvements of iDDC.

Learning the protein language: Evolution, structure, and function

Language models have recently emerged as a powerful machine-learning approach for distilling information from massive protein sequence databases. From readily available sequence data alone, these models discover evolutionary, structural, and functional organization across protein space. Using language models, we can encode amino-acid sequences into distributed vector representations that capture their structural and functional properties, as well as evaluate the evolutionary fitness of sequence variants. We discuss recent advances in protein language modeling and their applications to downstream protein property prediction problems. We then consider how these models can be enriched with prior biological knowledge and introduce an approach for encoding protein structural knowledge into the learned representations. The knowledge distilled by these models allows us to improve downstream function prediction through transfer learning. Deep protein language models are revolutionizing protein biology. They suggest new ways to approach protein and therapeutic design. However, further developments are needed to encode strong biological priors into protein language models and to increase their accessibility to the broader community.

SEQUENTIAL IMPORTANCE SAMPLING FOR MULTIRESOLUTION KINGMAN-TAJIMA COALESCENT COUNTING

Statistical inference of evolutionary parameters from molecular sequence data relies on coalescent models to account for the shared genealogical ancestry of the samples. However, inferential algorithms do not scale to available data sets. A strategy to improve computational efficiency is to rely on simpler coalescent and mutation models, resulting in smaller hidden state spaces. An estimate of the cardinality of the state-space of genealogical trees at different resolutions is essential to decide the best modeling strategy for a given dataset. To our knowledge, there is neither an exact nor approximate method to determine these cardinalities. We propose a sequential importance sampling algorithm to estimate the cardinality of the sample space of genealogical trees under different coalescent resolutions. Our sampling scheme proceeds sequentially across the set of combinatorial constraints imposed by the data, which in this work are completely linked sequences of DNA at a non recombining segment. We analyze the cardinality of different genealogical tree spaces on simulations to study the settings that favor coarser resolutions. We apply our method to estimate the cardinality of genealogical tree spaces from mtDNA data from the 1000 genomes and a sample from a Melanesian population at the β-globin locus.

Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding

Background

Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination.

Results

We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders’ genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders’ genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling.

Conclusions

Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-021-02354-7.

The germline mutational process in rhesus macaque and its implications for phylogenetic dating

BACKGROUND

Understanding the rate and pattern of germline mutations is of fundamental importance for understanding evolutionary processes.

RESULTS

Here we analyzed 19 parent-offspring trios of rhesus macaques (Macaca mulatta) at high sequencing coverage of ∼76× per individual and estimated a mean rate of 0.77 × 10-8de novo mutations per site per generation (95% CI: 0.69 × 10-8 to 0.85 × 10-8). By phasing 50% of the mutations to parental origins, we found that the mutation rate is positively correlated with the paternal age. The paternal lineage contributed a mean of 81% of the de novo mutations, with a trend of an increasing male contribution for older fathers. Approximately 3.5% of de novo mutations were shared between siblings, with no parental bias, suggesting that they arose from early development (postzygotic) stages. Finally, the divergence times between closely related primates calculated on the basis of the yearly mutation rate of rhesus macaque generally reconcile with divergence estimated with molecular clock methods, except for the Cercopithecoidea/Hominoidea molecular divergence dated at 58 Mya using our new estimate of the yearly mutation rate.

CONCLUSIONS

When compared to the traditional molecular clock methods, new estimated rates from pedigree samples can provide insights into the evolution of well-studied groups such as primates.

Ancient and recent introgression shape the evolutionary history of pollinator adaptation and speciation in a model monkeyflower radiation (Mimulus section Erythranthe)

