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bioRxiv2018

Pathogens, parasites, and parasitoids of ants: a synthesis of parasite biodiversity and epidemiological traits

1.Ants are among the most ecologically successful organisms on Earth, with a global distribution and diverse nesting and foraging ecologies. Ants are also social organisms, living in crowded, dense colonies that can range up to millions of individuals. Understanding the ecological success of the ants requires understanding how they have mitigated one of the major costs of social living-infection by parasitic organisms. Additionally, the ecological diversity of ants suggests that they may themselves harbor a diverse, and largely unknown, assemblage of parasites. As a first step, we need to know the taxonomic and functional diversity of the parasitic organisms infecting ants. To that end, we provide a comprehensive review of the parasitic organisms infecting ants by collecting all extant records. We synthesize major patterns in parasite ecology by categorizing how parasites encounter their ant hosts, whether they require host death as a developmental necessity, and how they transmit to future hosts.\n\nWe report 1,415 records of parasitic organisms infecting ants, the majority of which come from order Diptera (34.8%), phylum Fungi (25.6%), and order Hymenoptera (25.1%). Most parasitic organisms infecting ants are parasitoids (89.6%), requiring the death of their host as developmental necessity and most initially encounter their hosts in the extranidal environment (68.6%). Importantly, though most parasitic organisms infecting ants only need a single host to complete their life cycle (89.2%), the vast majority need to leave the nest before transmission to the next ant host can occur (88.3%), precluding ant-to-ant transmission within the nest. With respect to the host, we only found records for 9 out of 17 extant ant sub-families, and for 82 out of the currently recognized 334 ant genera. Though there is likely bias in the records reported, both host and parasite ecological traits and evolutionary histories underlie the pattern of ant-parasite association reported here. This work provides a foundation for future work that will begin to untangle the ecological drivers of ant-parasite relationships and the evolutionary implications thereof.

ecology↗
bioRxiv2018

DETECTION AND MOLECULAR EPIDEMIOLOGY OF HUMAN BOCAVIRUS IN CHILDREN WITH ACUTE GASTROENTERITIS FROM BRAZIL

Human Bocavirus (HBoV) is a recently discovered virus and was first detected in the nasopharyngeal aspirate samples and after in stool samples, suggesting that HBoV may be a causative agent for human enteric infections. Due to absence of treatment options, there is a need to understand the disease-causing mechanism of these viruses. The aim of this was to demonstrate the prevalence of HBoV from children less than 10 years with acute gastroenteritis in Brazil, during November 2011 to November 2012. Stool samples from hospitalized children [≤] 10 years who presented symptoms of acute gastroenteritis were analyzed for the presence of HBoV DNA by nested-PCR. HBoV- positivity was detected in 24.0% (54/225) of samples. Two peaks of HBoV detection were observed, during November 2011 and July to September 2012. Co-infections between HBoV and rotavirus A were identified in 50.0% (27/54) of specimens. Phylogenetic analysis identified the presence of HBoV-1 (94.8%), HBoV-2 (2.6%) and HBoV-3 (2.6%) species, with only minor variations among them. Further investigations are necessary to improve the knowledge on the role of HBoV in gastrointestinal infections.

microbiology↗
bioRxiv2018

Genomic epidemiology of syphilis reveals independent emergence of macrolide resistance across multiple circulating lineages

Syphilis is a sexually transmitted infection caused by Treponema pallidum subspecies pallidum and may lead to severe complications. Recent years have seen striking increases in syphilis in many countries. Previous analyses have suggested one lineage of syphilis, SS14, may have expanded recently, indicating emergence of a single pandemic azithromycin-resistant cluster. We used direct sequencing of T. pallidum combined with phylogenomic analyses to show that both SS14- and Nichols-lineages are simultaneously circulating in clinically relevant populations in multiple countries. We correlate the appearance of genotypic macrolide resistance with multiple independently evolved SS14 sub-lineages and show that genotypically resistant and sensitive sub-lineages are spreading contemporaneously, incompatible with the notion that SS14-lineage expansion is driven purely by macrolide resistance. These findings inform our understanding of the current syphilis epidemic by demonstrating how macrolide resistance evolves in Treponema subspecies and provide a warning on broader issues of antimicrobial resistance.

