A Novel Statistical Model to Estimate Host Genetic Effects Affecting Disease Transmission

There is increasing recognition that genetic diversity can affect the spread of diseases, potentially affecting plant and livestock disease control as well as the emergence of human disease outbreaks. Nevertheless, even though computational tools can guide the control of infectious diseases, few epidemiological models can simultaneously accommodate the inherent individual heterogeneity in multiple infectious disease traits influencing disease transmission, such as the frequently modeled propensity to become infected and infectivity, which describes the host ability to transmit the infection to susceptible individuals. Furthermore, current quantitative genetic models fail to fully capture the heritable variation in host infectivity, mainly because they cannot accommodate the nonlinear infection dynamics underlying epidemiological data. We present in this article a novel statistical model and an inference method to estimate genetic parameters associated with both host susceptibility and infectivity. Our methodology combines quantitative genetic models of social interactions with stochastic processes to model the random, nonlinear, and dynamic nature of infections and uses adaptive Bayesian computational techniques to estimate the model parameters. Results using simulated epidemic data show that our model can accurately estimate heritabilities and genetic risks not only of susceptibility but also of infectivity, therefore exploring a trait whose heritable variation is currently ignored in disease genetics and can greatly influence the spread of infectious diseases. Our proposed methodology offers potential impacts in areas such as livestock disease control through selective breeding and also in predicting and controlling the emergence of disease outbreaks in human populations.     

Indirect genetic effects and the spread of infectious disease: are we capturing the full heritable variation underlying disease prevalence?

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence.     

Assessment of global asymptomatic SARS-CoV-2 infection and management practices from China

With ongoing research, it was found that asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was widespread in coronavirus disease 2019 (COVID-19) populations. Studies have confirmed asymptomatic patients with COVID-19 have potential infectivity, and most of the transmission occurred before symptoms appear. Asymptomatic infection rates varied widely in different countries and regions. Identifying the asymptomatic infected persons and cutting off the infection source is an effective way to prevent the spread of this disease. However, asymptomatic patients have hidden clinical symptoms, and screening based only on the clinical symptoms of COVID-19 can easily lead to a missed diagnosis. Therefore, determining asymptomatic infection patients by SARS-CoV-2 nucleic acid testing is the gold standard. A series of prevention and control measures adopted by the Chinese government, especially the “Four Early” policy, have achieved outstanding achievements, which are worth learning from by other countries.     

Genetic Correlations among Canine Hip Dysplasia Radiographic Traits in a Cohort of Australian German Shepherd Dogs,and Implications for the Design of a More Effective Genetic Control Program

Canine hip dysplasia (CHD) is a common musculoskeletal disease in pedigree dog populations. It can cause severe pain and dysfunction which may require extensive medication and/or surgical treatment and often ultimately requires humane euthanasia. CHD has been found to be moderately heritable and, given its impact on welfare, should be considered an imperative breeding priority. The British Veterinary Association/Kennel Club scoring method is one of several measures used to assess the genetic propensity of potential breeding stock for dysplastic changes to the hips based on radiographic examination. It is a complex measure composed of nine ordinal traits, intended to evaluate both early and late dysplastic changes. It would be highly desirable if estimated breeding values (EBVs) for these nine traits were consolidated into a simpler, EBV-based, selection index more easily usable by breeders. A multivariate analysis on the phenotype scores from an Australian cohort of 13,124 German Shepherd Dogs (GSDs) returned genetic correlations between 0.48–0.97 for the nine traits which fell into two trait groups, Group 1 reflecting early changes (“laxity”) and Group 2 reflecting late changes (“osteoarthritis”). Principal components analysis of the ordinal EBVs suggested the same pattern, with strong differentiation between “laxity” and “osteoarthritis” traits in the second component. Taking account of all results, we recommend interim use of two selection indexes: the first being the average of ordinal EBVs for “laxity” traits and the second being the average of ordinal EBVs for “osteoarthritis” traits. The correlation between these two selection indexes (0.771–0.774) is sufficiently less than unity enabling the selection of dogs with different genetic propensity for laxity and for osteoarthritic CHD changes in GSDs; this may also be applicable in other breeds. Dogs with low propensity for severe osteoarthritic change in the presence of laxity may be of interest both in molecular research and breeding programs.     

