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The fraction of cases reported, known as ‘reporting’, is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018–2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5–10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities.

Background Nontyphoidal Salmonella (NTS) are associated with both diarrhea and bacteremia. Antimicrobial resistance (AMR) is common in NTS in low-middle income countries, but the major source(s) of AMR NTS in humans are not known. Here, we aimed to assess the role of animals as a source of AMR in human NTS infections in Vietnam. We retrospectively combined and analyzed 672 NTS human and animal isolates from four studies in southern Vietnam and compared serovars, sequence types (ST), and AMR profiles. We generated a population structure of circulating organisms and aimed to attribute sources of AMR in NTS causing invasive and noninvasive disease in humans using Bayesian multinomial mixture models. Results Among 672 NTS isolates, 148 (22%) originated from human blood, 211 (31%) from human stool, and 313 (47%) from animal stool. The distribution of serovars, STs, and AMR profiles differed among sources; serovars Enteritidis, Typhimurium, and Weltevreden were the most common in human blood, human stool, and animals, respectively. We identified an association between the source of NTS and AMR profile; the majority of AMR isolates were isolated from human blood (p, Highlights • The major source(s) of antimicrobial resistance (AMR) in nontyphoidal Salmonella (NTS) are unknown. • We analyzed 672 NTS isolates, 148 (41%) from human blood, 211 (59%) from human stool, and 136 (43%) from animal stool. • Enteritidis, Typhimurium, and Weltevreden were the common serovars in human blood, human stool, and animals, respectively. • The majority of AMR isolates were associated with human blood. • NTS from animals does explain the full extent of AMR in human NTS infections.

Characterizing genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata are not always easily integrated into these methods in a user-friendly fashion. Here, we present a deployable Python-based web-tool, mvmapper, for visualizing and exploring results of multivariate analyses in geographic space. This tool can be used to map results of virtually any multivariate analysis of georeferenced data, and routines for exporting results from a number of standard methods have been integrated in the R package adegenet, including principal components analysis (PCA), spatial PCA, discriminant analysis of principal components, principal coordinates analysis, nonmetric dimensional scaling and correspondence analysis. mvmapper's greatest strength is facilitating dynamic and interactive exploration of the statistical and geographic frameworks side by side, a task that is difficult and time-consuming with currently available tools. Source code and deployment instructions, as well as a link to a hosted instance of mvmapper, can be found at https://popphylotools.github.io/mvMapper/.

Azole drug resistance in the human-pathogenic fungus Aspergillus fumigatus continues to emerge, potentially leading to untreatable aspergillosis in immunosuppressed hosts. Two dominant, environmentally associated resistance mechanisms, which are thought to have evolved through selection by the agricultural application of azole fungicides, are now distributed globally. Understanding the effect that azole resistance is having on the genetic diversity and global population of A. fumigatus will help mitigate drug-resistant aspergillosis and maintain the azole class of fungicides for future use in both medicine and crop protection., The emergence of azole resistance in the pathogenic fungus Aspergillus fumigatus has continued to increase, with the dominant resistance mechanisms, consisting of a 34-nucleotide tandem repeat (TR34)/L98H and TR46/Y121F/T289A, now showing a structured global distribution. Using hierarchical clustering and multivariate analysis of 4,049 A. fumigatus isolates collected worldwide and genotyped at nine microsatellite loci using analysis of short tandem repeats of A. fumigatus (STRAf), we show that A. fumigatus can be subdivided into two broad clades and that cyp51A alleles TR34/L98H and TR46/Y121F/T289A are unevenly distributed across these two populations. Diversity indices show that azole-resistant isolates are genetically depauperate compared to their wild-type counterparts, compatible with selective sweeps accompanying the selection of beneficial mutations. Strikingly, we found that azole-resistant clones with identical microsatellite profiles were globally distributed and sourced from both clinical and environmental locations, confirming that azole resistance is an international public health concern. Our work provides a framework for the analysis of A. fumigatus isolates based on their microsatellite profile, which we have incorporated into a freely available, user-friendly R Shiny application (AfumID) that provides clinicians and researchers with a method for the fast, automated characterization of A. fumigatus genetic relatedness. Our study highlights the effect that azole drug resistance is having on the genetic diversity of A. fumigatus and emphasizes its global importance upon this medically important pathogenic fungus.