genomics↗
bioRxiv2018

Molecular epidemiology of Colombian Histoplasma capsulatum isolates shows their polyphyletic behavior and point out raw chicken manure as one of the infections sources

The thermally dimorphic fungus Histoplasma capsulatum is the causative agent of histoplasmosis, which is the most prevalent endemic mycosis in America. The replacement of organic matter in agro-ecosystems is necessary in the tropics, and the use of organic fertilizers has increased. Cases and outbreaks due to the presence of the fungus in these components have been reported. In Colombia, chicken manure is the most common raw material in the organic fertilizers production. In this work, we reached the isolation of the fungus from a chicken manure. Then, we were able to compare genetically 3 environmental isolates with 42 Colombian human clinical isolates. The genetic comparison showed the environmental isolates grouping together with the clinical isolates. This result suggests chicken manure as one of the infection source with H. capsulatum. Also, the phylogenetic analysis using another H. capsulatum isolates from databases showed that the Colombian isolates widely distributed in the relation tree. This result pointed out the great genetic diversity among H. capsulatum Colombian population.

microbiology↗
bioRxiv2018

A Mathematical Model Relating Pitocin Use During Labor with Offspring Autism Development in Terms of Oxytocin Receptor Desensitization in the Fetal Brain

This paper develops a mathematical model describing the potential buildup of high oxytocin concentrations in the maternal circulation during labor in terms of continuous Pitocin infusion rate, half-life and maternal weight. Oxytocin override of the degradation of oxytocin by placental oxytocinase is introduced to model the potential transfer of oxytocin from the maternal circulation across the placenta into the fetal circulation, and from there into the brain of the fetus. The desensitization unit D equal to 1.8E6 (pg-min)/ml is employed to establish a desensitization threshold, and by extension; a down-regulation threshold as a function of oxytocin override concentration and continuous Pitocin infusion time, that could be a factor in the subsequent development of autism among offspring. Epidemiological studies by Duke University (S.G. Gregory, 2013), Yale University (O. Weisman, 2015) and Harvard University (A.S. Oberg, 2016) are discussed regarding Pitocin use and offspring autism development for an explanation of the weak correlations they identified. The findings of the Harvard epidemiological study are re-interpreted regarding Pitocin use, and its conclusion questioned. Further evaluations of the findings of these three epidemiological studies are called for to incorporate medical information on quantity of Pitocin used, continuous Pitocin infusion rate, length of labor and maternal weight to determine if a correlation can be established with offspring autism development above an empirically determined desensitization threshold for Pitocin use. Suggestions for research are discussed, including an alternative to continuous Pitocin infusion, pulsatile infusion of Pitocin during labor induction, that may mitigate possible offspring autism development.\n\nHIGHLIGHTSO_LIBuildup of oxytocin (OT) in the maternal circulation mathematically modeled.\nC_LIO_LIRelationship of OT half-life and OT concentration in maternal circulation identified.\nC_LIO_LIOTR Desensitization related to Pitocin infusion time and OT override concentration.\nC_LIO_LIWeak correlations for autism development in epidemiological studies explained.\nC_LIO_LIExamination called for of medical records of Pitocin use in epidemiological studies.\nC_LI\n\nOUTLINE OF PAPERIntroduction\n\nO_LIDevelopment and application of mathematical model - Sections 1 through 6, notably: Figures 1 through 4\nC_LIO_LIDiscussion of oxytocin receptor desensitization as it pertains to mathematical model - Sections 7 and 8\nC_LIO_LIInfluence of mathematical model on interpretation of epidemiological studies - Sections 9 and 10\nC_LIO_LIResearch considerations - Section 11, notably:Subsection 11.1 - Call for detailed epidemiological analysis of selected medical information indicated by mathematical model Subsection 11.2 - Review of pulsatile Pitocin infusion to mitigate possible offspring autism development indicated by mathematical model\nC_LI\n\nO_FIG O_LINKSMALLFIG WIDTH=145 HEIGHT=200 SRC=\"FIGDIR/small/446997_fig1.gif\" ALT=\"Figure 1\">\nView larger version (62K):\norg.highwire.dtl.DTLVardef@1704563org.highwire.dtl.DTLVardef@1db729aorg.highwire.dtl.DTLVardef@41035eorg.highwire.dtl.DTLVardef@442b1f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Percent Oxytocin Removal by Liver and Kidneys and Percent Oxytocin Remaining in Blood with One-Minute Circulation Time as a Function of Oxytocin Half-Life in the Blood.\n\nC_FIG

