A model to estimate effects of SNPs on host susceptibility and infectivity for an endemic infectious disease

International audienceAbstractBackgroundInfectious diseases in farm animals affect animal health, decrease animal welfare and can affect human health. Selection and breeding of host individuals with desirable traits regarding infectious diseases can help to fight disease transmission, which is affected by two types of (genetic) traits: host susceptibility and host infectivity. Quantitative genetic studies on infectious diseases generally connect an individual’s disease status to its own genotype, and therefore capture genetic effects on susceptibility only. However, they usually ignore variation in exposure to infectious herd mates, which may limit the accuracy of estimates of genetic effects on susceptibility. Moreover, genetic effects on infectivity will exist as well. Thus, to design optimal breeding strategies, it is essential that genetic effects on infectivity are quantified. Given the potential importance of genetic effects on infectivity, we set out to develop a model to estimate the effect of single nucleotide polymorphisms (SNPs) on both host susceptibility and host infectivity. To evaluate the quality of the resulting SNP effect estimates, we simulated an endemic disease in 10 groups of 100 individuals, and recorded time-series data on individual disease status. We quantified bias and precision of the estimates for different sizes of SNP effects, and identified the optimum recording interval when the number of records is limited.ResultsWe present a generalized linear mixed model to estimate the effect of SNPs on both host susceptibility and host infectivity. SNP effects were on average slightly underestimated, i.e. estimates were conservative. Estimates were less precise for infectivity than for susceptibility. Given our sample size, the power to estimate SNP effects for susceptibility was 100% for differences between genotypes of a factor 1.56 or more, and was higher than 60% for infectivity for differences between genotypes of a factor 4 or more. When disease status was recorded 11 times on each animal, the optimal recording interval was 25 to 50% of the average infectious period.ConclusionsOur model was able to estimate genetic effects on susceptibility and infectivity. In future genome-wide association studies, it may serve as a starting point to identify genes that affect disease transmission and disease prevalence

Genetic parameters and genomic breeding values for digital dermatitis in Holstein Friesian dairy cattle: host susceptibility, infectivity and the basic reproduction ratio

International audienceBackground : For infectious diseases, the probability that an animal gets infected depends on its own susceptibility, and on the number of infectious herd mates and their infectivity. Together with the duration of the infectious period, susceptibility and infectivity determine the basic reproduction ratio of the disease ( R0 ). R0 is the average number of secondary cases caused by a typical infectious individual in an otherwise uninfected population. An infectious disease dies out when R0 is lower than 1. Thus, breeding strategies that aim at reducing disease prevalence should focus on reducing R0 , preferably to a value lower than 1. In animal breeding, however, R0 has received little attention. Here, we estimate the additive genetic variance in host susceptibility, host infectivity, and R0 for the endemic claw disease digital dermatitis (DD) in Holstein Friesian dairy cattle, and estimate genomic breeding values (GEBV ) for these traits. We recorded DD disease status of both hind claws of 1513 cows from 12 Dutch dairy farms, every 2 weeks, 11 times. The genotype data consisted of 75,904 single nucleotide polymorphisms (SNPs) for 1401 of the cows. We modelled the probability that a cow got infected between recordings, and compared four generalized linear mixed models. All models included a genetic effect for susceptibility; Models 2 and 4 also included a genetic effect for infectivity, while Models 1 and 2 included a farm*period interaction. We corrected for variation in exposure to infectious herd mates via an offset.Results : GEBV for R0 from the model that included genetic effects for susceptibility only had an accuracy of ~ 0.39 based on cross-validation between farms, which is very high given the limited amount of data and the complexity of the trait. Models with a genetic effect for infectivity showed a larger bias, but also a slightly higher accuracy of GEBV. Additive genetic standard deviation for R0 was large, i.e. ~ 1.17, while the mean R0 was 2.36.Conclusions : GEBV for R0 showed substantial variation. The mean R0 was only about one genetic standard deviation greater than 1. These results suggest that lowering DD prevalence by selective breeding is promising

The effect of risk-based trading and within-herd measures on Mycobacterium avium subspecies paratuberculosis spread within and between Irish dairy herds