There exists significant interest in developing statistical and computational tools for inferring 'who infected whom' in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains.

Antarctic shallow-water invertebrates are exceptional candidates to study population genetics and evolution, because of their peculiar evolutionary history and adaptation to extreme habitats that expand and retreat with the ice sheets. Among them, sponges are one of the major components, yet population connectivity of none of their many Antarctic species has been studied. To investigate gene flow, local adaptation and resilience to near-future changes caused by global warming, we sequenced 62 individuals of the sponge Dendrilla antarctica along the Western Antarctic Peninsula (WAP) and the South Shetlands (spanning ~900 km). We obtained information from 577 double digest restriction site-associated DNA sequencing (ddRADseq)-derived single nucleotide polymorphism (SNP), using RADseq techniques for the first time with shallow-water sponges. In contrast to other studies in sponges, our 389 neutral SNPs data set showed high levels of gene flow, with a subtle substructure driven by the circulation system of the studied area. However, the 140 outlier SNPs under positive selection showed signals of population differentiation, separating the central-southern WAP from the Bransfield Strait area, indicating a divergent selection process in the study area despite panmixia. Fourteen of these outliers were annotated, being mostly involved in immune and stress responses. We suggest that the main selective pressure on D. antarctica might be the difference in the planktonic communities present in the central-southern WAP compared to the Bransfield Strait area, ultimately depending on sea-ice control of phytoplankton blooms. Our study unveils an unexpectedly long-distance larval dispersal exceptional in Porifera, broadening the use of genome-wide markers within nonmodel Antarctic organisms.

Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data., Author summary The increasing availability of genetic sequence data has sparked an interest in using pathogen whole genome sequences to reconstruct the history of individual transmission events in an infectious disease outbreak. However, such methodologies rely on pathogen genomes mutating rapidly enough to discriminate between infected individuals, an assumption that remains to be investigated. To determine pathogen outbreaks for which genetic data is expected to be informative of transmission events, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating pathogen genome sequences sampled from transmission pairs. We characterise transmission divergence of ten major outbreak causing pathogens using simulations and find significant variation between diseases, with viral outbreaks generally exhibiting higher transmission divergence than bacterial ones. We reconstruct these outbreaks using the R-packages outbreaker and phybreak and find that genetic sequence data, though useful for rapidly evolving pathogens, provides little to no information about outbreaks with low transmission divergence, such as Streptococcus pneumoniae and Shigella sonnei. Our results demonstrate the need to incorporate other sources of outbreak data, such as contact tracing data and spatial location data, into outbreak reconstruction tools.

The increasing availability of large genomic data sets as well as the advent of Bayesian phylogenetics facilitates the investigation of phylogenetic incongruence, which can result in the impossibility of representing phylogenetic relationships using a single tree. While sometimes considered as a nuisance, phylogenetic incongruence can also reflect meaningful biological processes as well as relevant statistical uncertainty, both of which can yield valuable insights in evolutionary studies. We introduce a new tool for investigating phylogenetic incongruence through the exploration of phylogenetic tree landscapes. Our approach, implemented in the R package treespace, combines tree metrics and multivariate analysis to provide low‐dimensional representations of the topological variability in a set of trees, which can be used for identifying clusters of similar trees and group‐specific consensus phylogenies. treespace also provides a user‐friendly web interface for interactive data analysis and is integrated alongside existing standards for phylogenetics. It fills a gap in the current phylogenetics toolbox in R and will facilitate the investigation of phylogenetic results.

