Distinct evolutionary lineages of Schistocephalus parasites infecting co-occurring sculpin and stickleback fishes in Alaska.

Sculpins (coastrange and slimy) and sticklebacks (ninespine and threespine) are widely distributed fishes cohabiting 2 south-central Alaskan lakes (Aleknagik and Iliamna), and all these species are parasitized by cryptic diphyllobothriidean cestodes in the genus Schistocephalus. The goal of this investigation was to test for host-specific parasitic relationships between sculpins and sticklebacks based upon morphological traits (segment counts) and sequence variation across the NADH1 gene. A total of 446 plerocercoids was examined. Large, significant differences in mean segment counts were found between cestodes in sculpin (mean = 112; standard deviation [s.d.] = 15) and stickleback (mean = 86; s.d. = 9) hosts within and between lakes. Nucleotide sequence divergence between parasites from sculpin and stickleback hosts was 20.5%, and Bayesian phylogenetic analysis recovered 2 well-supported clades of cestodes reflecting intermediate host family (i.e. sculpin, Cottidae vs stickleback, Gasterosteidae). Our findings point to the presence of a distinct lineage of cryptic Schistocephalus in sculpins from Aleknagik and Iliamna lakes that warrants further investigation to determine appropriate evolutionary and taxonomic recognition.

Selection and Admixture in a Polytypic Aposematic Frog.

AbstractPhenotypic differentiation within polytypic species is often attributed to selection, particularly when selection might be acting on a trait that serves as a signal for predator avoidance and mate choice. We evaluated this hypothesis by examining phenotypic and genotypic clines between populations of the strawberry poison frog Oophaga pumilio, a polytypic species that exhibits aposematic color pattern variation that is thought to be subject to both natural and sexual selection. Our aim was to assess the extent of admixture and to estimate the strength of selection acting on coloration across a region of Panama where monomorphic populations of distinctly colored frogs are separated by polymorphic populations containing both color variants alongside intermediately colored individuals. We detected sharp clinal transitions across the study region, which is an expected outcome of strong selection, but we also detected evidence of widespread admixture, even at sites far from the phenotypic transition zone. Additionally, genotypic and phenotypic clines were neither concordant nor coincident, and with one exception, selection coefficients estimated from cline attributes were small. These results suggest that strong selection is not required for the maintenance of phenotypic divergence within polytypic species, challenging the long-standing notion that strong selection is implicit in the evolution of warning signals.

Complex eco-evolutionary responses of a foundational coastal marsh plant to global change.

Predicting the fate of coastal marshes requires understanding how plants respond to rapid environmental change. Environmental change can elicit shifts in trait variation attributable to phenotypic plasticity and act as selective agents to shift trait means, resulting in rapid evolution. Comparably, less is known about the potential for responses to reflect the evolution of trait plasticity. Here, we assessed the relative magnitude of eco-evolutionary responses to interacting global change factors using a multifactorial experiment. We exposed replicates of 32 Schoenoplectus americanus genotypes 'resurrected' from century-long, soil-stored seed banks to ambient or elevated CO2 , varying levels of inundation, and the presence of a competing marsh grass, across two sites with different salinities. Comparisons of responses to global change factors among age cohorts and across provenances indicated that plasticity has evolved in five of the seven traits measured. Accounting for evolutionary factors (i.e. evolution and sources of heritable variation) in statistical models explained an additional 9-31% of trait variation. Our findings indicate that evolutionary factors mediate ecological responses to environmental change. The magnitude of evolutionary change in plant traits over the last century suggests that evolution could play a role in pacing future ecosystem response to environmental change.

Making the Most of Clumping and Thresholding for Polygenic Scores.