Temperature Alters Host Genotype-Specific Susceptibility to Chytrid Infection

The cost of parasitism often depends on environmental conditions and host identity. Therefore, variation in the biotic and abiotic environment can have repercussions on both, species-level host-parasite interaction patterns but also on host genotype-specific susceptibility to disease. We exposed seven genetically different but concurrent strains of the diatom Asterionella formosa to one genotype of its naturally co-occurring chytrid parasite Zygorhizidium planktonicum across five environmentally relevant temperatures. We found that the thermal tolerance range of the tested parasite genotype was narrower than that of its host, providing the host with a “cold” and “hot” thermal refuge of very low or no infection. Susceptibility to disease was host genotype-specific and varied with temperature level so that no genotype was most or least resistant across all temperatures. This suggests a role of thermal variation in the maintenance of diversity in disease related traits in this phytoplankton host. The duration and intensity of chytrid parasite pressure on host populations is likely to be affected by the projected changes in temperature patterns due to climate warming both through altering temperature dependent disease susceptibility of the host and, potentially, through en- or disabling thermal host refugia. This, in turn may affect the selective strength of the parasite on the genetic architecture of the host population.     

Genetic management of infectious diseases: a heterogeneous epidemio-genetic model illustrated with S. aureus mastitis

Given that individuals are genetically heterogeneous in their degree of resistance to infection, a model is proposed to formulate appropriate choices that will limit the spread of an infectious disease. The model is illustrated with data on S. aureus mastitis and is based on parameters characterizing the spread of the disease (contact rate, probability of infection after contact, and rate of recovery after infection), the demography (replacement and culling rates) and the genetic composition (degree of relationship and heritability of the disease trait) of the animal population. To decrease infection pressure, it is possible to apply non-genetic procedures that increase the culling (e.g., culling of chronically infected cows) and recovery (e.g., antibiotic therapy) rates of infected cows. But the contribution of the paper is to show that genetic management of infectious disease is also theoretically possible as a control measure complementary to non-genetic actions. Indeed, the probability for an uninfected individual to become infected after contact with an infected one is partially related to their degree of kinship: the more closely they are related, the more likely they are to share identical genes like those associated to the non-resistance to infection. Different prospective genetic management procedures are proposed to decrease the contact rate between infected and uninfected relatives and keep the number of secondary cases generated by one infected animal below 1.     

Solar radiation decreases parasitism in Daphnia

Climate change and variation in atmospheric ozone are influencing the intensity of ultraviolet radiation (UVR) reaching ecosystems. Changing UVR regimes, in turn, may alter epidemics of infectious disease. This possibility hinges on the sensitivity of epidemiologically relevant traits of host and parasite to UVR. We address this issue using a planktonic system (a zooplankton host, Daphnia dentifera, and its virulent fungal parasite, Metschnikowia bicuspidata). Controlled laboratory experiments, coupled with in situ field incubations of spores, revealed that quite low levels of UVR (as well as longer wavelength light) sharply reduced the infectivity of fungal spores but did not affect host susceptibility to infection. The parasite's sensitivity to solar radiation may underlie patterns in a lake survey: higher penetration of solar radiation into lakes correlated with smaller epidemics that started later in autumn (as incident sunlight declined). Thus, solar radiation, by diminishing infectivity of the parasite, may potently reduce disease.     

Coxsackievirus B Exits the Host Cell in Shed Microvesicles Displaying Autophagosomal Markers

