Background The spread of infectious diseases in populations is controlled by the susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals. Estimating genetic risk factors for these three underlying host epidemiological traits can help reduce disease spread through genetic control strategies. Previous studies have identified important ‘disease resistance single nucleotide polymorphisms (SNPs)’, but how these affect the underlying traits is an unresolved question. Recent advances in computational statistics make it now possible to estimate the effects of SNPs on host traits from epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known about how to effectively design disease transmission experiments or field studies to maximise the precision with which these effects can be estimated. Results In this paper, we develop and validate analytical expressions for the precision of the estimates of SNP effects on the three above host traits for a disease transmission experiment with one or more non-interacting contact groups. Maximising these expressions leads to three distinct ‘experimental’ designs, each specifying a different set of ideal SNP genotype compositions across groups: (a) appropriate for a single contact-group, (b) a multi-group design termed “pure”, and (c) a multi-group design termed “mixed”, where ‘pure’ and ‘mixed’ refer to groupings that consist of individuals with uniformly the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than estimates for infectivity. Whereas the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferred because it uses information from naturally-occurring rather than artificial infections. The same design principles apply to estimates of the epidemiological impact of other categorical fixed effects, such as breed, line, family, sex, or vaccination status. Estimation of SNP effect precisions from a given experimental setup is implemented in an online software tool SIRE-PC . Conclusions Methodology was developed to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables that underlie host susceptibility, infectivity and recoverability. Designs that maximize the precision of estimates were derived.

Markov chain Monte Carlo (MCMC) is widely used for Bayesian inference in models of complex systems. Performance, however, is often unsatisfactory in models with many latent variables due to so-called poor mixing, necessitating the development of application-specific implementations. This paper introduces ‘posterior-based proposals' (PBPs), a new type of MCMC update applicable to a huge class of statistical models (whose conditional dependence structures are represented by directed acyclic graphs). PBPs generate large joint updates in parameter and latent variable space, while retaining good acceptance rates (typically 33%). Evaluation against other approaches (from standard Gibbs/random walk updates to state-of-the-art Hamiltonian and particle MCMC methods) was carried out for widely varying model types: an individual-based model for disease diagnostic test data, a financial stochastic volatility model, a mixed model used in statistical genetics and a population model used in ecology. While different methods worked better or worse in different scenarios, PBPs were found to be either near to the fastest or significantly faster than the next best approach (by up to a factor of 10). PBPs, therefore, represent an additional general purpose technique that can be usefully applied in a wide variety of contexts.

Summary Genetic selection of cattle more resistant to bovine tuberculosis (bTB) may offer a complementary control strategy. Hypothesising underlying non‐additive genetic variation, we present an approach using genome‐wide high density markers to identify genomic loci with dominance effects on bTB resistance and to test previously published regions with heterozygote advantage in bTB. Our data comprised 1151 Holstein–Friesian cows from Northern Ireland, confirmed bTB cases and controls, genotyped with the 700K Illumina BeadChip. Genome‐wide markers were tested for associations between heterozygosity and bTB status using marker‐based relationships. Results were tested for robustness against genetic structure, and the genotypic frequencies of a significant locus were tested for departures from Hardy‐Weinberg equilibrium. Genomic regions identified in our study and in previous publications were tested for dominance effects. Genotypic effects were estimated through ASReml mixed models. A SNP (rs43032684) on chromosome 6 was significant at the chromosome‐wide level, explaining 1.7% of the phenotypic variance. In the controls, there were fewer heterozygotes for rs43032684 (P < 0.01) with the genotypic values suggesting that heterozygosity confers a heterozygote disadvantage. The region surrounding rs43032684 had a significant dominance effect (P < 0.01). SNP rs43032684 resides within a pseudogene with a parental gene involved in macrophage response to infection and within a copy‐number‐variation region previously associated with nematode resistance. No dominance effect was found for the region on chromosome 11, as indicated by a previous candidate region bTB study. These findings require further validation with large‐scale data.