Polygenic prediction has the potential to contribute to precision medicine. Clumping and thresholding (C+T) is a widely used method to derive polygenic scores. When using C+T, several p value thresholds are tested to maximize predictive ability of the derived polygenic scores. Along with this p value threshold, we propose to tune three other hyper-parameters for C+T. We implement an efficient way to derive thousands of different C+T scores corresponding to a grid over four hyper-parameters. For example, it takes a few hours to derive 123K different C+T scores for 300K individuals and 1M variants using 16 physical cores. We find that optimizing over these four hyper-parameters improves the predictive performance of C+T in both simulations and real data applications as compared to tuning only the p value threshold. A particularly large increase can be noted when predicting depression status, from an AUC of 0.557 (95% CI: [0.544-0.569]) when tuning only the p value threshold to an AUC of 0.592 (95% CI: [0.580-0.604]) when tuning all four hyper-parameters we propose for C+T. We further propose stacked clumping and thresholding (SCT), a polygenic score that results from stacking all derived C+T scores. Instead of choosing one set of hyper-parameters that maximizes prediction in some training set, SCT learns an optimal linear combination of all C+T scores by using an efficient penalized regression. We apply SCT to eight different case-control diseases in the UK biobank data and find that SCT substantially improves prediction accuracy with an average AUC increase of 0.035 over standard C+T.

Microbial mediation of salinity stress response varies by plant genotype and provenance over time.

Although it is becoming widely appreciated that microbes can enhance plant tolerance to environmental stress, the nature of microbial mediation of exposure responses is not well understood. We addressed this deficit by examining whether microbial mediation of plant responses to elevated salinity is contingent on the environment and factors intrinsic to the host. We evaluated the influence of contrasting environmental conditions relative to host genotype, provenance and evolution by conducting a common-garden experiment utilizing ancestral and descendant cohorts of Schoenoplectus americanus genotypes recovered from two 100+ year coastal marsh seed banks. We compared S. americanus productivity and trait variation as well as associated endophytic microbial communities according to plant genotype, provenance, and age cohort under high and low salinity stress with and without native soil inoculation. The magnitude and direction of microbial mediation of S. americanus responses to elevated salinity varied according to individual genotype, provenance, as well as temporal shifts in genotypic variation and G × E (gene by environment) interactions. Relationships differed between plant traits and the structure of endosphere communities. Our findings indicate that plant-microbe associations and microbial mediation of plant stress are not only context-dependent but also dynamic. Our results additionally suggest that evolution can shape the fate of marsh ecosystems by altering how microbes confer plant tolerance to pressures linked to global change.

External control arm analysis: an evaluation of propensity score approaches, G-computation, and doubly debiased machine learning.

BACKGROUND: An external control arm is a cohort of control patients that are collected from data external to a single-arm trial. To provide an unbiased estimation of efficacy, the clinical profiles of patients from single and external arms should be aligned, typically using propensity score approaches. There are alternative approaches to infer efficacy based on comparisons between outcomes of single-arm patients and machine-learning predictions of control patient outcomes. These methods include G-computation and Doubly Debiased Machine Learning (DDML) and their evaluation for External Control Arms (ECA) analysis is insufficient. METHODS: We consider both numerical simulations and a trial replication procedure to evaluate the different statistical approaches: propensity score matching, Inverse Probability of Treatment Weighting (IPTW), G-computation, and DDML. The replication study relies on five type 2 diabetes randomized clinical trials granted by the Yale University Open Data Access (YODA) project. From the pool of five trials, observational experiments are artificially built by replacing a control arm from one trial by an arm originating from another trial and containing similarly-treated patients. RESULTS: Among the different statistical approaches, numerical simulations show that DDML has the smallest bias followed by G-computation. In terms of mean squared error, G-computation usually minimizes mean squared error. Compared to other methods, DDML has varying Mean Squared Error performances that improves with increasing sample sizes. For hypothesis testing, all methods control type I error and DDML is the most conservative. G-computation is the best method in terms of statistical power, and DDML has comparable power at [Formula: see text] but inferior ones for smaller sample sizes. The replication procedure also indicates that G-computation minimizes mean squared error whereas DDML has intermediate performances in between G-computation and propensity score approaches. The confidence intervals of G-computation are the narrowest whereas confidence intervals obtained with DDML are the widest for small sample sizes, which confirms its conservative nature. CONCLUSIONS: For external control arm analyses, methods based on outcome prediction models can reduce estimation error and increase statistical power compared to propensity score approaches.

Performing Highly Efficient Genome Scans for Local Adaptation with R Package pcadapt Version 4.

R package pcadapt is a user-friendly R package for performing genome scans for local adaptation. Here, we present version 4 of pcadapt which substantially improves computational efficiency while providing similar results. This improvement is made possible by using a different format for storing genotypes and a different algorithm for computing principal components of the genotype matrix, which is the most computationally demanding step in method pcadapt. These changes are seamlessly integrated into the existing pcadapt package, and users will experience a large reduction in computation time (by a factor of 20-60 in our analyses) as compared with previous versions.