Coxsackievirus B3 (CVB3), a member of the picornavirus family and enterovirus genus, causes viral myocarditis, aseptic meningitis, and pancreatitis in humans. We genetically engineered a unique molecular marker, “fluorescent timer” protein, within our infectious CVB3 clone and isolated a high-titer recombinant viral stock (Timer-CVB3) following transfection in HeLa cells. “Fluorescent timer” protein undergoes slow conversion of fluorescence from green to red over time, and Timer-CVB3 can be utilized to track virus infection and dissemination in real time. Upon infection with Timer-CVB3, HeLa cells, neural progenitor and stem cells (NPSCs), and C2C12 myoblast cells slowly changed fluorescence from green to red over 72 hours as determined by fluorescence microscopy or flow cytometric analysis. The conversion of “fluorescent timer” protein in HeLa cells infected with Timer-CVB3 could be interrupted by fixation, suggesting that the fluorophore was stabilized by formaldehyde cross-linking reactions. Induction of a type I interferon response or ribavirin treatment reduced the progression of cell-to-cell virus spread in HeLa cells or NPSCs infected with Timer-CVB3. Time lapse photography of partially differentiated NPSCs infected with Timer-CVB3 revealed substantial intracellular membrane remodeling and the assembly of discrete virus replication organelles which changed fluorescence color in an asynchronous fashion within the cell. “Fluorescent timer” protein colocalized closely with viral 3A protein within virus replication organelles. Intriguingly, infection of partially differentiated NPSCs or C2C12 myoblast cells induced the release of abundant extracellular microvesicles (EMVs) containing matured “fluorescent timer” protein and infectious virus representing a novel route of virus dissemination. CVB3 virions were readily observed within purified EMVs by transmission electron microscopy, and infectious virus was identified within low-density isopycnic iodixanol gradient fractions consistent with membrane association. The preferential detection of the lipidated form of LC3 protein (LC3 II) in released EMVs harboring infectious virus suggests that the autophagy pathway plays a crucial role in microvesicle shedding and virus release, similar to a process previously described as autophagosome-mediated exit without lysis (AWOL) observed during poliovirus replication. Through the use of this novel recombinant virus which provides more dynamic information from static fluorescent images, we hope to gain a better understanding of CVB3 tropism, intracellular membrane reorganization, and virus-associated microvesicle dissemination within the host.     

Microbiome-immune interactions in tuberculosis

Tuberculosis (TB) remains an infectious disease of global significance and a leading cause of death in low- and middle-income countries. Significant effort has been directed towards understanding Mycobacterium tuberculosis genomics, virulence, and pathophysiology within the framework of Koch postulates. More recently, the advent of “-omics” approaches has broadened our appreciation of how “commensal” microbes have coevolved with their host and have a central role in shaping health and susceptibility to disease. It is now clear that there is a diverse repertoire of interactions between the microbiota and host immune responses that can either sustain or disrupt homeostasis. In the context of the global efforts to combatting TB, such findings and knowledge have raised important questions: Does microbiome composition indicate or determine susceptibility or resistance to M. tuberculosis infection? Is the development of active disease or latent infection upon M. tuberculosis exposure influenced by the microbiome? Does microbiome composition influence TB therapy outcome and risk of reinfection with M. tuberculosis? Can the microbiome be actively managed to reduce risk of M. tuberculosis infection or recurrence of TB? Here, we explore these questions with a particular focus on microbiome-immune interactions that may affect TB susceptibility, manifestation and progression, the long-term implications of anti-TB therapy, as well as the potential of the host microbiome as target for clinical manipulation.     

Expression of parasite genetic variation changes over the course of infection: implications of within-host dynamics for the evolution of virulence

How infectious disease agents interact with their host changes during the course of infection and can alter the expression of disease-related traits. Yet by measuring parasite life-history traits at one or few moments during infection, studies have overlooked the impact of variable parasite growth trajectories on disease evolution. Here we show that infection-age-specific estimates of host and parasite fitness components can reveal new insight into the evolution of parasites. We do so by characterizing the within-host dynamics over an entire infection period for five genotypes of the castrating bacterial parasite Pasteuria ramosa infecting the crustacean Daphnia magna. Our results reveal that genetic variation for parasite-induced gigantism, host castration and parasite spore loads increases with the age of infection. Driving these patterns appears to be variation in how well the parasite maintains control of host reproduction late in the infection process. We discuss the evolutionary consequences of this finding with regard to natural selection acting on different ages of infection and the mechanism underlying the maintenance of castration efficiency. Our results highlight how elucidating within-host dynamics can shed light on the selective forces that shape infection strategies and the evolution of virulence.     

Strain Interactions as a Mechanism for Dominant Strain Alternation and Incidence Oscillation in Infectious Diseases: Seasonal Influenza as a Case Study

Background

Many human infectious diseases are caused by pathogens that have multiple strains and show oscillation in infection incidence and alternation of dominant strains which together are referred to as epidemic cycling. Understanding the underlying mechanisms of epidemic cycling is essential for forecasting outbreaks of epidemics and therefore important for public health planning. Current theoretical effort is mainly focused on the factors that are extrinsic to the pathogens themselves (“extrinsic factors”) such as environmental variation and seasonal change in human behaviours and susceptibility. Nevertheless, co-circulation of different strains of a pathogen was usually observed and thus strains interact with one another within concurrent infection and during sequential infection. The existence of these intrinsic factors is common and may be involved in the generation of epidemic cycling of multi-strain pathogens.

