abc: an R package for approximate Bayesian computation (ABC)

Clonal dynamics and somatic evolution of haematopoiesis in mouse

Haematopoietic stem cells maintain blood production throughout life. While extensively characterised using the laboratory mouse, little is known about how the population is sustained and evolves with age. We isolated stem cells and progenitors from young and old mice, identifying 221,890 somatic mutations genome-wide in 1845 single cell-derived colonies, and used phylogenetic analysis to infer the ontogeny and population dynamics of the stem cell pool. Mouse stem cells and progenitors accrue ~45 somatic mutations per year, a rate only about 2-fold greater than human progenitors despite the vastly different organismal sizes and lifespans. Phylogenetic patterns reveal that stem and multipotent progenitor cell pools are both established during embryogenesis, after which they independently self-renew in parallel over life. The stem cell pool grows steadily over the mouse lifespan to approximately 70,000 cells, self-renewing about every six weeks. Aged mice did not display the profound loss of stem cell clonal diversity characteristic of human haematopoietic ageing. However, targeted sequencing revealed small, expanded clones in the context of murine ageing, which were larger and more numerous following haematological perturbations and exhibited a selection landscape similar to humans. Our data illustrate both conserved features of population dynamics of blood and distinct patterns of age-associated somatic evolution in the short-lived mouse.

Extensive immune receptor repertoire diversity in disease-resistant rice landraces

Plants have powerful defense mechanisms and extensive immune receptor repertoires, yet crop monocultures are prone to epidemic diseases. Rice (Oryza sativa) is susceptible to many diseases, such as rice blast caused by Magnaporthe oryzae. Varietal resistance of rice to blast relies on intracellular nucleotide binding, leucine-rich repeat (NLR) receptors that recognize specific pathogen molecules and trigger immune responses. In the Yuanyang terraces in southwest China, rice landraces rarely show severe losses to disease whereas commercial inbred lines show pronounced field susceptibility. Here, we investigate within-landrace NLR sequence diversity of nine rice landraces and eleven modern varieties using complexity reduction techniques. We find that NLRs display high sequence diversity in landraces, consistent with balancing selection, and that balancing selection at NLRs is more pervasive in landraces than modern varieties. Notably, modern varieties lack many ancient NLR haplotypes that are retained in some landraces. Our study emphasizes the value of standing genetic variation that is maintained in farmer landraces as a resource to make modern crops and agroecosystems less prone to disease. The conservation of landraces is, therefore, crucial for ensuring food security in the face of dynamic biotic and abiotic threats.

Inference of Host-Pathogen Interaction Matrices from Genome-Wide Polymorphism Data

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype × genotype (G×G) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of hosts and pathogen genomes (co-genome-wide association studies) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an approximate Bayesian computation method to pinpoint loci with relevant G×G interactions and to infer their underlying interaction matrix. In a combined single nucleotide polymorphism dataset of 451 European humans and their infecting hepatitis C virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (G×G) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of G×G interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key G×G interactions underpinning HCV adaptation to the European human population after a recent expansion.

Whole Genomes Inform Genetic Rescue Strategy for Montane Red Foxes in North America

A few iconic examples have proven the value of facilitated gene flow for counteracting inbreeding depression and staving off extinction; yet, the practice is often not implemented for fear of causing outbreeding depression. Using genomic sequencing, climatic niche modeling, and demographic reconstruction, we sought to assess the risks and benefits of using translocations as a tool for recovery of endangered montane red fox (Vulpes vulpes) populations in the western United States. We demonstrated elevated inbreeding and homozygosity of deleterious alleles across all populations, but especially those isolated in the Cascade and Sierra Nevada ranges. Consequently, translocations would be expected to increase population growth by masking deleterious recessive alleles. Demographic reconstructions further indicated shallow divergences of less than a few thousand years among montane populations, suggesting low risk of outbreeding depression. These genomic-guided findings set the stage for future management, the documentation of which will provide a roadmap for recovery of other data-deficient taxa.

Dragons in the tropics - Phylogeography and speciation in Diporiphora lizards and common geographic breaks in co-distributed taxa

Co-distributed taxa can respond both similarly or differently to the same climatic and geological events, resulting in a range of phylogeographic patterns across the region. Using a nested approach on a taxonomically diverse yet morphologically conservative group of agamid lizards, we first aimed to evaluate more precisely the extent of phylogeographic structuring within the genus. Then, focusing on four lineages within the more widespread species, we assessed the impact of biogeographic barriers on phylogeographic structuring and demographic history of species, comparing to patterns previously observed in co-distributed taxa. These species occur in the Australian Monsoonal Tropics, a vast tropical savanna system with high richness and endemism associated with environmental heterogeneity and past climate fluctuations. The employment of genomic data helped to determine the relationships between specific taxa that were previously difficult to place. We found a local influence of biogeographic and climatic breaks on population dynamics, analogous to other species. We detected high levels of population structure in the West Kimberley and Arnhem Plateau, which are already known for high endemism. However, we also highlighted unique lineages in areas that have been overlooked until recently, in the South Kimberley and West Top End. Climatic and geographical features in the Arnhem Plateau act as a soft barrier between populations in the east and west regions of the Top End. These observations reflect patterns observed for other vertebrates across this rich biome, indicating how climatic variation, species' ecology, and landscape features interact to shape regional diversity and endemism.

Genome structural variants shape adaptive success of an invasive urban malaria vector Anopheles stephensi

Global changes are associated with the emergence of several invasive species. However, the genomic determinants of the adaptive success of an invasive species in a new environment remain poorly understood. Genomic structural variants (SVs), consisting of copy number variants, play an important role in adaptation. SVs often cause large adaptive shifts in ecologically important traits, which makes SVs compelling candidates for driving rapid adaptations to environmental changes, which is critical to invasive success. To address this problem, we investigated the role SVs play in the adaptive success of Anopheles stephensi , a primary vector of urban malaria in South Asia and an invasive malaria vector in several South Asian islands and Africa. We collected whole genome sequencing data from 115 mosquitoes from invasive island populations and four locations from mainland India, an ancestral range for the species. We identified 2,988 duplication copy number variants and 16,038 deletions in these strains, with ∼50% overlapping genes. SVs are enriched in genomic regions with signatures of selective sweeps in the mainland and invasive island populations, implying a putative adaptive role of SVs. Nearly all high-frequency SVs, including the candidate adaptive variants, in the invasive island populations are present on the mainland, suggesting a major contribution of existing variation to the success of the island populations. Among the candidate adaptive SVs, three duplications involving toxin-resistance genes evolved, likely due to the widespread application of insecticides in India since the 1950s. We also identify two SVs associated with the adaptation of An. stephensi larvae to brackish water in the island and two coastal mainland populations, where the mutations likely originated. Our results suggest that existing SVs play a vital role in the evolutionary success of An. stephensi in new environmental conditions.