A Homozygous Ancestral SVA-Insertion-Mediated Deletion in WDR66 Induces Multiple Morphological Abnormalities of the Sperm Flagellum and Male Infertility.

Multiple morphological abnormalities of the sperm flagellum (MMAF) is a severe form of male infertility defined by the presence of a mosaic of anomalies, including short, bent, curled, thick, or absent flagella, resulting from a severe disorganization of the axoneme and of the peri-axonemal structures. Mutations in DNAH1, CFAP43, and CFAP44, three genes encoding axoneme-related proteins, have been described to account for approximately 30% of the MMAF cases reported so far. Here, we searched for pathological copy-number variants in whole-exome sequencing data from a cohort of 78 MMAF-affected subjects to identify additional genes associated with MMAF. In 7 of 78 affected individuals, we identified a homozygous deletion that removes the two penultimate exons of WDR66 (also named CFAP251), a gene coding for an axonemal protein preferentially localized in the testis and described to localize to the calmodulin- and spoke-associated complex at the base of radial spoke 3. Sequence analysis of the breakpoint region revealed in all deleted subjects the presence of a single chimeric SVA (SINE-VNTR-Alu) at the breakpoint site, suggesting that the initial deletion event was potentially mediated by an SVA insertion-recombination mechanism. Study of Trypanosoma WDR66's ortholog (TbWDR66) highlighted high sequence and structural analogy with the human protein and confirmed axonemal localization of the protein. Reproduction of the human deletion in TbWDR66 impaired flagellar movement, thus confirming WDR66 as a gene associated with the MMAF phenotype and highlighting the importance of the WDR66 C-terminal region.

Efficient toolkit implementing best practices for principal component analysis of population genetic data.

MOTIVATION: Principal component analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (i) capturing linkage disequilibrium (LD) structure instead of population structure, (ii) projected PCs that suffer from shrinkage bias, (iii) detecting sample outliers and (iv) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and user-friendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr. RESULTS: For example, we find that PC19-PC40 in the UK Biobank capture complex LD structure rather than population structure. Using our automatic algorithm for removing long-range LD regions, we recover 16 PCs that capture population structure only. Therefore, we recommend using only 16-18 PCs from the UK Biobank to account for population structure confounding. We also show how to use PCA to restrict analyses to individuals of homogeneous ancestry. Finally, when projecting individual genotypes onto the PCA computed from the 1000 Genomes project data, we find a shrinkage bias that becomes large for PC5 and beyond. We then demonstrate how to obtain unbiased projections efficiently using bigsnpr. Overall, we believe this work would be of interest for anyone using PCA in their analyses of genetic data, as well as for other omics data. AVAILABILITY AND IMPLEMENTATION: R packages bigsnpr and bigutilsr can be installed from either CRAN or GitHub (see https://github.com/privefl/bigsnpr). A tutorial on the steps to perform PCA on 1000G data is available at https://privefl.github.io/bigsnpr/articles/bedpca.html. All code used for this paper is available at https://github.com/privefl/paper4-bedpca/tree/master/code. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Amplification of pathogenic Leptospira infection with greater abundance and co-occurrence of rodent hosts across a counter-urbanizing landscape.

Land use change can elevate disease risk by creating conditions beneficial to species that carry zoonotic pathogens. Observations of concordant global trends in increased pathogen prevalence or disease incidence and landscape change have generated concerns that urbanization could increase transmission risk of some pathogens. Yet host-pathogen relationships underlying transmission risk have not been well characterized within cities, even where contact between humans and species capable of transmitting pathogens of concern occurs. We addressed this deficit by testing the hypothesis that areas in cities experiencing greater population loss and infrastructure decline (i.e., counter-urbanization) can support a greater diversity of host species and a larger and more diverse pool of pathogens. We did so by characterizing pathogenic Leptospira infection relative to rodent host richness and abundance across a mosaic of abandonment in post-Katrina New Orleans (Louisiana, USA). We found that Leptospira infection loads were highest in areas that harboured increased rodent species richness (which ranged from one to four rodent species detected). Areas with greater host co-occurrence also harboured a greater abundance of hosts, including the host species most likely to carry high infection loads, indicating that Leptospira infection can be amplified by increases in overall and relative host abundance. Evidence of shared infection among rodent host species indicates that cross-species transmission of Leptospira probably increases infection at sites with greater host richness. Additionally, evidence that rodent co-occurrence and abundance and Leptospira infection load parallel abandonment suggests that counter-urbanization can elevate zoonotic disease risk within cities, particularly in underserved communities that are burdened with disproportionate concentrations of derelict properties.