Methods and Findings

To explore the mechanisms that are intrinsic to the pathogens themselves (“intrinsic factors”) for epidemic cycling, we consider a multi-strain SIRS model including cross-immunity and infectivity enhancement and use seasonal influenza as an example to parameterize the model. The Kullback-Leibler information distance was calculated to measure the match between the model outputs and the typical features of seasonal flu (an outbreak duration of 11 weeks and an annual attack rate of 15%). Results show that interactions among strains can generate seasonal influenza with these characteristic features, provided that: the infectivity of a single strain within concurrent infection is enhanced 2−7 times that within a single infection; cross-immunity as a result of past infection is 0.5–0.8 and lasts 2–9 years; while other parameters are within their widely accepted ranges (such as a 2–3 day infectious period and the basic reproductive number of 1.8–3.0). Moreover, the observed alternation of the dominant strain among epidemics emerges naturally from the best fit model. Alternative modelling that also includes seasonal forcing in transmissibility shows that both external mechanisms (i.e. seasonal forcing) and the intrinsic mechanisms (i.e., strain interactions) are equally able to generate the observed time-series in seasonal flu.

Conclusions

The intrinsic mechanism of strain interactions alone can generate the observed patterns of seasonal flu epidemics, but according to Kullback-Leibler information distance the importance of extrinsic mechanisms cannot be excluded. The intrinsic mechanism illustrated here to explain seasonal flu may also apply to other infectious diseases caused by polymorphic pathogens.     

Cross-Species Virus Transmission and the Emergence of New Epidemic Diseases

Summary: Host range is a viral property reflecting natural hosts that are infected either as part of a principal transmission cycle or, less commonly, as “spillover” infections into alternative hosts. Rarely, viruses gain the ability to spread efficiently within a new host that was not previously exposed or susceptible. These transfers involve either increased exposure or the acquisition of variations that allow them to overcome barriers to infection of the new hosts. In these cases, devastating outbreaks can result. Steps involved in transfers of viruses to new hosts include contact between the virus and the host, infection of an initial individual leading to amplification and an outbreak, and the generation within the original or new host of viral variants that have the ability to spread efficiently between individuals in populations of the new host. Here we review what is known about host switching leading to viral emergence from known examples, considering the evolutionary mechanisms, virus-host interactions, host range barriers to infection, and processes that allow efficient host-to-host transmission in the new host population.     

A unifying theory for genetic epidemiological analysis of binary disease data

Background

Genetic selection for host resistance offers a desirable complement to chemical treatment to control infectious disease in livestock. Quantitative genetics disease data frequently originate from field studies and are often binary. However, current methods to analyse binary disease data fail to take infection dynamics into account. Moreover, genetic analyses tend to focus on host susceptibility, ignoring potential variation in infectiousness, i.e. the ability of a host to transmit the infection. This stands in contrast to epidemiological studies, which reveal that variation in infectiousness plays an important role in the progression and severity of epidemics. In this study, we aim at filling this gap by deriving an expression for the probability of becoming infected that incorporates infection dynamics and is an explicit function of both host susceptibility and infectiousness. We then validate this expression according to epidemiological theory and by simulating epidemiological scenarios, and explore implications of integrating this expression into genetic analyses.

Results

Our simulations show that the derived expression is valid for a range of stochastic genetic-epidemiological scenarios. In the particular case of variation in susceptibility only, the expression can be incorporated into conventional quantitative genetic analyses using a complementary log-log link function (rather than probit or logit). Similarly, if there is moderate variation in both susceptibility and infectiousness, it is possible to use a logarithmic link function, combined with an indirect genetic effects model. However, in the presence of highly infectious individuals, i.e. super-spreaders, the use of any model that is linear in susceptibility and infectiousness causes biased estimates. Thus, in order to identify super-spreaders, novel analytical methods using our derived expression are required.

Conclusions

We have derived a genetic-epidemiological function for quantitative genetic analyses of binary infectious disease data, which, unlike current approaches, takes infection dynamics into account and allows for variation in host susceptibility and infectiousness.     

Biogeography and Virulence of Staphylococcus aureus

Background

Staphylococcus aureus is commonly carried asymptomatically in the human anterior nares and occasionally enters the bloodstream to cause invasive disease. Much of the global diversity of S. aureus remains uncharacterised, and is not clear how disease propensity varies between strains, and between host populations.