Inferences about past processes of adaptation and speciation require a gene-scale and genome-wide understanding of the evolutionary history of diverging taxa. In this study, we use genome-wide capture of nuclear gene sequences, plus skimming of organellar sequences, to investigate the phylogenomics of monkeyflowers in Mimulus section Erythranthe (27 accessions from seven species). Taxa within Erythranthe, particularly the parapatric and putatively sister species M. lewisii (bee-pollinated) and M. cardinalis (hummingbird-pollinated), have been a model system for investigating the ecological genetics of speciation and adaptation for over five decades. Across >8000 nuclear loci, multiple methods resolve a predominant species tree in which M. cardinalis groups with other hummingbird-pollinated taxa (37% of gene trees), rather than being sister to M. lewisii (32% of gene trees). We independently corroborate a single evolution of hummingbird pollination syndrome in Erythranthe by demonstrating functional redundancy in genetic complementation tests of floral traits in hybrids; together, these analyses overturn a textbook case of pollination-syndrome convergence. Strong asymmetries in allele sharing (Patterson's D-statistic and related tests) indicate that gene tree discordance reflects ancient and recent introgression rather than incomplete lineage sorting. Consistent with abundant introgression blurring the history of divergence, low-recombination and adaptation-associated regions support the new species tree, while high-recombination regions generate phylogenetic evidence for sister status for M. lewisii and M. cardinalis. Population-level sampling of core taxa also revealed two instances of chloroplast capture, with Sierran M. lewisii and Southern Californian M. parishii each carrying organelle genomes nested within respective sympatric M. cardinalis clades. A recent organellar transfer from M. cardinalis, an outcrosser where selfish cytonuclear dynamics are more likely, may account for the unexpected cytoplasmic male sterility effects of selfer M. parishii organelles in hybrids with M. lewisii. Overall, our phylogenomic results reveal extensive reticulation throughout the evolutionary history of a classic monkeyflower radiation, suggesting that natural selection (re-)assembles and maintains species-diagnostic traits and barriers in the face of gene flow. Our findings further underline the challenges, even in reproductively isolated species, in distinguishing re-use of adaptive alleles from true convergence and emphasize the value of a phylogenomic framework for reconstructing the evolutionary genetics of adaptation and speciation.

Comparative analysis of chloroplast genomes of seven perennial Helianthus species

Sequencing and a comparative analysis of the complete chloroplast genomes of seven perennial Helianthus species were carried out. The chloroplast genomes have a typical quadripartite structure, including large and small single regions and a pair of inverted repeats. Genome sizes were between 151,152 bp and 151,289 bp. The genome of H. grosseserratus was the smallest, while that of H. microcephalus was the largest. The size variation of the chloroplast genomes is substantially due to the change in the length of simple sequence repeats (SSRs) in non-coding regions. An analysis of these SSRs revealed 35 polymorphic loci (average PIC value > 0.5) that can be used to examine ecological and evolutionary processes in wild Helianthus species. Eight divergence hotspots, including five intergenic regions (petN-psbM, clpP intron, rps3-rpl16, ndhD-ccsA, and ndhF-rpl32) and three gene regions (rbcL, ycf1, and ndhF) were also identified in Helianthus chloroplast genomes. The evolutionary selection pressure analysis revealed a strong purifying selection. Only the rbcL gene experienced efficiency of positive selection at the annual/perennial transitions. The inverted repeat (IR)/single copy (SC) boundaries were identical in all of these (Helianthus) species. In general, the comparison of the genomes revealed low levels of sequence variability (Pi = 0.00051). This indicates that the chloroplast genomes of the studied perennial species of Helianthus, in addition to purifying selection, are closely related and have a recent divergence time.

Phylogeographic Approaches to Characterize the Emergence of Plant Pathogens

Phylogeography combines geographic information with phylogenetic and population genomic approaches to infer the evolutionary history of a species or population in a geographic context. This approach has been instrumental in understanding the emergence, spread, and evolution of a range of plant pathogens. In particular, phylogeography can address questions about where a pathogen originated, whether it is native or introduced, and when and how often introductions occurred. We review the theory, methods, and approaches underpinning phylogeographic inference and highlight applications providing novel insights into the emergence and spread of select pathogens. We hope that this review will be useful in assessing the power, pitfalls, and opportunities presented by various phylogeographic approaches.