Modelling the demographic history of human North African genomes points to a recent soft split divergence between populations

BACKGROUND

North African human populations present a complex demographic scenario due to the presence of an autochthonous genetic component and population substructure, plus extensive gene flow from the Middle East, Europe, and sub-Saharan Africa.

RESULTS

We conducted a comprehensive analysis of 364 genomes to construct detailed demographic models for the North African region, encompassing its two primary ethnic groups, the Arab and Amazigh populations. This was achieved through an Approximate Bayesian Computation with Deep Learning (ABC-DL) framework and a novel algorithm called Genetic Programming for Population Genetics (GP4PG). This innovative approach enabled us to effectively model intricate demographic scenarios, utilizing a subset of 16 whole genomes at > 30X coverage. The demographic model suggested by GP4PG exhibited a closer alignment with the observed data compared to the ABC-DL model. Both point to a back-to-Africa origin of North African individuals and a close relationship with Eurasian populations. Results support different origins for Amazigh and Arab populations, with Amazigh populations originating back in Epipaleolithic times, while GP4PG supports Arabization as the main source of Middle Eastern ancestry. The GP4PG model includes population substructure in surrounding populations (sub-Saharan Africa and Middle East) with continuous decaying gene flow after population split. Contrary to ABC-DL, the best GP4PG model does not require pulses of admixture from surrounding populations into North Africa pointing to soft splits as drivers of divergence in North Africa.

CONCLUSIONS

We have built a demographic model on North Africa that points to a back-to-Africa expansion and a differential origin between Arab and Amazigh populations.

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

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

Recurrent gene flow between Neanderthals and modern humans over the past 200,000 years

Although it is well known that the ancestors of modern humans and Neanderthals admixed, the effects of gene flow on the Neanderthal genome are not well understood. We develop methods to estimate the amount of human-introgressed sequences in Neanderthals and apply it to whole-genome sequence data from 2000 modern humans and three Neanderthals. We estimate that Neanderthals have 2.5 to 3.7% human ancestry, and we leverage human-introgressed sequences in Neanderthals to revise estimates of Neanderthal ancestry in modern humans, show that Neanderthal population sizes were significantly smaller than previously estimated, and identify two distinct waves of modern human gene flow into Neanderthals. Our data provide insights into the genetic legacy of recurrent gene flow between modern humans and Neanderthals.

Evidence for Admixture and Rapid Evolution During Glacial Climate Change in an Alpine Specialist

The pace of current climate change is expected to be problematic for alpine flora and fauna, as their adaptive capacity may be limited by small population size. Yet, despite substantial genetic drift following post-glacial recolonization of alpine habitats, alpine species are notable for their success surviving in highly heterogeneous environments. Population genomic analyses demonstrating how alpine species have adapted to novel environments with limited genetic diversity remain rare, yet are important in understanding the potential for species to respond to contemporary climate change. In this study, we explored the evolutionary history of alpine ground beetles in the Nebria ingens complex, including the demographic and adaptive changes that followed the last glacier retreat. We first tested alternative models of evolutionary divergence in the species complex. Using millions of genome-wide SNP markers from hundreds of beetles, we found evidence that the N. ingens complex has been formed by past admixture of lineages responding to glacial cycles. Recolonization of alpine sites involved a distributional range shift to higher elevation, which was accompanied by a reduction in suitable habitat and the emergence of complex spatial genetic structure. We tested several possible genetic pathways involved in adaptation to heterogeneous local environments using genome scan and genotype-environment association approaches. From the identified genes, we found enriched functions associated with abiotic stress responses, with strong evidence for adaptation to hypoxia-related pathways. The results demonstrate that despite rapid demographic change, alpine beetles in the N. ingens complex underwent rapid physiological evolution.

Hidden invasiveness of non-native Schlegel's Japanese gecko (Reptilia: Squamata: Gekkonidae) and three-way competition among natives and non-natives in Japan

In this study, we investigated the invasiveness of Gekko japonicus, a prevalent gecko species in Japan and an ancient non-native species, focusing on its competition with both the undescribed endemic Gekko species (referred to as Nishiyamori in Japanese) and G. hokouensis. These species are co-distributed with G. japonicus, leading us to hypothesize that G. japonicus was invasive upon its initial introduction. We employed niche analysis and population genetics through ddRAD-seq to assess the historical invasiveness of G. japonicus by comparing regions with and without interspecies competition. Our niche analysis across the Goto Islands, Hiradojima Island (colonized by G. japonicus) and the Koshikishima Islands (not colonized by G. japonicus) indicated that endemic Gekko sp. alter their microhabitat usage in response to invasions by other gecko species, despite having similar suitable habitats and microhabitat preferences. Population genetic analysis revealed significant population declines in Gekko sp. within areas of introduced competition, in contrast to stable populations in areas without such competition. These findings suggest a tripartite competitive relationship among the gecko species, with G. japonicus and G. hokouensis invasions restricting the distribution of the endemic Gekko sp. Consequently, G. japonicus may have historically acted as an invasive species. Acknowledging the historical dynamics of current biodiversity is crucial for addressing complex ecological issues and making informed conservation decisions.

A historical stepping-stone path for an island-colonizing cactus across a submerged "bridge" archipelago

Here we use population genomic data (ddRAD-Seq) and ecological niche modeling to test biogeographic hypotheses for the divergence of the island-endemic cactus species Cereus insularis Hemsl. (Cereeae; Cactaceae) from its sister species C. fernambucensis Lem. The Cereus insularis grows in the Fernando de Noronha Islands (FNI), a Neotropical archipelago located 350 km off the Brazilian Atlantic Forest (BAF) coast. Phylogeographic reconstructions support a northward expansion by the common ancestor of C. insularis and C. fernambucensis along the mainland BAF coast, with C. insularis diverging from the widespread mainland taxon C. fernambucensis after colonizing FNI in the late Pleistocene. The morphologically distinct C. insularis is monophyletic and nested within C. fernambucensis, as expected from a progenitor-derivative speciation model. We tested alternative biogeographic and demographic hypotheses for the colonization of the FNI using Approximate Bayesian Computation. We found the greatest support for a stepping-stone path that emerged during periods of decreased sea level (the "bridge" hypothesis), in congruence with historical ecological niche modeling that shows highly suitable habitats on stepping-stone islands during glacial periods. The outlier analyses reveal signatures of selection in C. insularis, suggesting a putative role of adaptation driving rapid anagenic differentiation of this species in FNI.