Methodology

We compared 147 isolates recovered from five kindergartens in Chengdu, China, with 51 isolates contemporaneously recovered from cases of pediatric infection from the main hospital serving this community. The samples were characterised by MLST, the presence/absence of PVL, and antibiotic resistance profiling.

Principal Findings

Genotype frequencies within individual kindergartens differ, but the sample recovered from cases of disease shows a general enrichment of certain MLST genotypes and PVL positive isolates. Genotypes under-represented in the disease sample tend to correspond to a single sequence cluster, and this cluster is more common in China than in other parts of the world.

Conclusions/Significance

Virulence propensity likely reflects a synergy between variation in the core genome (MLST) and accessory genome (PVL). By combining evidence form biogeography and virulence we demonstrate the existence of a “native” clade in West China which has lowered virulence, possibility due to acquired host immunity.     

Sheep Movement Networks and the Transmission of Infectious Diseases

Background and Methodology

Various approaches have been used to investigate how properties of farm contact networks impact on the transmission of infectious diseases. The potential for transmission of an infection through a contact network can be evaluated in terms of the basic reproduction number, R ₀. The magnitude of R ₀ is related to the mean contact rate of a host, in this case a farm, and is further influenced by heterogeneities in contact rates of individual hosts. The latter can be evaluated as the second order moments of the contact matrix (variances in contact rates, and co-variance between contacts to and from individual hosts). Here we calculate these quantities for the farms in a country-wide livestock network: >15,000 Scottish sheep farms in each of 4 years from July 2003 to June 2007. The analysis is relevant to endemic and chronic infections with prolonged periods of infectivity of affected animals, and uses different weightings of contacts to address disease scenarios of low, intermediate and high animal-level prevalence.

Principal Findings and Conclusions

Analysis of networks of Scottish farms via sheep movements from July 2003 to June 2007 suggests that heterogeneities in movement patterns (variances and covariances of rates of movement on and off the farms) make a substantial contribution to the potential for the transmission of infectious diseases, quantified as R ₀, within the farm population. A small percentage of farms (<20%) contribute the bulk of the transmission potential (>80%) and these farms could be efficiently targeted by interventions aimed at reducing spread of diseases via animal movement.     

Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits

Background

In the analysis of complex traits, genetic effects can be confounded with non-genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive genetic and non-genetic effects. This confounding may cause the estimated genetic variance components to be inaccurate and biased.

Methods

In this study, we constructed genetic covariance structures from whole-genome marker data, and thus used realized relationship matrices to estimate variance components in a heterogenous population of ~ 2200 mice for which four complex traits were investigated. These mice were genotyped for more than 10,000 single nucleotide polymorphisms (SNP) and the variances due to family, cage and genetic effects were estimated by models based on pedigree information only, aggregate SNP information, and model selection for specific SNP effects.

Results and conclusions

We show that the use of genome-wide SNP information can disentangle confounding factors to estimate genetic variances by separating genetic and non-genetic effects. The estimated variance components using realized relationship were more accurate and less biased, compared to those based on pedigree information only. Models that allow the selection of individual SNP in addition to fitting a relationship matrix are more efficient for traits with a significant dominance variance.     

Dissociation of Prion Protein Amyloid Seeding from Transmission of a Spongiform Encephalopathy

Misfolding and aggregation of proteins are common pathogenic mechanisms of a group of diseases called proteinopathies. The formation and spread of proteinaceous lesions within and between individuals were first described in prion diseases and proposed as the basis of their infectious nature. Recently, a similar “prion-like” mechanism of transmission has been proposed in other neurodegenerative diseases such as Alzheimer''s disease. We investigated if misfolding and aggregation of corrupted prion protein (PrP^TSE) are always associated with horizontal transmission of disease. Knock-in transgenic mice (101LL) expressing mutant PrP (PrP-101L) that are susceptible to disease but do not develop any spontaneous neurological phenotype were inoculated with (i) brain extracts containing PrP^TSE from healthy 101LL mice with PrP plaques in the corpus callosum or (ii) brain extracts from mice overexpressing PrP-101L with neurological disease, severe spongiform encephalopathy, and formation of proteinase K-resistant PrP^TSE. In all instances, 101LL mice developed PrP plaques in the area of inoculation and vicinity in the absence of clinical disease or spongiform degeneration of the brain. Importantly, 101LL mice did not transmit disease on serial passage, ruling out the presence of subclinical infection. Thus, in both experimental models the formation of PrP^TSE is not infectious. These results have implications for the interpretation of tests based on the detection of protein aggregates and suggest that de novo formation of PrP^TSE in the host does not always result in a transmissible prion disease. In addition, these results question the validity of assuming that all diseases due to protein misfolding can be transmitted between individuals.     