neuroscience↗
bioRxiv2017

Death of the assumption that "latent period" is fixed over the course of a plant disease epidemic

The latent period is a crucial life history trait, particularly for polycyclic plant diseases, because it determines how many complete monocycles could theoretically occur during an epidemic. Many empirical studies have focused on the variation of the latent period with pathogen or host genotype, or changes in response to environmental factors. The focus on these aspects is unsurprising, as these factors classically form the three parts of the epidemiological triangle. Experiments in controlled conditions are generally used to assess pathogenicity and host susceptibility, and also provide the opportunity to measure the distribution of latent periods in epidemiological systems. Once estimated for one or several pairs of host-pathogen genotypes, the mean value of this important trait is usually considered to be fixed and is often used \"as is\" in epidemiological models. We show here that the latent period can display non-negligible variability over the course of a disease epidemic, and that this variability has multiple sources, some of which have complex, antagonistic impacts. We develop arguments for four sources of variation that challenge the implicit assumption that the latent period remains constant: daily fluctuations in leaf temperature, nature of inoculum, host stage or age of host tissues, intra-population competition and selection for aggressiveness traits. We focus on the wheat fungal disease Septoria tritici blotch (Zymoseptoria tritici), making use of empirical datasets collected during the first authors own research projects and a targeted literature review. Such empirical epidemiological knowledge is new and potentially important for modelers. While some studies have demonstrated that the distribution of latent periods around the mean value has consequences for epidemiological dynamics, we show that it might also be important for epidemiological modelers to account for changes in this mean value during an annual epidemic. These results may be of critical importance for improving outbreak forecasting.

epidemiology↗
bioRxiv2017

Tuberculosis outbreak investigation using phylodynamic analysis

The fast evolution of pathogenic viruses has allowed for the development of phylodynamic approaches that extract information about the epidemiological characteristics of viral genomes. Thanks to advances in whole genome sequencing, they can be applied to slowly evolving bacterial pathogens like Mycobacterium tuberculosis.\n\nIn this study, we investigate the epidemiological dynamics underlying two M. tuberculosis outbreaks using phylodynamic methods. The first outbreak occurred in the Swiss city of Bern (1993-2012) and was caused by a drug-susceptible strain belonging to the phylogenetic M. tuberculosis Lineage 4. The second outbreak was caused by a multidrug-resistant (MDR) strain of Lineage 2, imported from the Wat Tham Krabok (WTK) refugee camp in Thailand into California.\n\nThere is little temporal signal in the Bern data set and moderate temporal signal in the WTK data set. We estimate an evolutionary rate of 0.0039 per single nucleotide polymorphism (SNP) per year for Bern and 0.0024 per SNP per year for WTK. Nevertheless, due to its high sampling proportion (90%) the Bern outbreak allows robust estimation of epidemiological parameters despite the poor temporal signal. Conversely, theres much uncertainty in the epidemiological estimates concerning the WTK outbreak, which has a small sampling proportion (9%). Our results suggest that both outbreaks peaked around 1990, although the Bernese outbreak was only detected in 1993, and the WTK outbreak around 2004. Furthermore, individuals were infected for a significantly longer period (around 9 years) in the WTK outbreak than in the Bern outbreak (4-5 years).\n\nOur work highlights both the limitations and opportunities of phylodynamic analysis of outbreaks involving slowly evolving pathogens: (i) estimation of the evolutionary rate is difficult on outbreak time scales and (ii) a high sampling proportion allows quantification of the age of the outbreak based on the sampling times, and thus allows for robust estimation of epidemiological parameters.