International audience; Towards an integrated ecosystem of R packages for the analysis of population genetic data

Genetic sequences of multiple genes are becoming increasingly common for a wide range of organisms including viruses, bacteria, and Eucaryotes. While such data may sometimes be treated as a single locus, in practice a number of biological and statistical phenomena can lead to phylogenetic incongruence. In such cases different loci should, at least as a preliminary step, be examined and analysed separately. The R software has become a popular platform for phylogenetics, with several packages implementing distance-based, parsimony, and likelihood-based phylogenetic reconstruction, and an even greater number of packages implementing phylogenetic comparative methods. Unfortunately, basic data structures and tools for analysing multiple genes have so far been lacking, thereby limiting potential for investigating phylogenetic incongruence. In this paper, we introduce the new R package apex to fill this gap. apex implements new object classes which extend existing standards for storing DNA and amino-acid sequences, and provides a number of convenient tools for handling, visualizing, and analyzing these data. In this paper, we introduce the main features of the package and illustrate its functionalities through the analysis of a simple dataset. This article is protected by copyright. All rights reserved.

The authors acknowledge financial support from the UKCRC Translational Infection Research (TIR) Initiative and the Medical Research Council (Grant number G1000803), with contributions to the grant from the Biotechnology and Biological Sciences Research Council, the National Institute for Health Research on behalf of the Department of Health, and the Chief Scientist Office of the Scottish Government Health Directorate (to Professor Peacock); from Wellcome Trust grant number 098051 awarded to the Wellcome Trust Sanger Institute; and the NIHR Cambridge Biomedical Research Centre (to Professor Peacock). S.Y.C.T. is an Australian National Health and Medical Research Council Career Development Fellow (1065736). Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of nosocomial infection. Whole-genome sequencing of MRSA has been used to define phylogeny and transmission in well-resourced healthcare settings, yet the greatest burden of nosocomial infection occurs in resource-restricted settings where barriers to transmission are lower. Here, we study the flux and genetic diversity of MRSA on ward and individual patient levels in a hospital where transmission was common. We repeatedly screened all patients on two intensive care units for MRSA carriage over a 3-mo period. All MRSA belonged to multilocus sequence type 239 (ST 239). We defined the population structure and charted the spread of MRSA by sequencing 79 isolates from 46 patients and five members of staff, including the first MRSA-positive screen isolates and up to two repeat isolates where available. Phylogenetic analysis identified a flux of distinct ST 239 clades over time in each intensive care unit. In total, five main clades were identified, which varied in the carriage of plasmids encoding antiseptic and antimicrobial resistance determinants. Sequence data confirmed intra- and interwards transmission events and identified individual patients who were colonized by more than one clade. One patient on each unit was the source of numerous transmission events, and deep sampling of one of these cases demonstrated colonization with a "cloud" of related MRSA variants. The application of whole-genome sequencing and analysis provides novel insights into the transmission of MRSA in under-resourced healthcare settings and has relevance to wider global health. Publisher PDF

International audience; Toxoplasma gondii, a protozoan found ubiquitously in mammals and birds, is the etiologic agent of toxoplasmosis, a disease causing substantial public health burden worldwide, including about 200,000 new cases of congenital toxoplasmosis each year. Clinical severity has been shown to vary across geographical regions, with South America exhibiting the highest burden. Unfortunately, the drivers of these heterogeneities are still poorly understood, and the geographical origin and historical spread of the pathogen worldwide are currently uncertain. A worldwide sample of 168 T. gondii isolates gathered in 13 populations was sequenced for five fragments of genes (140 single nucleotide polymorphisms from 3153bp per isolate). Phylogeny based on Maximum likelihood methods with estimation of the time to the most recent common ancestor (TMRCA) and geostatistical analyses were performed for inferring the putative origin of T. gondii. We show that extant strains of the pathogen likely evolved from a South American ancestor, around 1.5 million years ago, and reconstruct the subsequent spread of the pathogen worldwide. This emergence is much more recent than the appearance of ancestral T. gondii, believed to have taken place about 11 My ago, and follows the arrival of felids in this part of the world. We posit that an ancestral lineage of T. gondii likely arrived in South America with felids and that the evolution of oral infectivity through carnivorism and the radiation of felids in this region enabled a new strain to outcompete the ancestral lineage and undergo a pandemic radiation.