Fitting individual-based models of spatial population dynamics to long-term monitoring data

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Coevolution with hosts underpins speciation in brood-parasitic cuckoos

Coevolution between interacting species is thought to increase biodiversity, but evidence linking microevolutionary processes to macroevolutionary patterns is scarce. We leveraged two decades of behavioral research coupled with historical DNA analysis to reveal that coevolution with hosts underpins speciation in brood-parasitic bronze-cuckoos. At a macroevolutionary scale, we show that highly virulent brood-parasitic taxa have higher speciation rates and are more likely to speciate in sympatry than less-virulent and nonparasitic relatives. We reveal the microevolutionary process underlying speciation: Hosts reject cuckoo nestlings, which selects for mimetic cuckoo nestling morphology. Where cuckoos exploit multiple hosts, selection for mimicry drives genetic and phenotypic divergence corresponding to host preference, even in sympatry. Our work elucidates perhaps the most common, but poorly characterized, evolutionary process driving biological diversification.

The sweet tabaiba or there and back again: phylogeographical history of the Macaronesian Euphorbia balsamifera

BACKGROUND AND AIMS

Biogeographical relationships between the Canary Islands and north-west Africa are often explained by oceanic dispersal and geographical proximity. Sister-group relationships between Canarian and eastern African/Arabian taxa, the 'Rand Flora' pattern, are rare among plants and have been attributed to the extinction of north-western African populations. Euphorbia balsamifera is the only representative species of this pattern that is distributed in the Canary Islands and north-west Africa; it is also one of few species present in all seven islands. Previous studies placed African populations of E. balsamifera as sister to the Canarian populations, but this relationship was based on herbarium samples with highly degraded DNA. Here, we test the extinction hypothesis by sampling new continental populations; we also expand the Canarian sampling to examine the dynamics of island colonization and diversification.

METHODS

Using target enrichment with genome skimming, we reconstructed phylogenetic relationships within E. balsamifera and between this species and its disjunct relatives. A single nucleotide polymorphism dataset obtained from the target sequences was used to infer population genetic diversity patterns. We used convolutional neural networks to discriminate among alternative Canary Islands colonization scenarios.

KEY RESULTS

The results confirmed the Rand Flora sister-group relationship between western E. balsamifera and Euphorbia adenensis in the Eritreo-Arabian region and recovered an eastern-western geographical structure among E. balsamifera Canarian populations. Convolutional neural networks supported a scenario of east-to-west island colonization, followed by population extinctions in Lanzarote and Fuerteventura and recolonization from Tenerife and Gran Canaria; a signal of admixture between the eastern island and north-west African populations was recovered.

CONCLUSIONS

Our findings support the Surfing Syngameon Hypothesis for the colonization of the Canary Islands by E. balsamifera, but also a recent back-colonization to the continent. Populations of E. balsamifera from northwest Africa are not the remnants of an ancestral continental stock, but originated from migration events from Lanzarote and Fuerteventura. This is further evidence that oceanic archipelagos are not a sink for biodiversity, but may be a source of new genetic variability.

Experimental Evaluation of a Direct Fitness Effect of the De Novo Evolved Mouse Gene Pldi

De novo evolved genes emerge from random parts of noncoding sequences and have, therefore, no homologs from which a function could be inferred. While expression analysis and knockout experiments can provide insights into the function, they do not directly test whether the gene is beneficial for its carrier. Here, we have used a seminatural environment experiment to test the fitness of the previously identified de novo evolved mouse gene Pldi, which has been implicated to have a role in sperm differentiation. We used a knockout mouse strain for this gene and competed it against its parental wildtype strain for several generations of free reproduction. We found that the knockout (ko) allele frequency decreased consistently across three replicates of the experiment. Using an approximate Bayesian computation framework that simulated the data under a demographic scenario mimicking the experiment's demography, we could estimate a selection coefficient ranging between 0.21 and 0.61 for the wildtype allele compared to the ko allele in males, under various models. This implies a relatively strong selective advantage, which would fix the new gene in less than hundred generations after its emergence.

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.

Evolution of homologous recombination rates across bacteria

Bacteria are nonsexual organisms but are capable of exchanging DNA at diverse degrees through homologous recombination. Intriguingly, the rates of recombination vary immensely across lineages where some species have been described as purely clonal and others as "quasi-sexual." However, estimating recombination rates has proven a difficult endeavor and estimates often vary substantially across studies. It is unclear whether these variations reflect natural variations across populations or are due to differences in methodologies. Consequently, the impact of recombination on bacterial evolution has not been extensively evaluated and the evolution of recombination rate-as a trait-remains to be accurately described. Here, we developed an approach based on Approximate Bayesian Computation that integrates multiple signals of recombination to estimate recombination rates. We inferred the rate of recombination of 162 bacterial species and one archaeon and tested the robustness of our approach. Our results confirm that recombination rates vary drastically across bacteria; however, we found that recombination rate-as a trait-is conserved in several lineages but evolves rapidly in others. Although some traits are thought to be associated with recombination rate (e.g., GC-content), we found no clear association between genomic or phenotypic traits and recombination rate. Overall, our results provide an overview of recombination rate, its evolution, and its impact on bacterial evolution.

A survey of chromosomal instability measures across mechanistic models

Chromosomal instability (CIN) is the persistent reshuffling of cancer karyotypes via chromosome mis-segregation during cell division. In cancer, CIN exists at varying levels that have differential effects on tumor progression. However, mis-segregation rates remain challenging to assess in human cancer despite an array of available measures. We evaluated measures of CIN by comparing quantitative methods using specific, inducible phenotypic CIN models of chromosome bridges, pseudobipolar spindles, multipolar spindles, and polar chromosomes. For each, we measured CIN fixed and timelapse fluorescence microscopy, chromosome spreads, six-centromere FISH, bulk transcriptomics, and single-cell DNA sequencing (scDNAseq). As expected, microscopy of tumor cells in live and fixed samples significantly correlated (R = 0.72; P < 0.001) and sensitively detect CIN. Cytogenetics approaches include chromosome spreads and 6-centromere FISH, which also significantly correlate (R = 0.76; P < 0.001) but had limited sensitivity for lower rates of CIN. Bulk genomic DNA signatures and bulk transcriptomic scores, CIN70 and HET70, did not detect CIN. By contrast, scDNAseq detects CIN with high sensitivity, and significantly correlates with imaging methods (R = 0.82; P < 0.001). In summary, single-cell methods such as imaging, cytogenetics, and scDNAseq can measure CIN, with the latter being the most comprehensive method accessible to clinical samples. To facilitate the comparison of CIN rates between phenotypes and methods, we propose a standardized unit of CIN: Mis-segregations per Diploid Division. This systematic analysis of common CIN measures highlights the superiority of single-cell methods and provides guidance for measuring CIN in the clinical setting.

Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation

The development of mathematical models for studying newly emerging and re-emerging infectious diseases has gained momentum due to global events. The gyrodactylid-fish system, like many host-parasite systems, serves as a valuable resource for ecological, evolutionary, and epidemiological investigations owing to its ease of experimental manipulation and long-term monitoring. Although this system has an existing individual-based model, it falls short in capturing information about species-specific microhabitat preferences and other biological details for different Gyrodactylus strains across diverse fish populations. This current study introduces a new individual-based stochastic simulation model that uses a hybrid τ -leaping algorithm to incorporate this essential data, enhancing our understanding of the complexity of the gyrodactylid-fish system. We compare the infection dynamics of three gyrodactylid strains across three host populations. A modified sequential-type approximate Bayesian computation (ABC) method, based on sequential Monte Carlo and sequential importance sampling, is developed. Additionally, we establish two penalised local-linear regression methods (based on L1 and L2 regularisations) for ABC post-processing analysis to fit our model using existing empirical data. With the support of experimental data and the fitted mathematical model, we address open biological questions for the first time and propose directions for future studies on the gyrodactylid-fish system. The adaptability of the mathematical model extends beyond the gyrodactylid-fish system to other host-parasite systems. Furthermore, the modified ABC methodologies provide efficient calibration for other multi-parameter models characterised by a large set of correlated or independent summary statistics.

An efficient and robust ABC approach to infer the rate and strength of adaptation

Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in nonmodel species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to nonmodel genomes. We apply ABC-MK to the human proteome and a set of known virus interacting proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.

Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation

MOTIVATION

The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being surpassed by structurally constrained substitution (SCS) models. The SCS models often consider site-dependent evolution, a process that provides realism but complicates their implementation into likelihood functions that are commonly used for substitution model selection.

RESULTS

We present a method to perform selection among site-dependent SCS models, also among empirical and site-dependent SCS models, based on the approximate Bayesian computation (ABC) approach and its implementation into the computational framework ProteinModelerABC. The framework implements ABC with and without regression adjustments and includes diverse empirical and site-dependent SCS models of protein evolution. Using extensive simulated data, we found that it provides selection among SCS and empirical models with acceptable accuracy. As illustrative examples, we applied the framework to analyze a variety of protein families observing that SCS models fit them better than the corresponding best-fitting empirical substitution models.

AVAILABILITY AND IMPLEMENTATION

ProteinModelerABC is freely available from https://github.com/DavidFerreiro/ProteinModelerABC, can run in parallel and includes a graphical user interface. The framework is distributed with detailed documentation and ready-to-use examples.

Reconstructing colonization dynamics to establish how human activities transformed island biodiversity

Drivers and dynamics of initial human migrations across individual islands and archipelagos are poorly understood, hampering assessments of subsequent modification of island biodiversity. We developed and tested a new statistical-simulation approach for reconstructing the pattern and pace of human migration across islands at high spatiotemporal resolutions. Using Polynesian colonisation of New Zealand as an example, we show that process-explicit models, informed by archaeological records and spatiotemporal reconstructions of past climates and environments, can provide new and important insights into the patterns and mechanisms of arrival and establishment of people on islands. We find that colonisation of New Zealand required there to have been a single founding population of approximately 500 people, arriving between 1233 and 1257 AD, settling multiple areas, and expanding rapidly over both North and South Islands. These verified spatiotemporal reconstructions of colonisation dynamics provide new opportunities to explore more extensively the potential ecological impacts of human colonisation on New Zealand's native biota and ecosystems.

A cosmopolitan inversion facilitates seasonal adaptation in overwintering Drosophila

Fluctuations in the strength and direction of natural selection through time are a ubiquitous feature of life on Earth. One evolutionary outcome of such fluctuations is adaptive tracking, wherein populations rapidly adapt from standing genetic variation. In certain circumstances, adaptive tracking can lead to the long-term maintenance of functional polymorphism despite allele frequency change due to selection. Although adaptive tracking is likely a common process, we still have a limited understanding of aspects of its genetic architecture and its strength relative to other evolutionary forces such as drift. Drosophila melanogaster living in temperate regions evolve to track seasonal fluctuations and are an excellent system to tackle these gaps in knowledge. By sequencing orchard populations collected across multiple years, we characterized the genomic signal of seasonal demography and identified that the cosmopolitan inversion In(2L)t facilitates seasonal adaptive tracking and shows molecular footprints of selection. A meta-analysis of phenotypic studies shows that seasonal loci within In(2L)t are associated with behavior, life history, physiology, and morphological traits. We identify candidate loci and experimentally link them to phenotype. Our work contributes to our general understanding of fluctuating selection and highlights the evolutionary outcome and dynamics of contemporary selection on inversions.

Genomic Architecture Predicts Tree Topology, Population Structuring, and Demographic History in Amazonian Birds

Geographic barriers are frequently invoked to explain genetic structuring across the landscape. However, inferences on the spatial and temporal origins of population variation have been largely limited to evolutionary neutral models, ignoring the potential role of natural selection and intrinsic genomic processes known as genomic architecture in producing heterogeneity in differentiation across the genome. To test how variation in genomic characteristics (e.g. recombination rate) impacts our ability to reconstruct general patterns of differentiation between species that cooccur across geographic barriers, we sequenced the whole genomes of multiple bird populations that are distributed across rivers in southeastern Amazonia. We found that phylogenetic relationships within species and demographic parameters varied across the genome in predictable ways. Genetic diversity was positively associated with recombination rate and negatively associated with species tree support. Gene flow was less pervasive in genomic regions of low recombination, making these windows more likely to retain patterns of population structuring that matched the species tree. We further found that approximately a third of the genome showed evidence of selective sweeps and linked selection, skewing genome-wide estimates of effective population sizes and gene flow between populations toward lower values. In sum, we showed that the effects of intrinsic genomic characteristics and selection can be disentangled from neutral processes to elucidate spatial patterns of population differentiation.

Millennial processes of population decline, range contraction and near extinction of the European bison

European bison (Bison bonasus) were widespread throughout Europe during the late Pleistocene. However, the contributions of environmental change and humans to their near extinction have never been resolved. Using process-explicit models, fossils and ancient DNA, we disentangle the combinations of threatening processes that drove population declines and regional extinctions of European bison through space and across time. We show that the population size of European bison declined abruptly at the termination of the Pleistocene in response to rapid environmental change, hunting by humans and their interaction. Human activities prevented populations of European bison from rebounding in the Holocene, despite improved environmental conditions. Hunting caused range loss in the north and east of its distribution, while land use change was responsible for losses in the west and south. Advances in hunting technologies from 1500 CE were needed to simulate low abundances observed in 1870 CE. While our findings show that humans were an important driver of the extinction of the European bison in the wild, vast areas of its range vanished during the Pleistocene-Holocene transition because of post-glacial environmental change. These areas of its former range have been climatically unsuitable for millennia and should not be considered in reintroduction efforts.