epidemiology↗
bioRxiv2017

Paternal-age-related de novo mutations and risk for five disorders

BackgroundThere are well-established epidemiologic associations between advanced paternal age and increased offspring risk for several psychiatric and developmental disorders. These associations are commonly attributed to age-related de novo mutations. However, the actual magnitude of risk conferred by age-related de novo mutations in the male germline is unknown. Quantifying this risk would clarify the clinical and public health significance of delayed paternity.\n\nMethodsUsing results from large, parent-child trio whole-exome-sequencing studies, we estimated the relationship between paternal-age-related de novo single nucleotide variants (dnSNVs) and offspring risk for five disorders: autism spectrum disorders (ASD), congenital heart disease (CHD), neurodevelopmental disorders with epilepsy (EPI), intellectual disability (ID), and schizophrenia (SCZ). Using Danish national registry data, we then investigated the degree to which the epidemiologic association between each disorder and advanced paternal age was consistent with the estimated role of de novo mutations.\n\nResultsIncidence rate ratios comparing dnSNV-based risk to offspring of 45 versus 25-year-old fathers ranged from 1.05 (95% confidence interval 1.01-1.13) for SCZ to 1.29 (95% CI 1.13-1.68) for ID. Epidemiologic estimates of paternal age risk for CHD, ID and EPI were consistent with the dnSNV effect. However, epidemiologic effects for ASDs and SCZ significantly exceeded the risk that could be explained by dnSNVs alone (p<2e-4 for both comparisons).\n\nConclusionIncreasing dnSNVs due to advanced paternal age confer a small amount of offspring risk for psychiatric and developmental disorders. For ASD and SCZ, epidemiologic associations with delayed paternity largely reflect factors that cannot be assumed to increase with age.

genetics↗
bioRxiv2017

Bayesian Reconstruction of Transmission within Outbreaks using Genomic Variants

Pathogen genome sequencing can reveal details of transmission histories and is a powerful tool in the fight against infectious disease. In particular, within-host pathogen genomic variants identified through heterozygous nucleotide base calls are a potential source of information to identify linked cases and infer direction and time of transmission. However, using such data effectively to model disease transmission presents a number of challenges, including differentiating genuine variants from those observed due to sequencing error, as well as the specification of a realistic model for within-host pathogen population dynamics.\n\nHere we propose a new Bayesian approach to transmission inference, BadTrIP (BAyesian epiDemiological TRansmission Inference from Polymorphisms), that explicitly models evolution of pathogen populations in an outbreak, transmission (including transmission bottlenecks), and sequencing error. BadTrIP enables the inference of host-to-host transmission from pathogen sequencing data and epidemiological data. By assuming that genomic variants are unlinked, our method does not require the computationally intensive and unreliable reconstruction of individual haplotypes. Using simulations we show that BadTrIP is robust in most scenarios and can accurately infer transmission events by efficiently combining information from genetic and epidemiological sources; thanks to its realistic model of pathogen evolution and the inclusion of epidemiological data, BadTrIP is also more accurate than existing approaches. BadTrIP is distributed as an open source package (https://bitbucket.org/nicofmay/badtrip) for the phylogenetic software BEAST2.\n\nWe apply our method to reconstruct transmission history at the early stages of the 2014 Ebola outbreak, showcasing the power of within-host genomic variants to reconstruct transmission events.\n\nAuthor SummaryWe present a new tool to reconstruct transmission events within outbreaks. Our approach makes use of pathogen genetic information, notably genetic variants at low frequency within host that are usually discarded, and combines it with epidemiological information of host exposure to infection. This leads to accurate reconstruction of transmission even in cases where abundant within-host pathogen genetic variation and weak transmission bottlenecks (multiple pathogen units colonising a new host at transmission) would otherwise make inference difficult due to the transmission history differing from the pathogen evolution history inferred from pathogen isolets. Also, the use of within-host pathogen genomic variants increases the resolution of the reconstruction of the transmission tree even in scenarios with limited within-outbreak pathogen genetic diversity: within-host pathogen populations that appear identical at the level of consensus sequences can be discriminated using within-host variants. Our Bayesian approach provides a measure of the confidence in different possible transmission histories, and is published as open source software. We show with simulations and with an analysis of the beginning of the 2014 Ebola outbreak that our approach is applicable in many scenarios, improves our understanding of transmission dynamics, and will contribute to finding and limiting sources and routes of transmission, and therefore preventing the spread of infectious disease.