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software.

BACKGROUND: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. RESULTS: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. CONCLUSION: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

Rhizosphere microbial communities reflect genotypic and trait variation in a salt marsh ecosystem engineer.

PREMISE: There is growing recognition that intraspecific genetic variation in plants can influence associated soil microbial communities, but the functional bridges linking plant genotype with microbial community structure are not well understood. This deficit is due in part to a prevailing focus on characterizing relationships between microbial communities and functional trait variation among plant species or across plant communities, rather than within a single species. METHODS: We examined whether and how spatiotemporal variation in salt marsh rhizosphere microbial communities reflect plant provenance (genotypic variation) and associated trait variation within an ecosystem engineer, Spartina alterniflora. We planted S. alterniflora from four genetically distinct source populations in replicate sets of experimental plots across a shoreline in southeastern Louisiana, USA. After 2 years, we measured functional plant traits and profiled microbial communities. RESULTS: Bacterial and fungal α-diversity and richness were significantly higher in winter than in summer and corresponded to plant trait variation associated with provenance. Notably, 20% of the variation in fungal community composition was explained by trait differences while bacterial community structure did not reflect plant provenance or trait variation. However, evidence was found suggesting that bacterial communities are indirectly shaped by the influence of plant provenance on soil physicochemical properties. CONCLUSIONS: This study illustrates that intraspecific genetic and corresponding trait variation in an ecosystem engineer can shape rhizosphere microbial communities, with fungal communities being more responsive than bacteria to the influence of plant provenance and associated trait variation. Our results highlight the potential relevance of plant intraspecific variation in plant-microbe-soil feedbacks shaping naturally depauperate ecosystems like salt marshes.

Real-world evidence to support regulatory decision-making for medicines: Considerations for external control arms.

Randomized clinical trials (RCTs) are the gold standard in producing clinical evidence of efficacy and safety of medical interventions. More recently, a new paradigm is emerging-specifically within the context of preauthorization regulatory decision-making-for some novel uses of real-world evidence (RWE) from a variety of real-world data (RWD) sources to answer certain clinical questions. Traditionally reserved for rare diseases and other special circumstances, external controls (eg, historical controls) are recognized as a possible type of control arm for single-arm trials. However, creating and analyzing an external control arm using RWD can be challenging since design and analytics may not fully control for all systematic differences (biases). Nonetheless, certain biases can be attenuated using appropriate design and analytical approaches. The main objective of this paper is to improve the scientific rigor in the generation of external control arms using RWD. Here we (a) discuss the rationale and regulatory circumstances appropriate for external control arms, (b) define different types of external control arms, and (c) describe study design elements and approaches to mitigate certain biases in external control arms. This manuscript received endorsement from the International Society for Pharmacoepidemiology (ISPE).

Understanding utilization patterns of biologics and biosimilars in the United States to support postmarketing studies of safety and effectiveness.