Whole genomes from Angola and Mozambique inform about the origins and dispersals of major African migrations

As the continent of origin for our species, Africa harbours the highest levels of diversity anywhere on Earth. However, many regions of Africa remain under-sampled genetically. Here we present 350 whole genomes from Angola and Mozambique belonging to ten Bantu ethnolinguistic groups, enabling the construction of a reference variation catalogue including 2.9 million novel SNPs. We investigate the emergence of Bantu speaker population structure, admixture involving migrations across sub-Saharan Africa and model the demographic histories of Angolan and Mozambican Bantu speakers. Our results bring together concordant views from genomics, archaeology, and linguistics to paint an updated view of the complexity of the Bantu Expansion. Moreover, we generate reference panels that better represents the diversity of African populations involved in the trans-Atlantic slave trade, improving imputation accuracy in African Americans and Brazilians. We anticipate that our collection of genomes will form the foundation for future African genomic healthcare initiatives.

Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality

Diabetic neuropathy is a disorder characterized by impaired nerve function and reduction of the number of epidermal nerve fibers per epidermal surface. Additionally, as neuropathy related nerve fiber loss and regrowth progresses over time, the two-dimensional spatial arrangement of the nerves becomes more clustered. These observations suggest that with development of neuropathy, the spatial pattern of diminished skin innervation is defined by a thinning process which remains incompletely characterized. We regard samples obtained from healthy controls and subjects suffering from diabetic neuropathy as realisations of planar point processes consisting of nerve entry points and nerve endings, and propose point process models based on spatial thinning to describe the change as neuropathy advances. Initially, the hypothesis that the nerve removal occurs completely at random is tested using independent random thinning of healthy patterns. Then, a dependent parametric thinning model that favors the removal of isolated nerve trees is proposed. Approximate Bayesian computation is used to infer the distribution of the model parameters, and the goodness-of-fit of the models is evaluated using both non-spatial and spatial summary statistics. Our findings suggest that the nerve mortality process changes as neuropathy advances.

Population Genomics of the Mostly Thelytokous Diplolepis rosae (Linnaeus, 1758) (Hymenoptera: Cynipidae) Reveals Population-specific Selection for Sex

In Hymenoptera, arrhenotokous parthenogenesis (arrhenotoky) is a common reproductive mode. Thelytokous parthenogenesis (thelytoky), when virgin females produce only females, is less common and is found in several taxa. In our study, we assessed the efficacy of recombination and the effect of thelytoky on the genome structure of Diplolepis rosae, a gall wasp-producing bedeguars in dog roses. We assembled a high-quality reference genome using Oxford Nanopore long-read technology and sequenced 17 samples collected in France with high-coverage Illumina reads. We found two D. rosae peripatric lineages that differed in the level of recombination and homozygosity. One of the D. rosae lineages showed a recombination rate that was 13.2 times higher and per-individual heterozygosity that was 1.6 times higher. In the more recombining lineage, the genes enriched in functions related to male traits ('sperm competition", "insemination", and "copulation" gene ontology terms) showed signals of purifying selection, whereas in the less recombining lineage, the same genes showed traces pointing towards balancing or relaxed selection. Thus, although D. rosae reproduces mainly by thelytoky, selection may act to maintain sexual reproduction.

The genetic diversity of Nipah virus across spatial scales

Nipah virus (NiV), a highly lethal virus in humans, circulates silently in Pteropus bats throughout South and Southeast Asia. Difficulty in obtaining genomes from bats means we have a poor understanding of NiV diversity, including how many lineages circulate within a roost and the spread of NiV over increasing spatial scales. Here we develop phylogenetic approaches applied to the most comprehensive collection of genomes to date (N=257, 175 from bats, 73 from humans) from six countries over 22 years (1999-2020). In Bangladesh, where most human infections occur, we find evidence of increased spillover risk from one of the two co-circulating sublineages. We divide the four major NiV sublineages into 15 genetic clusters (emerged 20-44 years ago). Within any bat roost, there are an average of 2.4 co-circulating genetic clusters, rising to 5.5 clusters at areas of 1,500-2,000 km2. Using Approximate Bayesian Computation fit to a spatial signature of viral diversity, we estimate that each genetic cluster occupies an average area of 1.3 million km2 (95%CI: 0.6-2.3 million), with 14 clusters in an area of 100,000 km2 (95%CI: 6-24). In the few sites in Bangladesh and Cambodia where genomic surveillance has been concentrated, we estimate that most of the genetic clusters have been identified, but only ~15% of overall NiV diversity has been uncovered. Our findings are consistent with entrenched co-circulation of distinct lineages, even within individual roosts, coupled with slow migration over larger spatial scales.

Temporal matches between monarch butterfly and milkweed population changes over the past 25,000 years

In intimate ecological interactions, the interdependency of species may result in correlated demographic histories. For species of conservation concern, understanding the long-term dynamics of such interactions may shed light on the drivers of population decline. Here, we address the demographic history of the monarch butterfly, Danaus plexippus, and its dominant host plant, the common milkweed Asclepias syriaca (A. syriaca), using broad-scale sampling and genomic inference. Because genetic resources for milkweed have lagged behind those for monarchs, we first release a chromosome-level genome assembly and annotation for common milkweed. Next, we show that despite its enormous geographic range across eastern North America, A. syriaca is best characterized as a single, roughly panmictic population. Using approximate Bayesian computation with random forests (ABC-RF), a machine learning method for reconstructing demographic histories, we show that both monarchs and milkweed experienced population expansion during the most recent recession of North American glaciers 10,000-20,000 years ago. Our data also identify concurrent population expansions in both species during the large-scale clearing of eastern forests (∼200 years ago). Finally, we find no evidence that either species experienced a reduction in effective population size over the past 75 years. Thus, the well-documented decline of monarch abundance over the past 40 years is not visible in our genomic dataset, reflecting a possible mismatch of the overwintering census population to effective population size in this species.