genetics↗
bioRxiv2018

Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens

Genetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs quickly after resistance is deployed, resulting in significant crop losses and a continuing need to develop new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability. These strategies mainly rely on varying different combinations of resistance sources across time (crop rotations) and space. The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental comparison of the efficiency (i.e. ability to reduce disease impact) and durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of landscape-scale deployment strategies presents major logistical challenges.\n\nTherefore, we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above, including both qualitative resistance (i.e. major genes) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model, implemented in the R package landsepi, provides a new and useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters.\n\nThis article describes the model and its parameterisation for rust diseases of cereal crops, caused by fungi of the genus Puccinia. To illustrate the model, we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance. The comparison of the four major deployment strategies described above will be the objective of future studies.\n\nAuthor summaryThere are many recent examples which demonstrate the evolutionary potential of plant pathogens to overcome the resistances deployed in agricultural landscapes to protect our crops. Increasingly, it is recognised that how resistance is deployed spatially and temporally can impact on rates of pathogen evolution and resistance breakdown. Such deployment strategies are mainly based on the combination of several sources of resistance at different spatiotemporal scales. However, comparison of these strategies in a predictive sense is not an easy task, owing to the logistical difficulties associated with experiments involving the spread of a pathogen at large spatio-temporal scales. Moreover, both the durability of a strategy and the epidemiological protection it provides to crops must be assessed since these evaluation criteria are not necessarily correlated. Surprisingly, no current simulation model allows a thorough comparison of the different options. Here we describe a spatio-temporal model able to simulate a wide range of deployment strategies and resistance sources. This model, implemented in the R package landsepi, facilitates assessment of both epidemiological and evolutionary outcomes across simulated scenarios. In this work, the model is used to investigate the combination of different sources of resistance against fungal diseases such as rusts of cereal crops.

evolutionary biology↗
bioRxiv2018

Evolution of pathogen traits in response to quantitative host resistance in heterogeneous environments

We model the evolutionary epidemiology of spore-producing plant pathogens in heterogeneous environments sown with several cultivars carrying quantitative resistances. The model explicitly tracks the infection-age structure and genetic composition of the pathogen population. Each strain is characterized by pathogenicity traits describing its infection efficiency and a time-varying sporulation curve taking into account lesion ageing. We first derive a general expression of the basic reproduction number [R]0 for fungal pathogens in heterogeneous environments. We show that evolutionary attractors of the model coincide with local maxima of [R]0 only if the infection efficiency is the same on all host types. We then study how three basic resistance characteristics (pathogenicity trait targeted, resistance effectiveness, and adaptation cost) in interaction with the deployment strategy (proportion of fields sown with a resistant cultivar) (i) lead to pathogen diversification at equilibrium and (ii) shape the transient dynamics from evolutionary and epidemiological perspectives. We show that quantitative resistance impacting only the sporulation curve will always lead to a monomorphic population, while dimorphism (i.e. pathogen diversification) can occur with resistance altering infection efficiency, notably with high adaptation cost and proportion of R cultivar. Accordingly, the choice of quantitative resistance genes operated by plant breeders is a driver of pathogen diversification. From an evolutionary perspective, the emergence time of the evolutionary attractor best adapted to the R cultivar tends to be shorter when the resistance impacts infection efficiency than when it impacts sporulation. In contrast, from an epidemiological perspective, the epidemiological control is always higher when the resistance impacts infection efficiency. This highlights the difficulty of defining deployment strategies of quantitative resistance maximising at the same time epidemiological and evolutionary outcomes.