Johne’s disease (bovine paratuberculosis) is an endemic disease caused by Mycobacterium avium subspecies paratuberculosis (Map). Map is transmitted between herds primarily through movement of infected but undetected animals. Within infected herds, possible control strategies include improving herd hygiene by reducing calf exposure to faeces from cows, reducing stress in cows resulting in a longer latently infected period where shedding is minimal, or culling highly test-positive cows soon after detection. Risk-based trading can be a strategy to reduce the risk that Map spreads between herds. Our objective was to assess whether within-herd measures combined with risk-based trading could effectively control Map spread within and between dairy cattle herds in Ireland. We used a stochastic individual-based and between-herd mechanistic epidemiological model to simulate Map transmission. Movement and herd demographic data were available from 1st January 2009–31st December 2018. In total, 13,353 herds, with 4,494,768 dairy female animals, and 72,991 bulls were included in our dataset. The movement dataset consisted of 2,304,149 animal movements. For each herd, a weekly indicator was calculated that reflected the probability that the herd was free from infection. The indicator value increased when a herd tested negative, decreased when animals were introduced into a herd, and became 0 when a herd tested positive. Based on this indicator value, four Johne’s assurance statuses were distinguished: A) ≥ 0.7 – 1.0, B) ≥ 0.3 – 0.0 – < 0.3, and D) 0.0. A is the highest and D the lowest Johne’s assurance status. With risk-based trading some of the observed movements between herds were redirected based on Johne’s assurance status with the aim of reducing the risk that a non-infected herd acquired an infected animal. Risk-based trading effectively reduced the increase in herd prevalence over a 10-year-period in Ireland: from 50% without risk-based trading to 42% with risk-based trading in the metapopulation only, and 26% when external purchases were risk-based as well. However, for risk-based trading to be effective, a high percentage of dairy herds had to participate. The most important within-herd measures were improved herd hygiene and early culling of highly infectious cows. These measures reduced both herd and within-herd prevalence compared to the reference scenario. Combining risk-based trading with within-herd measures reduced within-herd prevalence even more effectively.Department of Agriculture, Food and the Marin

Transmission of digital dermatitis in dairy cattle: population dynamics and host quantitative genetics

Susceptibility, infectivity, the contact rate, and the duration of the infectious period together determine the basic reproduction ratio (R0). The R0 is the average number of secondary cases caused by a typical infectious individual in a fully susceptible population. It determines the ability of an infection to establish itself in a population. The threshold value is one; if R¬0 1 a major outbreak is possible, and sometimes such a disease may persist in a population. For endemic diseases in homogeneous populations, the prevalence in the equilibrium follows from R0 as 1-1/R_0 . Breeding strategies that aim to reduce the prevalence of endemic diseases should thus aim to reduce R0. Because R0 depends on both susceptibility and infectivity of the host population, genetic variation in both those traits should be taken into account. This thesis focusses on Digital Dermatitis (DD) in dairy cattle. DD is an endemic infectious claw disease associated with lameness. We collected time-series data on individual disease that might facilitate genetic selection against DD. In this thesis, we investigated transmission dynamics for DD and estimated genetic effects for both host susceptibility and host infectivity. We proposed a generalized linear mixed model to estimate SNP effects on both host susceptibility and host infectivity from time-series data on individual disease status. The model accounted for variation in exposure of susceptible individuals to infectious group mates, and for the infectivity genotypes of those group mates. The power to detect SNP effects was high for susceptibility but lower for infectivity. We applied the model to field data on DD to investigate the contribution of different disease classes to R0. The estimated R0 was 2.36, to which the class with irregular skin contributed 88.5%. Genomic estimated breeding values for R0 ranged from 0.62 to 6.68 with an accuracy of ~0.6. There were 135 SNPs with a suggestive association for several host susceptibility traits, and heritability estimates for these traits ranged from 0.09 to 0.37. These results show that genetic selection against DD is very promising; there is substantial heritable variation and a meaningful accuracy can be obtained from a limited amount of data

A stochastic model of the within-herd transmission of paratuberculosis in an Irish dairy herd

National audienc

A model to estimate effects of SNPs on host susceptibility and infectivity for an endemic infectious disease

Background: Infectious diseases in farm animals affect animal health, decrease animal welfare and can affect human health. Selection and breeding of host individuals with desirable traits regarding infectious diseases can help to fight disease transmission, which is affected by two types of (genetic) traits: host susceptibility and host infectivity. Quantitative genetic studies on infectious diseases generally connect an individual's disease status to its own genotype, and therefore capture genetic effects on susceptibility only. However, they usually ignore variation in exposure to infectious herd mates, which may limit the accuracy of estimates of genetic effects on susceptibility. Moreover, genetic effects on infectivity will exist as well. Thus, to design optimal breeding strategies, it is essential that genetic effects on infectivity are quantified. Given the potential importance of genetic effects on infectivity, we set out to develop a model to estimate the effect of single nucleotide polymorphisms (SNPs) on both host susceptibility and host infectivity. To evaluate the quality of the resulting SNP effect estimates, we simulated an endemic disease in 10 groups of 100 individuals, and recorded time-series data on individual disease status. We quantified bias and precision of the estimates for different sizes of SNP effects, and identified the optimum recording interval when the number of records is limited. Results: We present a generalized linear mixed model to estimate the effect of SNPs on both host susceptibility and host infectivity. SNP effects were on average slightly underestimated, i.e. estimates were conservative. Estimates were less precise for infectivity than for susceptibility. Given our sample size, the power to estimate SNP effects for susceptibility was 100% for differences between genotypes of a factor 1.56 or more, and was higher than 60% for infectivity for differences between genotypes of a factor 4 or more. When disease status was recorded 11 times on each animal, the optimal recording interval was 25 to 50% of the average infectious period. Conclusions: Our model was able to estimate genetic effects on susceptibility and infectivity. In future genome-wide association studies, it may serve as a starting point to identify genes that affect disease transmission and disease prevalence.</p

On the origin of the genetic variation in infectious disease prevalence: Genetic analysis of disease status versus infections for Digital Dermatitis in Dutch dairy cattle.