International audience; Population genetics and genomics have developed and been treated as independent fields of study despite having common roots. The continuous progress of sequencing technologies is contributing to (re-)connect these two disciplines. We review the challenges faced by data analysts and software developers when handling very big genetic data sets collected on many individuals. We then expose how R , as a computing language and development environment, proposes some solutions to meet these challenges. We focus on some specific issues that are often encountered in practice: handling and analysing single-nucleotide polymorphism data, handling and reading variant call format files, analysing haplotypes and linkage disequilibrium and performing multivariate analyses. We illustrate these implementations with some analyses of three recently published data sets that contain between 60,000 and 1,000,000 loci. We conclude with some perspectives on future developments of R software for population genomics.

Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new framework for combining several data streams, e.g. temporal, spatial and genetic data, to identify clusters of related cases of an infectious disease. Our method explicitly accounts for underreporting, and allows incorporating preexisting information about the disease, such as its serial interval, spatial kernel, and mutation rate. We define, for each data stream, a graph connecting all cases, with edges weighted by the corresponding pairwise distance between cases. Each graph is then pruned by removing distances greater than a given cutoff, defined based on preexisting information on the disease and assumptions on the reporting rate. The pruned graphs corresponding to different data streams are then merged by intersection to combine all data types; connected components define clusters of cases related for all types of data. Estimates of the reproduction number (the average number of secondary cases infected by an infectious individual in a large population), and the rate of importation of the disease into the population, are also derived. We test our approach on simulated data and illustrate it using data on dog rabies in Central African Republic. We show that the outbreak clusters identified using our method are consistent with structures previously identified by more complex, computationally intensive approaches., Author summary Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new approach which combines different types of data to identify clusters of related cases of an infectious disease. This approach relies on representing each type of data (e.g. temporal, spatial, or genetic) as a graph where nodes are cases, and two nodes are connected if the corresponding cases are closely related for this data. Our method then identifies clusters of cases which likely stem from the same introduction. Furthermore, we can use the size of these clusters to infer transmissibility of the disease and the number of importations of the pathogen into the population. We apply this approach to analyse dog rabies epidemics in Central African Republic. We show that outbreak clusters identified using our method are consistent with structures previously identified by more complex and computationally intensive approaches. Using simulated rabies epidemics, we show that our method has excellent potential for optimally detecting outbreak clusters. We also identify promising areas of research for transforming our method into a routine analysis tool for processing disease surveillance data.

There is an ongoing discussion in the literature on whether human mitochondrial DNA (mtDNA) evolves neutrally. There have been previous claims for natural selection on human mtDNA based on an excess of non-synonymous mutations and higher evolutionary persistence of specific mitochondrial mutations in Arctic populations. However, these findings were not supported by the reanalysis of larger datasets. Using a geographical framework, we perform the first direct test of the relative extent to which climate and past demography have shaped the current spatial distribution of mtDNA sequences worldwide. We show that populations living in colder environments have lower mitochondrial diversity and that the genetic differentiation between pairs of populations correlates with difference in temperature. These associations were unique to mtDNA; we could not find a similar pattern in any other genetic marker. We were able to identify two correlated non-synonymous point mutations in the ND3 and ATP6 genes characterized by a clear association with temperature, which appear to be plausible targets of natural selection producing the association with climate. The same mutations have been previously shown to be associated with variation in mitochondrial pH and calcium dynamics. Our results indicate that natural selection mediated by climate has contributed to shape the current distribution of mtDNA sequences in humans.