Background: Susceptibility to Mycobacterium bovis infection in cattle is governed in part by host genetics. However, cattle diagnosed as infected with M. bovis display varying signs of pathology. The variation in host response to infection could represent a continuum since time of exposure or distinct outcomes due to differing pathogen handling. The relationships between host genetics and variation in host response and pathological sequelae following M. bovis infection were explored by genotyping 1966 Holstein-Friesian dairy cows at 538,231 SNPs with three distinct phenotypes. These were: single intradermal cervical comparative tuberculin (SICCT) test positives with visible lesions (VLs), SICCT-positives with undetected visible lesions (NVLs) and matched controls SICCT-negative on multiple occasions.Results: Regional heritability mapping identified three loci associated with the NVL phenotype on chromosomes 17, 22 and 23, distinct to the region on chromosome 13 associated with the VL phenotype. The region on chromosome 23 was at genome-wide significance and candidate genes overlapping the mapped window included members of the bovine leukocyte antigen class IIb region, a complex known for its role in immunity and disease resistance. Chromosome heritability analysis attributed variance to six and thirteen chromosomes for the VL and NVL phenotypes, respectively, and four of these chromosomes were found to explain a proportion of the phenotypic variation for both the VL and NVL phenotype. By grouping the M. bovis outcomes (VLs and NVLs) variance was attributed to nine chromosomes. When contrasting the two M. bovis infection outcomes (VLs vs NVLs) nine chromosomes were found to harbour heritable variation. Regardless of the case phenotype under investigation, chromosome heritability did not exceed 8% indicating that the genetic control of bTB resistance consists of variants of small to moderate effect situated across many chromosomes of the bovine genome.Conclusions: These findings suggest the host genetics of M. bovis infection outcomes is governed by distinct and overlapping genetic variants. Thus, variation in the pathology of M. bovis infected cattle may be partly genetically determined and indicative of different host responses or pathogen handling. There may be at least three distinct outcomes following M. bovis exposure in dairy cattle: resistance to infection, infection resulting in pathology or no detectable pathology.

Genetic evaluations for resistance to bovine tuberculosis (bTB) were calculated based on British national data including individual animal tuberculin skin test results, postmortem examination (presence of bTB lesions and bacteriological culture for Mycobacterium bovis), animal movement and location information, production history, and pedigree records. Holstein cows with identified sires in herds with bTB breakdowns (new herd incidents) occurring between the years 2000 and 2014 were considered. In the first instance, cows with a positive reaction to the skin test and a positive postmortem examination were defined as infected. Values of 0 and 1 were assigned to healthy and infected animal records, respectively. Data were analyzed with mixed models. Linear and logit function heritability estimates were 0.092 and 0.172, respectively. In subsequent analyses, breakdowns were split into 2-mo intervals to better model time of exposure and infection in the contemporary group. Intervals with at least one infected individual were retained and multiple intervals within the same breakdown were included. Healthy animal records were assigned values of 0, and infected records a value of 1 in the interval of infection and values reflecting a diminishing probability of infection in the preceding intervals. Heritability and repeatability estimates were 0.115 and 0.699, respectively. Reliabilities and across time stability of the genetic evaluation were improved with the interval model. Subsequently, 2 more definitions of "infected" were analyzed with the interval model: (1) all positive skin test reactors regardless of postmortem examination, and (2) all positive skin test reactors plus nonreactors with positive postmortem examination. Estimated heritability was 0.085 and 0.089, respectively; corresponding repeatability estimates were 0.701 and 0.697. Genetic evaluation reliabilities and across time stability did not change. Correlations of genetic evaluations for bTB with other traits in the current breeding goal were mostly not different from zero. Correlation with the UK Profitable Lifetime Index was moderate, significant, and favorable. Results demonstrated the feasibility of a national genetic evaluation for bTB resistance. Selection for enhanced resistance will have a positive effect on profitability and no antagonistic effects on current breeding goal traits. Official genetic evaluations are now based on the interval model and the last bTB trait definition.