HOW SPECIFICITY AND EPIDEMIOLOGY DRIVE THE COEVOLUTION OF STATIC TRAIT DIVERSITY IN HOSTS AND PARASITES

There is typically considerable variation in the level of infectivity of parasites and the degree of resistance of hosts within populations. This trait variation is critical not only to the evolutionary dynamics but also to the epidemiology, and potentially the control of infectious disease. However, we lack an understanding of the processes that generate and maintain this trait diversity. We examine theoretically how epidemiological feedbacks and the characteristics of the interaction between host types and parasites strains determine the coevolution of host–parasite diversity. The interactions include continuous characterizations of the key phenotypic features of classic gene‐for‐gene and matching allele models. We show that when there are costs to resistance in the hosts and infectivity in the parasite, epidemiological feedbacks may generate diversity but this is limited to dimorphism, often of extreme types, in a broad range of realistic infection scenarios. For trait polymorphism, there needs to be both specificity of infection between host types and parasite strains as well as incompatibility between particular strains and types. We emphasize that although the high specificity is well known to promote temporal “Red Queen” diversity, it is costs and combinations of hosts and parasites that cannot infect that will promote static trait diversity.     

Within‐host interactions shape virulence‐related traits of trematode genotypes

Within‐host interactions between co‐infecting parasites can significantly influence the evolution of key parasite traits, such as virulence (pathogenicity of infection). The type of interaction is expected to predict the direction of selection, with antagonistic interactions favouring more virulent genotypes and synergistic interactions less virulent genotypes. Recently, it has been suggested that virulence can further be affected by the genetic identity of co‐infecting partners (G × G interactions), complicating predictions on disease dynamics. Here, we used a natural host–parasite system including a fish host and a trematode parasite to study the effects of G × G interactions on infection virulence. We exposed rainbow trout (Oncorhynchus mykiss) either to single genotypes or to mixtures of two genotypes of the eye fluke Diplostomum pseudospathaceum and estimated parasite infectivity (linearly related to pathogenicity of infection, measured as coverage of eye cataracts) and relative cataract coverage (controlled for infectivity). We found that both traits were associated with complex G × G interactions, including both increases and decreases from single infection to co‐infection, depending on the genotype combination. In particular, combinations where both genotypes had low average infectivity and relative cataract coverage in single infections benefited from co‐infection, while the pattern was opposite for genotypes with higher performance. Together, our results show that infection outcomes vary considerably between single and co‐infections and with the genetic identity of the co‐infecting parasites. This can result in variation in parasite fitness and consequently impact evolutionary dynamics of host–parasite interactions.     

A major quantitative trait locus affecting resistance to Tilapia lake virus in farmed Nile tilapia (Oreochromis niloticus)

Enhancing host resistance to infectious disease has received increasing attention in recent years as a major goal of farm animal breeding programs. Combining field data with genomic tools can provide opportunities to understand the genetic architecture of disease resistance, leading to new opportunities for disease control. In the current study, a genome-wide association study was performed to assess resistance to the Tilapia lake virus (TiLV), one of the biggest threats affecting Nile tilapia (Oreochromis niloticus); a key aquaculture species globally. A pond outbreak of TiLV in a pedigreed population of the GIFT strain was observed, with 950 fish classified as either survivor or mortality, and genotyped using a 65 K SNP array. A significant QTL of large effect was identified on chromosome Oni22. The average mortality rate of tilapia homozygous for the resistance allele at the most significant SNP (P value = 4.51E−10) was 11%, compared to 43% for tilapia homozygous for the susceptibility allele. Several candidate genes related to host response to viral infection were identified within this QTL, including lgals17, vps52, and trim29. These results provide a rare example of a major QTL affecting a trait of major importance to a farmed animal. Genetic markers from the QTL region have potential in marker-assisted selection to improve host resistance, providing a genetic solution to an infectious disease where few other control or mitigation options currently exist.Subject terms: Quantitative trait loci, Quantitative trait, Genetic markers, Animal breeding, Genome-wide association studies