PURPOSE: To describe utilization of filgrastim and infliximab, the first two products with biosimilars approved in the United States. METHODS: We identified use of filgrastim (reference, tbo-filgrastim, and filgrastim-sndz) and infliximab (reference, infliximab-dyyb, and infliximab-abda) in the Sentinel Distributed Database using Healthcare Common Procedure Coding System (HCPCS) codes and National Drug Codes (NDCs) from January 2015 to August 2018. We calculated the proportion of use by code type and assessed uptake over time. We compared baseline patient characteristics and treatment indications. Among patients with >1 exposure episode, we characterized gaps between episodes. RESULTS: Use was identified primarily via HCPCS codes (filgrastim: 86.4%-97.7%; infliximab: 87.8%-100%) although some was identified via NDCs (filgrastim: 2.2%-13.5%; infliximab: <0.1%-6.5%). Filgrastim reference product use declined from 89.4% in January 2015 to 30.3% in June 2018, with corresponding increases in filgrastim-sndz (0% to 49.3%) and tbo-filgrastim (10.6% to 20.4%). Infliximab biosimilar uptake was low (9.7% in June 2018). We identified 94 846 filgrastim reference product, 27 143 tbo-filgrastim, and 38 264 filgrastim-sndz users. For infliximab, we identified 125 412 reference product, 1034 infliximab-dyyb, 49 infliximab-abda, and 4855 undetermined biosimilar users. Patients receiving filgrastim products were largely similar, but differences in age, sex, and indication were observed across infliximab product users. The median exposure episode gap ranged from 1 to 3 days for filgrastim and 48 to 50 days for infliximab. CONCLUSION: Use of biosimilar filgrastim has increased in the United States, but infliximab biosimilar use remains low. Data on identification of biosimilars in claims data and observed gaps between exposure episodes can be used to support drug safety studies of biosimilars.

Migratory flexibility in native Hawai'ian amphidromous fishes.

We assessed the prevalence of life history variation across four of the five native amphidromous Hawai'ian gobioids to determine whether some or all exhibit evidence of partial migration. Analysis of otolith Sr.: Ca concentrations affirmed that all are amphidromous and revealed evidence of partial migration in three of the four species. We found that 25% of Lentipes concolor (n = 8), 40% of Eleotris sandwicensis (n = 20) and 29% of Stenogobius hawaiiensis (n = 24) did not exhibit a migratory life-history. In contrast, all individuals of Sicyopterus stimpsoni (n = 55) included in the study went to sea as larvae. Lentipes concolor exhibited the shortest mean larval duration (LD) at 87 days, successively followed by E. sandwicensis (mean LD = 102 days), S. hawaiiensis (mean LD = 114 days) and S. stimpsoni (mean LD = 120 days). These findings offer a fresh perspective on migratory life histories that can help improve efforts to conserve and protect all of these and other at-risk amphidromous species that are subject to escalating anthropogenic pressures in both freshwater and marine environments.

Evidence of local adaptation in a waterfall-climbing Hawaiian goby fish derived from coupled biophysical modeling of larval dispersal and post-settlement selection.

BACKGROUND: Local adaptation of marine and diadromous species is thought to be a product of larval dispersal, settlement mortality, and differential reproductive success, particularly in heterogeneous post-settlement habitats. We evaluated this premise with an oceanographic passive larval dispersal model coupled with individual-based models of post-settlement selection and reproduction to infer conditions that underlie local adaptation in Sicyopterus stimpsoni, an amphidromous Hawaiian goby known for its ability to climb waterfalls. RESULTS: Our model results demonstrated that larval dispersal is spatio-temporally asymmetric, with more larvae dispersed from the southeast (the Big Island) to northwest (Kaua'i) along the archipelago, reflecting prevailing conditions such as El Niño/La Niña oscillations. Yet connectivity is nonetheless sufficient to result in homogenous populations across the archipelago. We also found, however, that ontogenetic shifts in habitat can give rise to adaptive morphological divergence when the strength of predation-driven post-settlement selection crosses a critical threshold. Notably, our simulations showed that larval dispersal is not the only factor determining the likelihood of morphological divergence. We found adaptive potential and evolutionary trajectories of S. stimpsoni were greater on islands with stronger environmental gradients and greater variance in larval cohort morphology due to fluctuating immigration. CONCLUSIONS: Contrary to expectation, these findings indicate that immigration can act in concert with selection to favor local adaptation and divergence in species with marine larval dispersal. Further development of model simulations, parameterized to reflect additional empirical estimates of abiotic and biotic factors, will help advance our understanding of the proximate and ultimate mechanisms driving adaptive evolution, population resilience, and speciation in marine-associated species.

Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species.