The purpose of this study was to investigate the origin of the genetic variation in the prevalence of bovine digital dermatitis (DD) by comparing a genetic analysis of infection events to a genetic analysis of disease status. DD is an important endemic infectious disease affecting the claws of cattle. For disease status, we analysed binary data on individual disease status (0,1; indicating being free versus infected), whereas for infections, we analysed binary data on disease transmission events (1,0; indicating becoming infected or not). The analyses of the two traits were compared using cross-validation. The analysis of disease status captures a combination of genetic variation in disease susceptibility and the ability of individuals to recover, whereas the analysis of infections captures genetic variation in susceptibility only. Estimated genetic variances for both traits indicated substantial genetic variation. The GEBV for disease status and infections correlated with only 0.60, indicating that both models indeed capture distinct information. Together, these results suggest the presence of genetic variation not only in disease susceptibility, but also in the ability of individuals to recover from DD. We argue that the presence of genetic variation in recovery implies that breeders should distinguish between infected individuals versus infectious individuals. This is because epidemiological theory shows that selection for recovery is effective only when it targets recovery from being infectious

Genetic parameters and genomic breeding values for digital dermatitis in Holstein Friesian dairy cattle: Host susceptibility, infectivity and the basic reproduction ratio

Background: For infectious diseases, the probability that an animal gets infected depends on its own susceptibility, and on the number of infectious herd mates and their infectivity. Together with the duration of the infectious period, susceptibility and infectivity determine the basic reproduction ratio of the disease ($R_{0}$ R 0). $R_{0}$ R 0 is the average number of secondary cases caused by a typical infectious individual in an otherwise uninfected population. An infectious disease dies out when $R_{0}$ R 0 is lower than 1. Thus, breeding strategies that aim at reducing disease prevalence should focus on reducing $R_{0}$ R 0, preferably to a value lower than 1. In animal breeding, however, $R_{0}$ R 0 has received little attention. Here, we estimate the additive genetic variance in host susceptibility, host infectivity, and $R_{0}$ R 0 for the endemic claw disease digital dermatitis (DD) in Holstein Friesian dairy cattle, and estimate genomic breeding values (GEBV) for these traits. We recorded DD disease status of both hind claws of 1513 cows from 12 Dutch dairy farms, every 2 weeks, 11 times. The genotype data consisted of 75,904 single nucleotide polymorphisms (SNPs) for 1401 of the cows. We modelled the probability that a cow got infected between recordings, and compared four generalized linear mixed models. All models included a genetic effect for susceptibility; Models 2 and 4 also included a genetic effect for infectivity, while Models 1 and 2 included a farm*period interaction. We corrected for variation in exposure to infectious herd mates via an offset. Results: GEBV for $R_{0}$ R 0 from the model that included genetic effects for susceptibility only had an accuracy of ~ 0.39 based on cross-validation between farms, which is very high given the limited amount of data and the complexity of the trait. Models with a genetic effect for infectivity showed a larger bias, but also a slightly higher accuracy of GEBV. Additive genetic standard deviation for $R_{0}$ R 0 was large, i.e. ~ 1.17, while the mean $R_{0}$ R 0 was 2.36. Conclusions: GEBV for $R_{0}$ R 0 showed substantial variation. The mean $R_{0}$ R 0 was only about one genetic standard deviation greater than 1. These results suggest that lowering DD prevalence by selective breeding is promising.</p

Digital Dermatitis in dairy cattle : The contribution of different disease classes to transmission

Digital Dermatitis (DD) is a claw disease mainly affecting the hind feet of dairy cattle. Digital Dermatitis is an infectious disease, transmitted via the environment, where the infectious "agent" is a combination of bacteria. The standardized classification for DD lesions developed by Döpfer et al. (1997) and extended by Berry et al. (2012) has six distinct classes: healthy (M0), an active granulomatous area of 0-2 cm (M1), an ulcerative lesion of >2 cm (M2), an ulcerative lesion covered by a scab (M3), alteration of the skin (M4), and a combination of M4 and M1 (M4.1).We hypothesize that classes M1, M2, M3, M4, and M4.1 are the potentially infectious classes that can contribute to the basic reproduction ratio (R0), the average number of new infections caused by a typical infected individual. Here, we determine differences in infectivity between the classes, the sojourn time in each of the classes, and the contribution of each class to R0. The analysis is based on data from twelve farms in the Netherlands that were visited every two weeks, eleven times.We found that 93.89% of the transitions from M0 was observed as a transition to class M4, and feet with another class-at-infection rapidly transitioned to class M4. As a consequence, about 70% of the infectious time was spent in class M4. Transmission rate parameters of class-at-infection M1, M2, M3, and M4 were not significantly different from each other, but differed from class-at-infection M4.1. However, due to the relative large amount of time spend in class M4, regardless of the class-at-infection, R0 was almost completely determined by this class. The R0 was 2.36, to which class-at-infection M4 alone contributed 88.5%.Thus, M4 lesions should be prevented to lower R0 to a value below one, while painful M2 lesions should be prevented for animal welfare reasons