Increasing attention is being devoted to taking landscape information into account in genetic studies. Among landscape variables, space is often considered as one of the most important. To reveal spatial patterns, a statistical method should be spatially explicit, that is, it should directly take spatial information into account as a component of the adjusted model or of the optimized criterion. In this paper we propose a new spatially explicit multivariate method, spatial principal component analysis (sPCA), to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations. This analysis does not require data to meet Hardy-Weinberg expectations or linkage equilibrium to exist between loci. The sPCA yields scores summarizing both the genetic variability and the spatial structure among individuals (or populations). Global structures (patches, clines and intermediates) are disentangled from local ones (strong genetic differences between neighbors) and from random noise. Two statistical tests are proposed to detect the existence of both types of patterns. As an illustration, the results of principal component analysis (PCA) and sPCA are compared using simulated datasets and real georeferenced microsatellite data of Scandinavian brown bear individuals (Ursus arctos). sPCA performed better than PCA to reveal spatial genetic patterns. The proposed methodology is implemented in the adegenet package of the free software R.

Phenotypic variation in hybridizing species or subspecies is a prerequisite for allowing conservation ecologists and wildlife managers to identify parental populations and their hybrids in the field. We assessed the reliability of a set of eight morphological (body size and pelage characters) and four anatomical criteria (skull and intestine morphometric measurements) to distinguish between 302 French specimens classified as wildcat, domestic cat or hybrid on the basis of a Bayesian analysis (STRUCTURE) of their multilocus microsatellite genotypes. This aim was achieved by performing a set of multivariate analyses on morphological, anatomical and genetic data sets (Hill and Smith's analysis, co-inertia analysis and discriminant analysis of principal components). Wildcats and domestic cats were very satisfactorily distinguished, even when using simple non-invasive morphological criteria easily usable in the field like the morphology of the tail, dorsal line or flank stripes. Using anatomical instead of morphological characters slightly increased the discriminating power. Many more difficulties arose when we tried to distinguish hybrid specimens from both wildcat and domestic ones. Anatomical characters performed better than morphological ones in recognizing hybrids, but the assignment success rate remained very low, about 31.6% and 1.5%, respectively. Overall, the most discriminating characters were two continuous, derived anatomical characters: the cranial index followed by the intestinal index. Classification of specimens in three classes based on their microsatellite genotypes appeared to be inadequate for identifying hybrid specimens, as hybrid specimens seemed to be distributed along an anatomical continuum. With this observation in mind, we assessed the linear relationships between a proxy of the individual level of hybridization (qik) and the cranial and intestinal indices, respectively. Both relationships were highly significant. The greatest correlation was found with the cranial index (R² = 60.4%). Altogether, our results suggest that future work should be geared towards enhancing the measure of hybridization using more discriminating molecular markers and improving morphometric skull measurements through the use of modern geometric morphometric methods, using landmarks rather than skull dimension.

Recent years have seen progress in the development of statistically rigorous frameworks to infer outbreak transmission trees (“who infected whom”) from epidemiological and genetic data. Making use of pathogen genome sequences in such analyses remains a challenge, however, with a variety of heuristic approaches having been explored to date. We introduce a statistical method exploiting both pathogen sequences and collection dates to unravel the dynamics of densely sampled outbreaks. Our approach identifies likely transmission events and infers dates of infections, unobserved cases and separate introductions of the disease. It also proves useful for inferring numbers of secondary infections and identifying heterogeneous infectivity and super-spreaders. After testing our approach using simulations, we illustrate the method with the analysis of the beginning of the 2003 Singaporean outbreak of Severe Acute Respiratory Syndrome (SARS), providing new insights into the early stage of this epidemic. Our approach is the first tool for disease outbreak reconstruction from genetic data widely available as free software, the R package outbreaker. It is applicable to various densely sampled epidemics, and improves previous approaches by detecting unobserved and imported cases, as well as allowing multiple introductions of the pathogen. Because of its generality, we believe this method will become a tool of choice for the analysis of densely sampled disease outbreaks, and will form a rigorous framework for subsequent methodological developments., Author Summary Understanding how infectious diseases are transmitted from one individual to another is essential for designing containment strategies and epidemic prevention. Recently, the reconstruction of transmission trees (“who infected whom”) has been revolutionized by the availability of pathogen genome sequences. Exploiting this information remains a challenge, however, with a variety of heuristic approaches having been explored to date. Here, we introduce a new method which uses both pathogen DNA and collection dates to gain insights into transmission events, and detect unobserved cases and separate introductions of the disease. Our approach is also useful for identifying super-spreaders, i.e., cases which caused many subsequent infections. After testing our method using simulations, we use it to gain new insights into the beginning of the 2003 Singaporean outbreak of Severe Acute Respiratory Syndrome (SARS). Our approach is applicable to a wide range of diseases and available in a free software package called outbreaker.