Admixture between populations provides opportunity to study biological adaptation and phenotypic variation. Admixture studies rely on local ancestry inference for admixed individuals, which consists of computing at each locus the number of copies that originate from ancestral source populations. Existing software packages for local ancestry inference are tuned to provide accurate results on human data and recent admixture events. Here, we introduce Loter, an open-source software package that does not require any biological parameter besides haplotype data in order to make local ancestry inference available for a wide range of species. Using simulations, we compare the performance of Loter to HAPMIX, LAMP-LD, and RFMix. HAPMIX is the only software severely impacted by imperfect haplotype reconstruction. Loter is the less impacted software by increasing admixture time when considering simulated and admixed human genotypes. For simulations of admixed Populus genotypes, Loter and LAMP-LD are robust to increasing admixture times by contrast to RFMix. When comparing length of reconstructed and true ancestry tracts, Loter and LAMP-LD provide results whose accuracy is again more robust than RFMix to increasing admixture times. We apply Loter to individuals resulting from admixture between Populus trichocarpa and Populus balsamifera and lengths of ancestry tracts indicate that admixture took place ∼100 generations ago. We expect that providing a rapid and parameter-free software for local ancestry inference will make more accessible genomic studies about admixture processes.

Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr.

Motivation: Genome-wide datasets produced for association studies have dramatically increased in size over the past few years, with modern datasets commonly including millions of variants measured in dozens of thousands of individuals. This increase in data size is a major challenge severely slowing down genomic analyses, leading to some software becoming obsolete and researchers having limited access to diverse analysis tools. Results: Here we present two R packages, bigstatsr and bigsnpr, allowing for the analysis of large scale genomic data to be performed within R. To address large data size, the packages use memory-mapping for accessing data matrices stored on disk instead of in RAM. To perform data pre-processing and data analysis, the packages integrate most of the tools that are commonly used, either through transparent system calls to existing software, or through updated or improved implementation of existing methods. In particular, the packages implement fast and accurate computations of principal component analysis and association studies, functions to remove single nucleotide polymorphisms in linkage disequilibrium and algorithms to learn polygenic risk scores on millions of single nucleotide polymorphisms. We illustrate applications of the two R packages by analyzing a case-control genomic dataset for celiac disease, performing an association study and computing polygenic risk scores. Finally, we demonstrate the scalability of the R packages by analyzing a simulated genome-wide dataset including 500 000 individuals and 1 million markers on a single desktop computer. Availability and implementation: https://privefl.github.io/bigstatsr/ and https://privefl.github.io/bigsnpr/. Supplementary information: Supplementary data are available at Bioinformatics online.

Unravelling the invasion history of the Asian tiger mosquito in Europe.

Multiple introductions are key features for the establishment and persistence of introduced species. However, little is known about the contribution of genetic admixture to the invasive potential of populations. To address this issue, we studied the recent invasion of the Asian tiger mosquito (Aedes albopictus) in Europe. Combining genome-wide single nucleotide polymorphisms and historical knowledge using an approximate Bayesian computation framework, we reconstruct the colonization routes and establish the demographic dynamics of invasion. The colonization of Europe involved at least three independent introductions in Albania, North Italy and Central Italy that subsequently acted as dispersal centres throughout Europe. We show that the topology of human transportation networks shaped demographic histories with North Italy and Central Italy being the main dispersal centres in Europe. Introduction modalities conditioned the levels of genetic diversity in invading populations, and genetically diverse and admixed populations promoted more secondary introductions and have spread farther than single-source invasions. This genomic study provides further crucial insights into a general understanding of the role of genetic diversity promoted by modern trade in driving biological invasions.

Estimating effective population size for a cestode parasite infecting three-spined sticklebacks.

Remarkably few attempts have been made to estimate contemporary effective population size (Ne) for parasitic species, despite the valuable perspectives it can offer on the tempo and pace of parasite evolution as well as coevolutionary dynamics of host-parasite interactions. In this study, we utilized multi-locus microsatellite data to derive single-sample and temporal estimates of contemporary Ne for a cestode parasite (Schistocephalus solidus) as well as three-spined stickleback hosts (Gasterosteus aculeatus) in lakes across Alaska. Consistent with prior studies, both approaches recovered small and highly variable estimates of parasite and host Ne. We also found that estimates of host Ne and parasite Ne were sensitive to assumptions about population genetic structure and connectivity. And, while prior work on the stickleback-cestode system indicates that physiographic factors external to stickleback hosts largely govern genetic variation in S. solidus, our findings indicate that stickleback host attributes and factors internal to the host - namely body length, genetic diversity and infection - shape contemporary Ne of cestode parasites.