The East Asian wild apples, Malus baccata (L.) Borkh and Malus hupehensis (Pamp.) Rehder., are additional contributors to the genomes of cultivated European and Chinese varieties

The domestication process in long-lived plant perennials differs dramatically from that of annuals, with a huge amount of genetic exchange between crop and wild populations. Though apple is a major fruit crop grown worldwide, the contribution of wild apple species to the genetic makeup of the cultivated apple genome remains a topic of intense study. We used population genomics approaches to investigate the contributions of several wild apple species to European and Chinese rootstock and dessert genomes, with a focus on the extent of wild-crop gene flow. Population genetic structure inferences revealed that the East Asian wild apples, Malus baccata (L.) Borkh and M. hupehensis (Pamp.), form a single panmictic group, and that the European dessert and rootstock apples form a specific gene pool whereas the Chinese dessert and rootstock apples were a mixture of three wild gene pools, suggesting different evolutionary histories of European and Chinese apple varieties. Coalescent-based inferences and gene flow estimates indicated that M. baccata - M. hupehensis contributed to the genome of both European and Chinese cultivated apples through wild-to-crop introgressions, and not as an initial contributor as previously supposed. We also confirmed the contribution through wild-to-crop introgressions of Malus sylvestris Mill. to the cultivated apple genome. Apple tree domestication is therefore one example in woody perennials that involved gene flow from several wild species from multiple geographical areas. This study provides an example of a complex protracted process of domestication in long-lived plant perennials, and is a starting point for apple breeding programmes.

Genetic differentiation and evolution of broad-leaved evergreen shrub and tree varieties of Daphniphyllum macropodum (Daphniphyllaceae)

Tree form evolution is an important ecological specialization for woody species, but its evolutionary process with adaptation is poorly understood, especially on the microevolutionary scale. Daphniphyllum macropodum comprises two varieties: a tree variety growing in a warm temperate climate with light snowfall and a shrub variety growing in a cool temperate climate with heavy snowfall in Japan. Chloroplast DNA variations and genome-wide single-nucleotide polymorphisms across D. macropodum populations and D. teijsmannii as an outgroup were used to reveal the evolutionary process of the shrub variety. Population genetic analysis indicated that the two varieties diverged but were weakly differentiated. Approximate Bayesian computation analysis supported a scenario that assumed migration between the tree variety and the southern populations of the shrub variety. We found migration between the two varieties where the distributions of the two varieties are in contact, and it is concordant with higher tree height in the southern populations of the shrub variety than the northern populations. The genetic divergence between the two varieties was associated with snowfall. The heavy snowfall climate is considered to have developed since the middle Quaternary in this region. The estimated divergence time between the two varieties suggests that the evolution of the two varieties may be concordant with such paleoclimatic change.

Ghost admixture in eastern gorillas

Archaic admixture has had a substantial impact on human evolution with multiple events across different clades, including from extinct hominins such as Neanderthals and Denisovans into modern humans. In great apes, archaic admixture has been identified in chimpanzees and bonobos but the possibility of such events has not been explored in other species. Here, we address this question using high-coverage whole-genome sequences from all four extant gorilla subspecies, including six newly sequenced eastern gorillas from previously unsampled geographic regions. Using approximate Bayesian computation with neural networks to model the demographic history of gorillas, we find a signature of admixture from an archaic 'ghost' lineage into the common ancestor of eastern gorillas but not western gorillas. We infer that up to 3% of the genome of these individuals is introgressed from an archaic lineage that diverged more than 3 million years ago from the common ancestor of all extant gorillas. This introgression event took place before the split of mountain and eastern lowland gorillas, probably more than 40 thousand years ago and may have influenced perception of bitter taste in eastern gorillas. When comparing the introgression landscapes of gorillas, humans and bonobos, we find a consistent depletion of introgressed fragments on the X chromosome across these species. However, depletion in protein-coding content is not detectable in eastern gorillas, possibly as a consequence of stronger genetic drift in this species.

A stabilizing eco-evolutionary feedback loop in the wild

There is increasing evidence that evolutionary and ecological processes can operate on the same timescale1,2 (i.e., contemporary time). As such, evolution can be sufficiently rapid to affect ecological processes such as predation or competition. Thus, evolution can influence population, community, and ecosystem-level dynamics. Indeed, studies have now shown that evolutionary dynamics can alter community structure3,4,5,6 and ecosystem function.7,8,9,10 In turn, shifts in ecological dynamics driven by evolution might feed back to affect the evolutionary trajectory of individual species.11 This feedback loop, where evolutionary and ecological changes reciprocally affect one another, is a central tenet of eco-evolutionary dynamics.1,12 However, most work on such dynamics in natural populations has focused on one-way causal associations between ecology and evolution.13 Hence, direct empirical evidence for eco-evolutionary feedback is rare and limited to laboratory or mesocosm experiments.13,14,15,16 Here, we show in the wild that eco-evolutionary dynamics in a plant-feeding arthropod community involve a negative feedback loop. Specifically, adaptation in cryptic coloration in a stick-insect species mediates bird predation, with local maladaptation increasing predation. In turn, the abundance of arthropods is reduced by predation. Here, we experimentally manipulate arthropod abundance to show that these changes at the community level feed back to affect the stick-insect evolution. Specifically, low-arthropod abundance increases the strength of selection on crypsis, increasing local adaptation of stick insects in a negative feedback loop. Our results suggest that eco-evolutionary feedbacks are able to stabilize complex systems by preventing consistent directional change and therefore increasing resilience.

Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.

A survey of CIN measures across mechanistic models

Chromosomal instability (CIN) is the persistent reshuffling of cancer karyotypes via chromosome mis-segregation during cell division. In cancer, CIN exists at varying levels that have differential effects on tumor progression. However, mis-segregation rates remain challenging to assess in human cancer despite an array of available measures. We evaluated measures of CIN by comparing quantitative methods using specific, inducible phenotypic CIN models of chromosome bridges, pseudobipolar spindles, multipolar spindles, and polar chromosomes. For each, we measured CIN fixed and timelapse fluorescence microscopy, chromosome spreads, 6-centromere FISH, bulk transcriptomics, and single cell DNA sequencing (scDNAseq). As expected, microscopy of tumor cells in live and fixed samples correlated well (R=0.77; p<0.01) and sensitively detect CIN. Cytogenetics approaches include chromosome spreads and 6-centromere FISH, which also correlate well (R=0.77; p<0.01) but had limited sensitivity for lower rates of CIN. Bulk genomic DNA signatures and bulk transcriptomic scores, CIN70 and HET70, did not detect CIN. By contrast, single-cell DNA sequencing (scDNAseq) detects CIN with high sensitivity, and correlates very well with imaging methods (R=0.83; p<0.01). In summary, single-cell methods such as imaging, cytogenetics, and scDNAseq can measure CIN, with the latter being the most comprehensive method accessible to clinical samples. To facilitate comparison of CIN rates between phenotypes and methods, we propose a standardized unit of CIN: Mis-segregations per Diploid Division (MDD). This systematic analysis of common CIN measures highlights the superiority of single-cell methods and provides guidance for measuring CIN in the clinical setting.