Environment and pathogens shape local and regional adaptations to climate change in the chocolate tree, Theobroma cacao L

Predicting the potential fate of a species in the face of climate change requires knowing the distribution of molecular adaptations across the geographic range of the species. In this work, we analysed 79 genomes of Theobroma cacao, an Amazonian tree known for the fruit from which chocolate is produced, to evaluate how local and regional molecular signatures of adaptation are distributed across the natural range of the species. We implemented novel techniques that incorporate summary statistics from multiple selection scans to infer selective sweeps. The majority of the molecular adaptations in the genome are not shared among populations. We show that ~71.5% of genes under selection also show significant associations with changes in environmental variables. Our results support the interpretation that these genes contribute to local adaptation of the populations in response to abiotic factors. We also found strong patterns of molecular adaptation in a diverse array of disease resistance genes (6.5% of selective sweeps), suggesting that differential adaptation to pathogens also contributes significantly to local adaptations. Our results are consistent with the interpretation that local selective pressures are more important than regional selective pressures in explaining adaptation across the range of a species.

A paradigm shift in our view of species drives current trends in biological classification

Discontent about changes in species classifications has grown in recent years. Many of these changes are seen as arbitrary, stemming from unjustified conceptual and methodological grounds, or leading to species that are less distinct than those recognised in the past. We argue that current trends in species classification are the result of a paradigm shift toward which systematics and population genetics have converged and that regards species as the phylogenetic lineages that form the branches of the Tree of Life. Species delimitation now consists of determining which populations belong to which individual phylogenetic lineage. This requires inferences on the process of lineage splitting and divergence, a process to which we have only partial access through incidental evidence and assumptions that are themselves subject to refutation. This approach is not free of problems, as horizontal gene transfer, introgression, hybridisation, incorrect assumptions, sampling and methodological biases can mislead inferences of phylogenetic lineages. Increasing precision is demanded through the identification of both sister relationships and processes blurring or mimicking phylogeny, which has triggered, on the one hand, the development of methods that explicitly address such processes and, on the other hand, an increase in geographical and character data sampling necessary to infer/test such processes. Although our resolving power has increased, our knowledge of sister relationships - what we designate as species resolution - remains poor for many taxa and areas, which biases species limits and perceptions about how divergent species are or ought to be. We attribute to this conceptual shift the demise of trinominal nomenclature we are witnessing with the rise of subspecies to species or their rejection altogether; subspecies are raised to species if they are found to correspond to phylogenetic lineages, while they are rejected as fabricated taxa if they reflect arbitrary partitions of continuous or non-hereditary variation. Conservation strategies, if based on taxa, should emphasise species and reduce the use of subspecies to avoid preserving arbitrary partitions of continuous variation; local variation is best preserved by focusing on biological processes generating ecosystem resilience and diversity rather than by formally naming diagnosable units of any kind. Since many binomials still designate complexes of species rather than individual species, many species have been discovered but not named, geographical sampling is sparse, gene lineages have been mistaken for species, plenty of species limits remain untested, and many groups and areas lack adequate species resolution, we cannot avoid frequent changes to classifications as we address these problems. Changes will not only affect neglected taxa or areas, but also popular ones and regions where taxonomic research remained dormant for decades and old classifications were taken for granted.

Sometimes hidden but always there: the assumptions underlying genetic inference of demographic histories

Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present-day and past genetic variation. Advances in laboratory procedures, sequencing and genotyping technologies in the past decades have resulted in massive increases in high-quality genome-wide genetic data from present-day populations and allowed retrieval of genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records. This article is part of the theme issue 'Cross-disciplinary approaches to prehistoric demography'.

Assessing the evolutionary persistence of ecological relationships: A review and preview

Species interactions, such as pollination, parasitism and predation, form the basis of functioning ecosystems. The origins and resilience of such interactions therefore merit attention. However, fossils only occasionally document ancient interactions, and phylogenetic methods are blind to recent interactions. Is there some other way to track shared species experiences? "Comparative demography" examines when pairs of species jointly thrived or declined. By forging links between ecology, epidemiology, and evolutionary biology, this method sheds light on biological adaptation, species resilience, and ecosystem health. Here, we describe how this method works, discuss examples, and suggest future directions in hopes of inspiring interest, imitators, and critics.