evolutionary biology↗
bioRxiv2017

Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape

Characterising the spatio-temporal dynamics of pathogens in natura is key to ensuring their efficient prevention and control. However, it is notoriously difficult to estimate dispersal parameters at scales that are relevant to real epidemics. Epidemiological surveys can provide informative data, but parameter estimation can be hampered when the timing of the epidemiological events is uncertain, and in the presence of interactions between disease spread, surveillance, and control. Further complications arise from imperfect detection of disease, and from the computationally intractable number of data on individual hosts arising from landscape-level surveys. Here, we present a Bayesian framework that overcomes these barriers by integrating over associated uncertainties in a model explicitly combining the processes of disease dispersal, surveillance and control. Using a novel computationally efficient approach to account for patch geometry, we demonstrate that disease dispersal distances can be estimated accurately in a fragmented landscape when disease control is ongoing. Applying this model to data for an aphid-borne virus (Plum pox virus) surveyed for 15 years over 600 orchards, we obtain the first estimate of the distribution of the flight distances of infectious aphids at the landscape scale. Most infectious aphids leaving a tree land beyond the bounds of a 1-ha orchard (50% of flights terminate within about 90 m). Moreover, long-distance flights are not rare (10% of flights exceed 1 km). By their impact on our quantitative understanding of winged aphids dispersal, these results can inform the design of management strategies for plant viruses, which are mainly aphid-borne.\n\nAuthor SummaryIn spatial epidemiology, dispersal kernels quantify how the probability of pathogen dissemination varies with distance. Spatial models of pathogen spread are sensitive to kernel parameters; yet these parameters have rarely been estimated using field data gathered at relevant scales. Robust estimation is rendered difficult by practical constraints limiting the number of surveyed individuals, and uncertainties concerning their disease status. Here, we present a framework that overcomes these barriers and permits inference for a between-patch transmission model. Extensive simulations show that dispersal kernels can be estimated from epidemiological surveillance data. When applied to such data collected from more than 600 orchards during 15 years of a plant virus epidemic our approach enables the estimation of the dispersal kernel of infectious winged aphids. This kernel is long-tailed, as 50% of the infectious aphids leaving a tree terminate their infectious flight within 90 m and 10% beyond 1 km. This first estimate of flight distances at the landscape scale for aphids-a group of vectors transmitting numerous viruses-is crucial for the science-based design of control strategies targeting plant virus epidemics.

epidemiology↗
bioRxiv2017

A data-driven model for the assessment of age-dependent patterns of Tuberculosis burden and impact evaluation of novel vaccines.

In the case of tuberculosis (TB), the capabilities of epidemic models to produce quantitatively robust forecasts are limited by multiple hindrances. Among these, understanding the complex relationship between disease epidemiology and populations' age structure has been highlighted as one of the most relevant. TB dynamics depends on age in multiple ways, some of which are traditionally simplified in the literature. That is the case of the heterogeneities in contact intensity among different age-strata that are common to all air-borne diseases, but still typically neglected in the TB case. Furthermore, whilst demographic structures of many countries are rapidly aging, demographic dynamics is pervasively ignored when modeling TB spreading. In this work, we present a TB transmission model that incorporates country-specific demographic prospects and empirical contact data around a data-driven description of TB dynamics. Using our model, we find that the inclusion of demographic dynamics is followed by an increase in the burden levels prospected for the next decades in the areas of the world that are most hit by the disease nowadays. Similarly, we show that considering realistic patterns of contacts among individuals in different age-strata reshapes the transmission patterns reproduced by the models, a result with potential implications for the design of age-focused epidemiological interventions.\n\nSignificance StatementEven though tuberculosis (TB) is acknowledged as a strongly age-dependent disease, it remains unclear how TB epidemics would react, in the following decades, to the generalized aging that human populations are experiencing worldwide. This situation is partly caused by the limitations of current transmission models at describing the relationship between demography and TB transmission. Here, we present a data-driven epidemiological model that, unlike previous approaches, explicitly contemplates relevant aspects of the coupling between agestructure and TB dynamics, such as demographic evolution and contact heterogeneities. Using our model, we identify substantial biases in epidemiological forecasts rooted in an inadequate description of these aspects, both at the level of aggregated incidence and mortality rates and their distribution across age-strata.