The geographical distribution of single nucleotide polymorphism (SNP) in the mitochondrial genome of the human malaria parasite Plasmodium falciparum was investigated. We identified 88 SNPs in 516 isolates from seven parasite populations in Africa, Southeast Asia and Oceania. Analysis of the SNPs postulated a sub-Saharan African origin and recovered a strong negative correlation between within-population SNP diversity and geographic distance from the putative African origin over Southeast Asia and Oceania. These results are consistent with those previously obtained for nuclear genome-encoded housekeeping genes, indicating that the pattern of inheritance does not substantially affect the geographical distribution of SNPs.

The Senegal River Basin (SRB) experienced a major epidemic of intestinal schistosomiasis in the early nineties, after the construction of a dam for irrigation purposes. Exceptionally low cure rates following praziquantel (PZQ) treatment at the onset of the epidemic raised concerns about PZQ resistant strains of Schistosoma mansoni, although they could also be attributed to the intense transmission at that time. A field study in the same region more than 15 years later found cure rates for S. mansoni still to be low, whereas Schistosoma haematobium responded well to treatment. We collected S. mansoni miracidia from children at base-line prior to treatment, six months after two PZQ treatments and two years after the start of the study when they had received a total of five PZQ treatments. In total, 434 miracidia from 12 children were successfully genotyped with at least six out of nine DNA microsatellite loci. We found no significant differences in the genetic diversity of, and genetic differentiation between parasite populations before and after repeated treatment, suggesting that PZQ treatment does not have an impact on the neutral evolution of the parasite. This is in stark contrast with a similar study in Tanzania where a significant decrease in genetic diversity was observed in S. mansoni miracidia after a single round of PZQ treatment. We argue that PZQ resistance might play a role in our study area, although rapid re-infection cannot be excluded. It is important to monitor this situation carefully and conduct larger field studies with short-term follow-up after treatment. Since PZQ is the only general schistosomicide available, the possibility of PZQ resistance is of great concern both for disease control and for curative use in clinical practice. © 2013 Elsevier B.V.

The genetic diversity of Yersinia pestis , the etiologic agent of plague, is extremely limited because of its recent origin coupled with a slow clock rate. Here we identified 2,326 SNPs from 133 genomes of Y. pestis strains that were isolated in China and elsewhere. These SNPs define the genealogy of Y. pestis since its most recent common ancestor. All but 28 of these SNPs represented mutations that happened only once within the genealogy, and they were distributed essentially at random among individual genes. Only seven genes contained a significant excess of nonsynonymous SNP, suggesting that the fixation of SNPs mainly arises via neutral processes, such as genetic drift, rather than Darwinian selection. However, the rate of fixation varies dramatically over the genealogy: the number of SNPs accumulated by different lineages was highly variable and the genealogy contains multiple polytomies, one of which resulted in four branches near the time of the Black Death. We suggest that demographic changes can affect the speed of evolution in epidemic pathogens even in the absence of natural selection, and hypothesize that neutral SNPs are fixed rapidly during intermittent epidemics and outbreaks.