Taxonomic status of Chinese blue sheep (Pseudois nayaur): new evidence of a distinct subspecies

The blue sheep is an endemic species to the Qinghai-Tibet Plateau and surrounding regions. It has been regarded as having 2 subspecies: Pseudois nayaur nayaur and P. n. szechuanensis. However, such a classification remains controversial. Herein, we analyze 10 microsatellite loci and part of the mitochondrial control region for clarification in such taxonomic debates. We use samples from 168 individuals from 6 geographic populations covering almost all the distribution areas of the species in China to carry out comparisons. Phylogenetic trees derived from both the microsatellite and mitochondrial markers combined with the discriminant analysis of principal components (DAPC) and the STRUCTURE analysis reveal that the individuals in the Helan Mountains are well grouped with a distinct evolutionary lineage and are significantly different from the other populations of P. n. szechuanensis according to Fst values, implying that this isolated population should be categorized as a valid subspecies; namely, Pseudois nayaur alashanicus. The isolation-by-distance (IBD) analysis shows a significant positive relationship between genetic and geographical distances among the populations.

Evaluation of haplotype callers for next-generation sequencing of viruses

Currently, the standard practice for assembling next-generation sequencing (NGS) reads of viral genomes is to summarize thousands of individual short reads into a single consensus sequence, thus confounding useful intra-host diversity information for molecular phylodynamic inference. It is hypothesized that a few viral strains may dominate the intra-host genetic diversity with a variety of lower frequency strains comprising the rest of the population. Several software tools currently exist to convert NGS sequence variants into haplotypes. Previous benchmarks of viral haplotype reconstruction programs used simulation scenarios that are useful from a mathematical perspective but do not reflect viral evolution and epidemiology. Here, we tested twelve NGS haplotype reconstruction methods using viral populations simulated under realistic evolutionary dynamics. We simulated coalescent-based populations that spanned known levels of viral genetic diversity, including mutation rates, sample size and effective population size, to test the limits of the haplotype reconstruction methods and to ensure coverage of predicted intra-host viral diversity levels (especially HIV-1). All twelve investigated haplotype callers showed variable performance and produced drastically different results that were mainly driven by differences in mutation rate and, to a lesser extent, in effective population size. Most methods were able to accurately reconstruct haplotypes when genetic diversity was low. However, under higher levels of diversity (e.g., those seen intra-host HIV-1 infections), haplotype reconstruction quality was highly variable and, on average, poor. All haplotype reconstruction tools, except QuasiRecomb and ShoRAH, greatly underestimated intra-host diversity and the true number of haplotypes. PredictHaplo outperformed, in regard to highest precision, recall, and lowest UniFrac distance values, the other haplotype reconstruction tools followed by CliqueSNV, which, given more computational time, may have outperformed PredictHaplo. Here, we present an extensive comparison of available viral haplotype reconstruction tools and provide insights for future improvements in haplotype reconstruction tools using both short-read and long-read technologies.

Maximum likelihood estimators for scaled mutation rates in an equilibrium mutation-drift model

The stationary sampling distribution of a neutral decoupled Moran or Wright-Fisher diffusion with neutral mutations is known to first order for a general rate matrix with small but otherwise unconstrained mutation rates. Using this distribution as a starting point we derive results for maximum likelihood estimates of scaled mutation rates from site frequency data under three model assumptions: a twelve-parameter general rate matrix, a nine-parameter reversible rate matrix, and a six-parameter strand-symmetric rate matrix. The site frequency spectrum is assumed to be sampled from a fixed size population in equilibrium, and to consist of allele frequency data at a large number of unlinked sites evolving with a common mutation rate matrix without selective bias. We correct an error in a previous treatment of the same problem (Burden and Tang, 2017) affecting the estimators for the general and strand-symmetric rate matrices. The method is applied to a biological dataset consisting of a site frequency spectrum extracted from short autosomal introns in a sample of Drosophila melanogaster individuals.