epidemiology↗
bioRxiv2016

Polygenic analysis of schizophrenia and 19 immune diseases reveals modest pleiotropy

Epidemiological studies indicate that many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenotypic correlation between immune diseases and schizophrenia might be explained by shared genetic risk factors (genetic correlation). We used data from a large genome-wide association study (GWAS) of schizophrenia (N=35,476 cases and 46,839 controls) to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified three variants with pleiotropic effects, located in regions associated with both schizophrenia and immune disease. Our analyses provided the strongest evidence of pleiotropy at rs1734907 ([~]85kb upstream of EPHB4), a variant which was associated with increased risk of both Crohns disease (OR = 1.16, P = 1.67x10-13) and schizophrenia (OR = 1.07, P = 7.55x10-6). Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using polygenic risk scores (PRS) and cross-trait LD Score regression (LDSC). PRS revealed significant genetic overlap with schizophrenia for narcolepsy (p=4.1x10-4), primary biliary cirrhosis (p=1.4x10-8), psoriasis (p=3.6x10-5), systemic lupus erythematosus (p=2.2x10-8), and ulcerative colitis (p=4.3x10-4). Genetic correlations between these immune diseases and schizophrenia, estimated using LDSC, ranged from 0.10 to 0.18 and were consistent with the expected phenotypic correlation based on epidemiological data. We also observed suggestive evidence of sex-dependent genetic correlation between schizophrenia and multiple sclerosis (interaction p=0.02), with genetic risk scores for multiple sclerosis associated with greater risk of schizophrenia among males but not females. Our findings suggest that shared genetic risk factors contribute to the epidemiological co-occurrence of schizophrenia and certain immune diseases, and suggest that in some cases this genetic correlation is sex-dependent. Author SummaryImmune diseases occur at different rates among patients with schizophrenia compared to the general population. While the reasons for this phenotypic correlation are unclear, shared genetic risk (genetic correlation) has been proposed as a contributing factor. Prior studies have estimated the genetic correlation between schizophrenia and a handful of immune diseases, with conflicting results. Here, we performed a comprehensive cross-disorder investigation of schizophrenia and 19 immune diseases. We identified three individual genetic variants associated with both schizophrenia and immune diseases, including a variant near EPHB4 - a gene whose protein product guides the migration of lymphocytes towards infected cells in the immune system and the migration of neuronal axons in the brain. We demonstrated significant genome-wide genetic correlation between schizophrenia and narcolepsy, primary biliary cirrhosis, psoriasis, systemic lupus erythematosus, and ulcerative colitis. Finally, we identified a potential sex-dependent pleiotropic effect between schizophrenia and multiple sclerosis. Our findings point to shared genetic risk for schizophrenia and at least a subset of immune diseases, which likely contributes to their epidemiological co-occurrence. These results raise the possibility that the same genetic variants may exert their effects on neurons or immune cells to influence the development of psychiatric and immune disorders, respectively.