Populations of Plasmodium falciparum , the most virulent human malaria parasite, are diverse owing to wide levels of transmission and endemicity of infection. Genetic diversity of P. falciparum antigens, within and between parasite populations, remains a confounding factor in malaria pathogenesis as well as clinical trials of vaccine candidates. Variation of target antigens in parasite populations may arise from immune pressure depending on the levels of acquired immunity. Alternatively, similar to our study in housekeeping genes [Tanabe et al. Curr Biol 2010;70:1–7], within-population genetic diversity of vaccine candidate antigens may also be determined by geographical distance from a postulated origin in Central sub-Saharan Africa. To address this question, we obtained full-length sequences of P. falciparum genes, apical membrane antigen 1 ( ama1 ) ( n = 459), circumsporozoite protein ( csp ) ( n = 472) and merozoite surface protein 1 ( msp1 ) ( n = 389) from seven geographically diverse parasite populations in Africa, Southeast Asia and Oceania; and, together with previously determined sequences ( n = 13 and 15 for csp and msp1 , respectively) analyzed within-population single nucleotide polymorphism (SNP) diversity. The three antigen genes showed SNP diversity that supports a model of isolation-by-distance. The standardized number of polymorphic sites per site, expressed as θ S , indicates that 77–83% can be attributed by geographic distance from the African origin, suggesting that geographic distance plays a significant role in variation in target vaccine candidate antigens. Furthermore, we observed that a large proportion of SNPs in the antigen genes were shared between African and non-African parasite populations, demonstrating long term persistence of those SNPs. Our results provide important implications for developing effective malaria vaccines and better understanding of acquired immunity against falciparum malaria.

Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. In particular, genetic data coupled with dates and locations of sampled isolates can be used to reconstruct the spatiotemporal dynamics of pathogens during outbreaks. Thus far, phylogenetic methods have been used to tackle this issue. Although these approaches have proved useful for informing on the spread of pathogens, they do not aim at directly reconstructing the underlying transmission tree. Instead, phylogenetic models infer most recent common ancestors between pairs of isolates, which can be inadequate for densely sampled recent outbreaks, where the sample includes ancestral and descendent isolates. In this paper, we introduce a novel method based on a graph approach to reconstruct transmission trees directly from genetic data. Using simulated data, we show that our approach can efficiently reconstruct genealogies of isolates in situations where classical phylogenetic approaches fail to do so. We then illustrate our method by analyzing data from the early stages of the swine-origin A/H1N1 influenza pandemic. Using 433 isolates sequenced at both the hemagglutinin and neuraminidase genes, we reconstruct the likely history of the worldwide spread of this new influenza strain. The presented methodology opens new perspectives for the analysis of genetic data in the context of disease outbreaks.

Due to the lack of fossil evidence, the timescales of bacterial evolution are largely unknown. The speed with which genetic change accumulates in populations of pathogenic bacteria, however, is a key parameter that is crucial for understanding the emergence of traits such as increased virulence or antibiotic resistance, together with the forces driving pathogen spread. Methicillin-resistant Staphylococcus aureus (MRSA) is a common cause of hospital-acquired infections. We have investigated an MRSA strain (ST225) that is highly prevalent in hospitals in Central Europe. By using mutation discovery at 269 genetic loci (118,804 basepairs) within an international isolate collection, we ascertained extremely low diversity among European ST225 isolates, indicating that a recent population bottleneck had preceded the expansion of this clone. In contrast, US isolates were more divergent, suggesting they represent the ancestral population. While diversity was low, however, our results demonstrate that the short-term evolutionary rate in this natural population of MRSA resulted in the accumulation of measurable DNA sequence variation within two decades, which we could exploit to reconstruct its recent demographic history and the spatiotemporal dynamics of spread. By applying Bayesian coalescent methods on DNA sequences serially sampled through time, we estimated that ST225 had diverged since approximately 1990 (1987 to 1994), and that expansion of the European clade began in 1995 (1991 to 1999), several years before the new clone was recognized. Demographic analysis based on DNA sequence variation indicated a sharp increase of bacterial population size from 2001 to 2004, which is concordant with the reported prevalence of this strain in several European countries. A detailed ancestry-based reconstruction of the spatiotemporal dispersal dynamics suggested a pattern of frequent transmission of the ST225 clone among hospitals within Central Europe. In addition, comparative genomics indicated complex bacteriophage dynamics., Author Summary Because fossils of bacteria do not exist or are morphologically indeterminate, the timescales of bacterial evolution are widely unknown. We have investigated the short-term evolution of a particular strain of methicillin-resistant Staphylococcus aureus (MRSA), a notorious cause of hospital-associated infections. By comparing 118 kilobases of DNA from MRSA isolates that had been collected at different points in time, we demonstrate that this strain has accumulated measurable DNA sequence variation within two decades. Further, we exploited this sequence diversity to estimate the short-term evolutionary rate and to date divergence times without paleontological calibration, and to reconstruct the recent demographic expansion and spatial spread of this MRSA.