Genetic variation for bovine tuberculosis (bTB) resistance exists in cattle, thus breeding for resistance is a viable complementary control option. bTB infected cattle vary in signs of pathology, suggesting bTB presents as a spectrum of phenotypes. This study investigated the host genetics under-pinning different bTB infection outcomes to increase understanding of biological mechanisms involved in bTB resistance. A case-control design was set up and dairy cows were sampled from Northern Ireland herds. Using the diagnostic skin test and post-mortem examination, infected skin test positive cases were divided into two sub-categories: those with disease (visible lesions detected (VL cases)) and those showing no visible lesions (NVL cases). Resistant controls were those with multiple negative skin test readings. 1966 cows, defined by these 3 phenotypes, were genotyped at 538,231 SNPs. The contribution of each chromosome (BTA) to heritability (h2c) was estimated using a linear mixed model and genomic regions of variation (h2r) were identified using regional heritability (RH) mapping with 100-SNP overlapping windows. Analyses considered controls against each case phenotype. Chromosome heritability analysis revealed a total of 5 and 14 chromosomes respectively contributed to heritability for VL cases (2.1% ≤ h2c ≤ 5.1%) and NVL cases (1.7% ≤ h2c ≤ 7.7%). Of the 5 chromosomes contributing to heritability in VL cases, 4 were also detected in NVL cases. RH mapping identified 1 genomic region in the VL cases at 71.5–71.8 Mb on BTA13 (h2r = 2.1%) and 3 genomic regions in the NVL cases at 19.3–19.8 Mb on BTA17 (h2r = 5.3%), 57.2–57.5 Mb on BTA22 (h2r = 3.9%) and 6.6–7.1 Mb on BTA23 (h2r = 3.5%). Genes associated with these regions were identified. Of interest was the bovine leukocyte antigen (BoLA) class IIb region located at 6.97–7.53 Mb near the region found on BTA23. BoLA genes play a major role in immune response to infection, being involved in processing and presenting of foreign antigens. The results suggest there may be at least two bTB infection outcomes controlled by distinct and overlapping genetic variants. Breeding values can incorporate both bTB infection states as they share underlying chromosomal variance.

Ticks and tick-borne diseases are among the main causes of economic loss in the South African cattle industry through high morbidity and mortality rates. Concerns of the general public regarding chemical residues may tarnish their perceptions of food safety and environmental health when the husbandry of cattle includes frequent use of acaricides to manage ticks. The primary objective of this study was to identify single nucleotide polymorphism (SNP) markers associated with host resistance to ticks in South African Nguni cattle. Tick count data were collected monthly from 586 Nguni cattle reared in four herds under natural grazing conditions over a period of two years. The counts were recorded for six species of ticks attached in eight anatomical locations on the animals and were summed by species and anatomical location. This gave rise to 63 measured phenotypes or traits, with results for 12 of these traits being reported here. Tick count (x) data were transformed using log10(x+1) and the resulting values were examined for normality. DNA was extracted from hair and blood samples and was genotyped using the Illumina BovineSNP50 assay. After quality control (call rate >90%, minor allele frequency >0.02), 40,436 SNPs were retained for analysis. Genetic parameters were estimated and association analysis for tick resistance was carried out using two approaches: a genome-wide association (GWA) analysis using the GenABEL package and a regional heritability mapping (RHM) analysis. The Bonferroni genome-wide (P<0.05) corrected significance threshold was 1.24×10(-6), with 2.47×10(-5) as the suggestive significance threshold (P<0.10) (i.e., one false positive per genome scan) in the GWA analysis. Likelihood ratio test (LRT) thresholds for genome-wide and suggestive significance were 13.5 and 9.15 for the RHM analysis. Six ixodid tick species were identified, with Amblyomma hebraeum (the vector for Heartwater disease) being the dominant species. Heritability estimates (h(2)) from the fitted animal and sire models ranged from 0.02±0.00 to 0.17±0.04 for the transformed tick count data. Several genomic regions harbouring quantitative trait loci (QTL) were identified for different tick count traits by both the GWA and RHM approaches. Three genome-wide significant regions on chromosomes 7, 10 and 19 were identified for total tick count on the head, total body A. hebraeum tick count and total A. hebraeum on the perineum region, respectively. Additional regions significant at the suggestive level were identified on chromosomes 1, 3, 6, 7, 8, 10, 11, 12, 14, 15, 17, 19 and 26 for several of the traits. The GWA approach identified more genomic regions than did the RHM approach. The chromosomal regions identified here as harbouring QTL underlying variation in tick burden form the basis for further analyses to identify specific candidate genes and polymorphisms related to cattle tick resistance and provide the potential for marker-assisted selection in Nguni cattle.