Genetics↗
bioRxiv2018

A quantitative evaluation of MIRU-VNTR typing against whole-genome sequencing for identifying Mycobacterium tuberculosis transmission: A prospective observational cohort study

SummaryO_ST_ABSBackgroundC_ST_ABSMycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat (MIRU-VNTR) typing is widely used in high-income countries for Mycobacterium tuberculosis typing. Whole-genome sequencing (WGS) is known to deliver greater specificity, but no quantitative prospective comparison has yet been undertaken.\n\nMethodsWe studied isolates from the English Midlands, sampled consecutively between 1 January 2012 and 31 December 2015. In addition to routinely performed MIRU-VNTR typing, DNA was extracted from liquid cultures and sequenced using Illumina technology. Demographic and epidemiological data were extracted from the Enhanced Tuberculosis Surveillance system maintained by Public Health England. Closely related samples, defined using a threshold of five single nucleotide variants (SNVs), were compared to samples with identical MIRU-VNTR profiles, with shared epidemiological risk factors, and to those with both characteristics.\n\nFindings1,999 patients were identified for whom at least one M. tuberculosis isolate had been MIRU-VNTR typed and sequenced. Comparing epidemiological risk factors with close genetic relatedness, only coresidence had a positive predictive value of over 5%. Excluding co-resident individuals, 18.6% of patients with identical MIRU-VNTR profiles were within 5 SNVs. Where patients also shared social risk factors and ethnic group, this rose to 48%. Only 8% of MIRU-VNTR linked pairs in lineage 1 were within 5 SNV, compared to 31% in lineage 4.\n\nInterpretationIn the setting studied, MIRU-VNTR typing and epidemiological risk factors are poorly predictive of close genomic relatedness, assessed by SNV. MIRU-VNTR performance varies markedly by lineage.\n\nFundingPublic Health England, National Institute of Health Research Oxford Biomedical Research Centre.

microbiology↗
bioRxiv2018

SciLMM: Computing heritability with millions of individuals

The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records.\n\nAuthor SummaryThe advent of online genealogy services allows the assembly of population-scale family trees, spanning millions of individuals and centuries of human history. Such datasets enable answering genetic epidemiology questions on unprecedented scales. Here we present Sci-LMM, a pedigree analysis framework that combines techniques from animal and plant breeding research and from human genetics research for large-scale pedigree analysis. We apply Sci-LMM to analyze population-scale human genealogical records, spanning trillions of relationships. We have made both Sci-LMM and an anonymized dataset of millions of individuals freely available to download, making the analysis of population-scale human family trees widely accessible to the research community. Together, these resources allow researchers to investigate genetic and epidemiological questions on an unprecedented scale.

genetics↗
bioRxiv2018

Bayesian phylodynamic inference with complex models

Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry.The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST phylogenetics platform.

bioinformatics↗
bioRxiv2018

GI-16 lineage (624/I or Q1), there and back again: the history of one of the major threat for poultry farming of our era

The genetic variability of Infectious bronchitis virus (IBV) is one of the main challenges for its control, hindering not only the development of effective vaccination strategies but also its classification and, consequently, epidemiology understanding. The 624/I and Q1 genotypes, now recognized to be part of the GI-16 lineage, represent an excellent example of the practical consequences of IBV molecular epidemiology limited knowledge. In fact, being their common origin unrecognized for a long time, independent epidemiological pictures were drawn for the two genotypes. To fix this misinterpretation, the present study reconstructs the history, population dynamics and spreading patterns of GI-16 lineage as a whole using a phylodynamic approach. A collection of worldwide available hypervariable region 1 and 2 (HVR12) and 3 (HVR3) sequences of the S1 protein was analysed together with 258 HVR3 sequences obtained from samples collected in Italy (the country where this genotype was initially identified) since 1963. The results demonstrate that after its emergence at the beginning of the XX century, GI-16 was able to persist until present days in Italy. Approximately in the late 1980s, it migrated to Asia, which became the main nucleus for further spreading to Middle East, Europe and especially South America, likely through multiple introduction events. A remarkable among-country diffusion was also demonstrated in Asia and South America. Interestingly, although most of the recent Italian GI-16 strains originated from ancestral viruses detected in the same country, a couple were closely related to Chinese ones, supporting a backward viral flow from China to Italy.\n\nBesides to the specific case-study results, this work highlights the misconceptions that originate from the lack of a unified nomenclature and poor molecular epidemiology data generation and sharing. This shortcoming appears particularly relevant since the described scenario could likely be shared by many other IBV genotypes and pathogens in general.

evolutionary biology↗