Informative and adaptive distances and summary statistics in sequential approximate Bayesian computation

Calibrating model parameters on heterogeneous data can be challenging and inefficient. This holds especially for likelihood-free methods such as approximate Bayesian computation (ABC), which rely on the comparison of relevant features in simulated and observed data and are popular for otherwise intractable problems. To address this problem, methods have been developed to scale-normalize data, and to derive informative low-dimensional summary statistics using inverse regression models of parameters on data. However, while approaches only correcting for scale can be inefficient on partly uninformative data, the use of summary statistics can lead to information loss and relies on the accuracy of employed methods. In this work, we first show that the combination of adaptive scale normalization with regression-based summary statistics is advantageous on heterogeneous parameter scales. Second, we present an approach employing regression models not to transform data, but to inform sensitivity weights quantifying data informativeness. Third, we discuss problems for regression models under non-identifiability, and present a solution using target augmentation. We demonstrate improved accuracy and efficiency of the presented approach on various problems, in particular robustness and wide applicability of the sensitivity weights. Our findings demonstrate the potential of the adaptive approach. The developed algorithms have been made available in the open-source Python toolbox pyABC.

Genetic and demographic signatures accompanying the evolution of the selfing syndrome in Daphne kiusiana, an evergreen shrub

BACKGROUND AND AIMS

The evolution of mating systems from outcrossing to self-fertilization is a common transition in flowering plants. This shift is often associated with the 'selfing syndrome', which is characterized by less visible flowers with functional changes to control outcrossing. In most cases, the evolutionary history and demographic dynamics underlying the evolution of the selfing syndrome remain poorly understood.

METHODS

Here, we characterize differences in the demographic genetic consequences and associated floral-specific traits between two distinct geographical groups of a wild shrub, Daphne kiusiana, endemic to East Asia; plants in the eastern region (southeastern Korea and Kyushu, Japan) exhibit smaller and fewer flowers compared to those of plants in the western region (southwestern Korea). Genetic analyses were conducted using nuclear microsatellites and chloroplast DNA (multiplexed phylogenetic marker sequencing) datasets.

KEY RESULTS

A high selfing rate with significantly increased homozygosity characterized the eastern lineage, associated with lower levels of visibility and herkogamy in the floral traits. The two lineages harboured independent phylogeographical histories. In contrast to the western lineage, the eastern lineage showed a gradual reduction in the effective population size with no signs of a severe bottleneck despite its extreme range contraction during the last glacial period.

CONCLUSIONS

Our results suggest that the selfing-associated morphological changes in D. kiusiana are of relatively old origin (at least 100 000 years ago) and were driven by directional selection for efficient self-pollination. We provide evidence that the evolution of the selfing syndrome in D. kiusiana is not strongly associated with a severe population bottleneck.

Rapid evolution during climate change: demographic and genetic constraints on adaptation to severe drought

Populations often vary in their evolutionary responses to a shared environmental perturbation. A key hurdle in building more predictive models of rapid evolution is understanding this variation-why do some populations and traits evolve while others do not? We combined long-term demographic and environmental data, estimates of quantitative genetic variance components, a resurrection experiment and individual-based evolutionary simulations to gain mechanistic insights into contrasting evolutionary responses to a severe multi-year drought. We examined five traits in two populations of a native California plant, Clarkia xantiana, at three time points over 7 years. Earlier flowering phenology evolved in only one of the two populations, though both populations experienced similar drought severity and demographic declines and were estimated to have considerable additive genetic variance for flowering phenology. Pairing demographic and experimental data with evolutionary simulations suggested that while seed banks in both populations probably constrained evolutionary responses, a stronger seed bank in the non-evolving population resulted in evolutionary stasis. Gene flow through time via germ banks may be an important, underappreciated control on rapid evolution in response to extreme environmental perturbations.

Phylogenomic analyses reveal incongruences between divergence times and fossil records of freshwater snails in East Asia

Fossils provide important insight into our understanding of phylogenetic history by serving as calibration points for divergence time estimation. However, uncertainties in the fossil record due to parallel evolution and convergent evolution can critically affect estimates of node ages. Here, we compare and contrast estimates of phylogenetic divergence with geologic and fossil history for two freshwater snail genera of the family Viviparidae in East Asia (Cipangopaludina and Margarya). Cipangopaludina species are commonly widely distributed species in East Asia, but extant Margarya species are endemic to the ancient lakes in Yunnan, China. According to some previous studies, parallel evolution or convergent evolution of shell morphology has occurred in the family several times which may affect divergence time estimation using fossil records. In this study, we used SNP data derived from ddRAD-seq loci to investigate population demographic history of both genera. Our results show a common pattern of lake endemic lineages diversifying from widely distributed lineages in the Miocene, and multiple colonization to a single ancient lake occurred in the Pleistocene. Our results indicate substantial incongruence among estimated phylogenomic divergence times, some fossil records, and formation ages of ancient lakes. These findings suggest some fossil records may be misidentified in these groups and highlight the need to carefully evaluate geological evidence and fossil records when using these for divergence time estimation.

Developing an evolutionary baseline model for humans: jointly inferring purifying selection with population history

Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but it is also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection ( e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome ( i.e. , purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, while strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.

Genomics, Population Divergence, and Historical Demography of the World's Largest and Endangered Butterfly, The Queen Alexandra's Birdwing

The world's largest butterfly is the microendemic Papua New Guinean Ornithoptera alexandrae. Despite years of conservation efforts to protect its habitat and breed this up-to-28-cm butterfly, this species still figures as endangered in the IUCN Red List and is only known from two allopatric populations occupying a total of only ∼140 km². Here we aim at assembling reference genomes for this species to investigate its genomic diversity, historical demography and determine whether the population is structured, which could provide guidance for conservation programs attempting to (inter)breed the two populations. Using a combination of long and short DNA reads and RNA sequencing, we assembled six reference genomes of the tribe Troidini, with four annotated genomes of O. alexandrae and two genomes of related species Ornithoptera priamus and Troides oblongomaculatus. We estimated the genomic diversity of the three species, and we proposed scenarios for the historical population demography using two polymorphism-based methods taking into account the characteristics of low-polymorphic invertebrates. Indeed, chromosome-scale assemblies reveal very low levels of nuclear heterozygosity across Troidini, which appears to be exceptionally low for O. alexandrae (lower than 0.01%). Demographic analyses demonstrate low and steadily declining Ne throughout O. alexandrae history, with a divergence into two distinct populations about 10,000 years ago. These results suggest that O. alexandrae distribution has been microendemic for a long time. It should also make local conservation programs aware of the genomic divergence of the two populations, which should not be ignored if any attempt is made to cross the two populations.