Bayesian statistics provides a framework for the integration of dynamic models with incomplete data to enable inference of model parameters and unobserved aspects of the system under study. An important class of dynamic models is discrete state space, continuous-time Markov processes (DCTMPs). Simulated via the Doob-Gillespie algorithm, these have been used to model systems ranging from chemistry to ecology to epidemiology. A new type of proposal, termed 'model-based proposal' (MBP), is developed for the efficient implementation of Bayesian inference in DCTMPs using Markov chain Monte Carlo (MCMC). This new method, which in principle can be applied to any DCTMP, is compared (using simple epidemiological SIS and SIR models as easy to follow exemplars) to a standard MCMC approach and a recently proposed particle MCMC (PMCMC) technique. When measurements are made on a single-state variable (e.g. the number of infected individuals in a population during an epidemic), model-based proposal MCMC (MBP-MCMC) is marginally faster than PMCMC (by a factor of 2-8 for the tests performed), and significantly faster than the standard MCMC scheme (by a factor of 400 at least). However, when model complexity increases and measurements are made on more than one state variable (e.g. simultaneously on the number of infected individuals in spatially separated subpopulations), MBP-MCMC is significantly faster than PMCMC (more than 100-fold for just four subpopulations) and this difference becomes increasingly large.

Pancreas disease (PD), caused by a salmonid alphavirus (SAV), has a large negative economic and animal welfare impact on Atlantic salmon aquaculture. Evidence for genetic variation in host resistance to this disease has been reported, suggesting that selective breeding may potentially form an important component of disease control. The aim of this study was to explore the genetic architecture of resistance to PD, using survival data collected from two unrelated populations of Atlantic salmon; one challenged with SAV as fry in freshwater (POP 1) and one challenged with SAV as post-smolts in sea water (POP 2). Analyses of the binary survival data revealed a moderate-to-high heritability for host resistance to PD in both populations (fry POP 1 h 2 ~0.5; post-smolt POP 2 h 2 ~0.4). Subsets of both populations were genotyped for single nucleotide polymorphism markers, and six putative resistance quantitative trait loci (QTL) were identified. One of these QTL was mapped to the same location on chromosome 3 in both populations, reaching chromosome-wide significance in both the sire- and dam-based analyses in POP 1, and genome-wide significance in a combined analysis in POP 2. This independently verified QTL explains a significant proportion of host genetic variation in resistance to PD in both populations, suggesting a common underlying mechanism for genetic resistance across lifecycle stages. Markers associated with this QTL are being incorporated into selective breeding programs to improve PD resistance.

This paper considers genetic resistance to infectious disease in sheep, with appropriate comparison with goats, and explores how such variation may be used to assist in disease control. Many studies have attempted to quantify the extent to which host animals differ genetically in their resistance to infection or in the disease side-effects of infection, using either recorded animal pedigrees or information from genetic markers to quantify the genetic variation. Across all livestock species, whenever studies are sufficiently well powered, then genetic variation in disease resistance is usually seen and such evidence is presented here for three infections or diseases of importance to sheep, namely mastitis, foot rot and scrapie. A further class of diseases of importance in most small ruminant production systems, gastrointestinal nematode infections, is outside the scope of this review. Existence of genetic variation implies the opportunity, at least in principle, to select animals for increased resistance, with such selection ideally used as part of an integrated control strategy. For each of the diseases under consideration, evidence for genetic variation is presented, the role of selection as an aid to disease control is outlined and possible side effects of selection in terms of effects in performance, effects on resistance to other diseases and potential parasite/pathogen coevolution risks are considered. In all cases, the conclusion is drawn that selection should work and it should be beneficial, with the main challenge being to define cost effective selection protocols that are attractive to sheep farmers.

The genomic basis of average daily gain (ADG) and Porcine Respiratory and Reproductive Syndrome (PRRS) IgG-antibody measured by ELISA sample-to-positive ratio (S/P), was assessed in crossbred replacement gilts entering commercial farms. S/P at time of entry (S/P-d0), after acclimation (S/P-Acc), and parity 1 (S/P-Par), and ADG during acclimation were analyzed. All traits had low heritability, except S/P-Acc (0.47). Previously identified candidate SNPs for S/P on SSC7 (MARC, ASGA, M1GA, and ALGA) were validated, but effects of the SSC4 WUR SNP on S/P and growth were not significant. Two regions on SSC7 previously associated with S/P, that harbor the four candidate SNPs, were validated. One of the SSC7 regions lost its association when candidate SNPs were included in the model, indicating that these SNPs captured the effect of the region on the trait. Novel regions were associated with ADG, S/P-d0, and S/PAcc.

Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically significant viral diseases facing the global swine industry. Viremia profiles of PRRS virus challenged pigs reflect the severity and progression of infection within the host and provide crucial information for subsequent control measures. In this study we analyse the largest available longitudinal PRRS viremia dataset from an in-vivo experiment. The primary objective was to provide a suitable mathematical description of viremia profiles with biologically meaningful parameters for quantitative analysis of profile characteristics. The Wood’s function, a gamma-type function, and a biphasic extended Wood’s function were fit to the individual profiles using Bayesian inference with a likelihood framework. Using maximum likelihood inference and numerous fit criteria, we established that the broad spectrum of viremia trends could be adequately represented by either uni- or biphasic Wood’s functions. Three viremic categories emerged: cleared (uni-modal and below detection within 42 days post infection(dpi)), persistent (transient experimental persistence over 42 dpi) and rebound (biphasic within 42 dpi). The convenient biological interpretation of the model parameters estimates, allowed us not only to quantify inter-host variation, but also to establish common viremia curve characteristics and their predictability. Statistical analysis of the profile characteristics revealed that persistent profiles were distinguishable within 21 dpi, whereas it is not possible to predict the onset of viremia rebound. Analysis of the neutralizing antibody(nAb) data indicated that there was a ubiquitous strong response to the homologous PRRSV challenge, but high variability in the range of cross-protection of the nAbs. Persistent pigs were found to have a significantly higher nAb cross-protectivity than pigs that either cleared viremia or experienced rebound within 42 dpi. Our study provides novel insights into the nature and degree of variation of hosts’ responses to infection as well as new informative traits for subsequent genomic and dynamic modelling studies. The development of a within-host dynamic model, parameterised by the statistical model outputs, extends the existing model of PRRSV infection dynamics to allow for testing hypothesis surrounding: quasi-species dynamics, virus mutation mechanisms and the latest host genetic insights into the immune response. We aim to combine the insights gained from the statistical model with the results of immunological and genomic studies to develop a dynamic framework that describes how individual components interact. The model’s dynamic nature allows for predictions of virus load, the host’s infection status and immune response over the time-course of infection. Both the statistical and the dynamic mathematical modelling of the within-host dynamics of PRRS virus infections provides insights into the consequences of treatment and control of infection in individual hosts, and allow us to identify important knowledge gaps to be addressed in future studies.

Porcine reproductive and respiratory syndrome (PRRS) is the most economically significant disease impacting pig production in North America, Europe, and Asia, causing reproductive losses such as increased rates of stillbirth and mummified piglets. The objective of this study was to explore the genetic basis of host response to the PRRS virus (PRRSV) in a commercial multiplier sow herd before and after a PRRS outbreak, using antibody response and reproductive traits. Reproductive data comprising number born alive (NBA), number alive at 24 h (NA24), number stillborn (NSB), number born mummified (NBM), proportion born dead (PBD), number born dead (NBD), number weaned (NW), and number of mortalities through weaning (MW) of 5,227 litters from 1,967 purebred Landrace sows were used along with a pedigree comprising 2,995 pigs. The PRRS outbreak date was estimated from rolling averages of farrowing traits and was used to split the data into a pre-PRRS phase and a PRRS phase. All 641 sows in the herd during the outbreak were blood sampled 46 d after the estimated outbreak date and were tested for anti-PRRSV IgG using ELISA (sample-to-positive [S/P] ratio). Genetic parameters of traits were estimated separately for the pre-PRRS and PRRS phase data sets. Sows were genotyped using the PorcineSNP60 BeadChip, and genome-wide association studies (GWAS) were performed using method Bayes B. Heritability estimates for reproductive traits ranged from 0.01 (NBM) to 0.12 (NSB) and from 0.01 (MW) to 0.12 (NBD) for the pre-PRRS and PRRS phases, respectively. S/P ratio had heritability (0.45) and strong genetic correlations with most traits, ranging from -0.72 (NBM) to 0.73 (NBA). In the pre-PRRS phase, regions associated with NSB and PBD explained 1.6% and 3% of the genetic variance, respectively. In the PRRS phase, regions associated with NBD, NSB, and S/P ratio explained 0.8%, 11%, and 50.6% of the genetic variance, respectively. For S/P ratio, 2 regions on SSC 7 (SSC7) separated by 100 Mb explained 40% of the genetic variation, including a region encompassing the major histocompatibility complex, which explained 25% of the genetic variance. These results indicate a significant genomic component associated with PRRSV antibody response and NSB in this data set. Also, the high heritability and genetic correlation estimates for S/P ratio during the PRRS phase suggest that S/P ratio could be used as an indicator of the impact of PRRS on reproductive traits.