Reporting of New tick-borne encephalitis virus strains isolated in Eastern Siberia (Russia) in 1960-2011 and explaining them in an evolutionary context using Bayesian phylogenetic inference

Tick-borne encephalitis virus (TBEV) is one of the main tick-borne viral pathogens of humans. Infection may induce signs of meningitis, encephalitis, paralysis and high fever. TBEV is well studied by molecular phylogenetic methods. The present-day implementation of Bayesian phylogenetic models allows population dynamics to be tracked, providing changes in population size that were not directly observed. However, the description of the past population dynamics of TBEV is rare in the literature. In our investigation, we provide data on the dynamics of viral genetic diversity of TBEV in Zabaikalsky Krai (Eastern Siberia, Russia) revealed by the Bayesian coalescent inference in a BEAST program. As a data set, we used the envelope (E) protein partial gene sequences (1308 nt) of 38 TBEV strains (including six "886-84-like" or Baikalian subtype strains (TBEV-B)), isolated in Zabaikalsky Krai (Eastern Siberia, Russia) in 1960-1963 and 1995-2011. To increase estimations reliability, we compared 9 model combinations by Path sampling and Stepping-stone sampling methods. It has been shown that the genetic diversity decline in the population history of TBEV in the 1950s coincides with the date of the beginning of wide dichlorodiphenyltrichloroethane forest dusting in Siberia. We assumed that the TBEV population on the territory of Siberia went through a genetic bottleneck. Also, we provide data estimating the divergence time of TBEV-B strains and indicate the specific evolution rate of an ancestor lineage of the Baikalian subtype, illustrated on a phylogenetic tree, and reconstructed under a relaxed clock model.

Discrepancies between genetic and ecological divergence patterns suggest a complex biogeographic history in a Neotropical genus

Phylogenetic patterns and the underlying speciation processes can be deduced from morphological, functional, and ecological patterns of species similarity and divergence. In some cases, though, species retain multiple similarities and remain almost indistinguishable; in other cases, evolutionary convergence can make such patterns misleading; very often in such cases, the "true" picture only emerges from carefully built molecular phylogenies, which may come with major surprises. In addition, closely related species may experience gene flow after divergence, thus potentially blurring species delimitation. By means of advanced inferential methods, we studied molecular divergence between species of the Virola genus (Myristicaceae): widespread Virola michelii and recently described, endemic V. kwatae, using widespread V. surinamensis as a more distantly related outgroup with different ecology and morphology-although with overlapping range. Contrary to expectations, we found that the latter, and not V. michelii, was sister to V. kwatae. Therefore, V. kwatae probably diverged from V. surinamensis through a recent morphological and ecological shift, which brought it close to distantly related V. michelii. Through the modeling of the divergence process, we inferred that gene flow between V. surinamensis and V. kwatae stopped soon after their divergence and resumed later, in a classical secondary contact event which did not erase their ecological and morphological differences. While we cannot exclude that initial divergence occurred in allopatry, current species distribution and the absence of geographical barriers make complete isolation during speciation unlikely. We tentatively conclude that (a) it is possible that divergence occurred in allopatry/parapatry and (b) secondary contact did not suppress divergence.

Japan considered from the hypothesis of farmer/language spread

Formally, the Farming/Language Dispersal hypothesis as applied to Japan relates to the introduction of agriculture and spread of the Japanese language (between ca. 500 BC-AD 800). We review current data from genetics, archaeology, and linguistics in relation to this hypothesis. However, evidence bases for these disciplines are drawn from different periods. Genetic data have primarily been sampled from present-day Japanese and prehistoric Jōmon peoples (14,000-300 BC), preceding the introduction of rice agriculture. The best archaeological evidence for agriculture comes from western Japan during the Yayoi period (ca. 900 BC-AD 250), but little is known about northeastern Japan, which is a focal point here. And despite considerable hypothesizing about prehistoric language, the spread of historic languages/ dialects through the islands is more accessible but difficult to relate to prehistory. Though the lack of Yayoi skeletal material available for DNA analysis greatly inhibits direct study of how the pre-agricultural Jōmon peoples interacted with rice agriculturalists, our review of Jōmon genetics sets the stage for further research into their relationships. Modern linguistic research plays an unexpected role in bringing Izumo (Shimane Prefecture) and the Japan Sea coast into consideration in the populating of northeastern Honshu by agriculturalists beyond the Kantō region.