The evolutionary history of rice azaleas (Rhododendron tschonoskii alliance) involved niche evolution to a montane environment

PREMISE

The formation of isolated montane geography on islands promotes evolution, speciation, and then radiation if there are ecological changes. Thus, investigating evolutionary histories of montane species and associated ecological changes may help efforts to understand how endemism formed in islands' montane floras. To explore this process, we investigated the evolutionary history of the Rhododendron tschonoskii alliance, which grows in montane environments of the Japanese archipelago and the Korean Peninsula.

METHODS

We studied the five species in the R. tschonoskii alliance and 30 outgroup species, using genome-wide single-nucleotide polymorphisms and cpDNA sequences, in association with environmental analyses.

RESULTS

The monophyletic R. tschonoskii alliance diverged since the late Miocene. Species in the alliance currently inhabit a cold climatic niche that is largely different from that of the outgroup species. We observed clear genetic and niche differentiations between the taxa of the alliance.

CONCLUSIONS

The association of the alliance's evolution with the formation of cooler climates on mountains indicates that it was driven by global cooling since the mid-Miocene and by rapid uplift of mountains since the Pliocene. The combination of geographic and climatic isolation promoted high genetic differentiation between taxa, which has been maintained by climatic oscillations since the Quaternary.

Inferring the mode and strength of ongoing selection

Genome sequence data are no longer scarce. The UK Biobank alone comprises 200,000 individual genomes, with more on the way, leading the field of human genetics toward sequencing entire populations. Within the next decades, other model organisms will follow suit, especially domesticated species such as crops and livestock. Having sequences from most individuals in a population will present new challenges for using these data to improve health and agriculture in the pursuit of a sustainable future. Existing population genetic methods are designed to model hundreds of randomly sampled sequences but are not optimized for extracting the information contained in the larger and richer data sets that are beginning to emerge, with thousands of closely related individuals. Here we develop a new method called trio-based inference of dominance and selection (TIDES) that uses data from tens of thousands of family trios to make inferences about natural selection acting in a single generation. TIDES further improves on the state of the art by making no assumptions regarding demography, linkage, or dominance. We discuss how our method paves the way for studying natural selection from new angles.

Indigenous people from Amazon show genetic signatures of pathogen-driven selection

Ecological conditions in the Amazon rainforests are historically favorable for the transmission of numerous tropical diseases, especially vector-borne diseases. The high diversity of pathogens likely contributes to the strong selective pressures for human survival and reproduction in this region. However, the genetic basis of human adaptation to this complex ecosystem remains unclear. This study investigates the possible footprints of genetic adaptation to the Amazon rainforest environment by analyzing the genomic data of 19 native populations. The results based on genomic and functional analysis showed an intense signal of natural selection in a set of genes related to Trypanosoma cruzi infection, which is the pathogen responsible for Chagas disease, a neglected tropical parasitic disease native to the Americas that is currently spreading worldwide.

Inferring the Demographic History and Inheritance Mode of Tetraploid Species Using ABC

Genomic patterns of diversity and divergence are impacted by certain life history traits, reproductive systems, and demographic history. The latter is characterized by fluctuations in population sizes over time, as well as by temporal patterns of introgression. For a given organism, identifying a demographic history that deviates from the standard neutral model allows a better understanding of its evolution but also helps to reduce the risk of false positives when screening for molecular targets of natural selection. Tetraploid organisms and beyond have demographic histories that are complicated by the mode of polyploidization, the mode of inheritance, and different scenarios of gene flow between sub-genomes and diploid parental species. Here we provide guidelines for experimenters wishing to address these issues through a flexible statistical framework: approximate Bayesian computation (ABC). The emphasis is on the general philosophy of the approach to encourage future users to exploit the enormous flexibility of ABC beyond the limitations imposed by generalist data analysis pipelines.

Deep Learning in Population Genetics

Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, convolutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.

Late Pleistocene landscape changes and habitat specialization as promoters of population genomic divergence in Amazonian floodplain birds

Although vicariant processes are expected to leave similar genomic signatures among codistributed taxa, ecological traits such as habitat and stratum can influence genetic divergence within species. Here, we combined landscape history and habitat specialization to understand the historical and ecological factors responsible for current levels of genetic divergence in three species of birds specialized in seasonally flooded habitats in muddy rivers and which are widespread in the Amazon basin but have isolated populations in the Rio Branco. Populations of the white-bellied spinetail (Mazaria propinqua), lesser wagtail-tyrant (Stigmatura napensis) and bicolored conebill (Conirostrum bicolor) are currently isolated in the Rio Branco by the black-waters of the lower Rio Negro, offering a unique opportunity to test the effect of river colour as a barrier to gene flow. We used ultraconserved elements (UCEs) to test alternative hypotheses of population history in a comparative phylogeographical approach by modelling genetic structure, demographic history and testing for shared divergence time among codistributed taxa. Our analyses revealed that (i) all three populations from the Rio Branco floodplains are genetically distinct from other populations along the Amazon River floodplains; (ii) these divergences are the result of at least two distinct events, consistent with species habitat specialization; and (iii) the most likely model of population evolution includes lower population connectivity during the Late Pleistocene transition (~250,000 years ago), with gene flow being completely disrupted after the Last Glacial Maximum (~21,000 years ago). Our findings highlight how landscape evolution modulates population connectivity in habitat specialist species and how organisms can have different responses to the same historical processes of environmental change, depending on their habitat affinity.

Species delineation and genetic structure of two Chaerephon species (C. pusillus and C. leucogaster) on Madagascar and the Comoro archipelago

Cryptic species diversity is known to be common in bats but remains challenging to study in these mammals, whose natural history traits render their sampling and monitoring challenging. For these animals, indirect genetic approaches provide a powerful tool to gain insight into the evolutionary history and ecology of cryptic bat species. The speciation history of the polyphyletic Chaerephon pumilus species group (Molossidae) is poorly understood, including those found on western Indian Ocean islands. Two species in this complex have been identified in the Comoros: C. pusillus and C. leucogaster. Here, we aim to genetically characterize these two species and investigate their spatial population genetic structure. Analyzing five nuclear microsatellite markers from 200 individuals and one mitochondrial DNA gene (Cyt-b) from 161 (out of the 200) individuals sampled on Madagascar and the Comoros, our findings indicated that these species are genetically differentiated. We observed mitonuclear discordance in numerous individuals (33% of the 161 mtDNA-sequenced individuals). Based on ABC analyses, we found that this pattern could potentially be the result of asymmetric introgressive hybridization from C. leucogaster to C. pusillus and calls for further studies on the demographic history of these species. Moreover, at the intra-specific level, analyses of the microsatellite loci suggested the evidence of a more pronounced, although weak, geographically based genetic structure in C. pusillus than in C. leucogaster. Altogether, our findings provide preliminary insights into the eco-evolutionary aspects of this species complex and warrant further research to understand hybridization dynamics and mechanisms responsible for mitonuclear discordance.