International audience; Plague is a pandemic human invasive disease caused by the bacterial agent Yersinia pestis. We here report a comparison of 17 whole genomes of Y. pestis isolates from global sources. We also screened a global collection of 286 Y. pestis isolates for 933 SNPs using Sequenom MassArray SNP typing. We conducted phylogenetic analyses on this sequence variation dataset, assigned isolates to populations based on maximum parsimony and, from these results, made inferences regarding historical transmission routes. Our phylogenetic analysis suggests that Y. pestis evolved in or near China and spread through multiple radiations to Europe, South America, Africa and Southeast Asia, leading to country-specific lineages that can be traced by lineage-specific SNPs. All 626 current isolates from the United States reflect one radiation, and 82 isolates from Madagascar represent a second radiation. Subsequent local microevolution of Y. pestis is marked by sequential, geographically specific SNPs.

Multivariate analyses such as principal component analysis were among the first statistical methods employed to extract information from genetic markers. From their early applications to current innovations, these approaches have proven to be efficient for the analysis of the genetic variability in various contexts such as human genetics, conservation and adaptation studies. However, because multivariate analysis is a wide and diversified area of statistics, choosing a method appropriate to both the data and to the question being asked can be difficult. Moreover, some particularities of genetic markers need to be taken into account when using multivariate methods. As a consequence, multivariate analyses are often used as black boxes, which results in frequent mistakes in the literature. In this review, we provide a critical analysis of the application of multivariate methods to genetic markers, using a general framework that unifies all these methods for the sake of clarity. First, we focus on some common mistakes in these applications and ways to avoid these pitfalls. We then detail the most critical particularities of allele frequencies that demand adaptations of multivariate methods, and we propose solutions to the subsequent problems. Finally, we tackle several questions of interest in which multivariate analysis has a great role to play, such as the study of the typological coherence of different genetic markers, or the investigation of spatial genetic patterns.

Working with weakly congruent markers means that consensus genetic structuring of populations requires methods explicitly devoted to this purpose. The method, which is presented here, belongs to the multivariate analyses. This method consists of different steps. First, single-marker analyses were performed using a version of principal component analysis, which is designed for allelic frequencies (%PCA). Drawing confidence ellipses around the population positions enhances %PCA plots. Second, a multiple co-inertia analysis (MCOA) was performed, which reveals the common features of single-marker analyses, builds a reference structure and makes it possible to compare single-marker structures with this reference through graphical tools. Finally, a typological value is provided for each marker. The typological value measures the efficiency of a marker to structure populations in the same way as other markers. In this study, we evaluate the interest and the efficiency of this method applied to a European and African bovine microsatellite data set. The typological value differs among markers, indicating that some markers are more efficient in displaying a consensus typology than others. Moreover, efficient markers in one collection of populations do not remain efficient in others. The number of markers used in a study is not a sufficient criterion to judge its reliability. "Quantity is not quality".

Background The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. Results We introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Conclusions Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.