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

Biemans, Floor

doi:10.18174/443357

Cited by 4 publications

(4 citation statements)

References 78 publications

(132 reference statements)

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“…Analysis of incomplete epidemic data to draw inferences on epidemiological parameters is well established [25,26]. However, analysing such data to draw joint inferences on both the disease epidemiology and host genetic variation has proven challenging [24,27]. Recent studies have expanded conventional quantitative genetics threshold models to enable joint genetic evaluation of cattle susceptibility to, and recoverability from, mastitis [28,29], which led to identification of novel SNPs and candidate genes associated with these traits [18].…”

Section: Introductionmentioning

confidence: 99%

Estimating individuals’ genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data

et al. 2020

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Individuals differ widely in their contribution to the spread of infection within and across populations. Three key epidemiological host traits affect infectious disease spread: susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection to others) and recoverability (propensity to recover quickly). Interventions aiming to reduce disease spread may target improvement in any one of these traits, but the necessary statistical methods for obtaining risk estimates are lacking. In this paper we introduce a novel software tool called SIRE (standing for “Susceptibility, Infectivity and Recoverability Estimation”), which allows for the first time simultaneous estimation of the genetic effect of a single nucleotide polymorphism (SNP), as well as non-genetic influences on these three unobservable host traits. SIRE implements a flexible Bayesian algorithm which accommodates a wide range of disease surveillance data comprising any combination of recorded individual infection and/or recovery times, or disease diagnostic test results. Different genetic and non-genetic regulations and data scenarios (representing realistic recording schemes) were simulated to validate SIRE and to assess their impact on the precision, accuracy and bias of parameter estimates. This analysis revealed that with few exceptions, SIRE provides unbiased, accurate parameter estimates associated with all three host traits. For most scenarios, SNP effects associated with recoverability can be estimated with highest precision, followed by susceptibility. For infectivity, many epidemics with few individuals give substantially more statistical power to identify SNP effects than the reverse. Importantly, precise estimates of SNP and other effects could be obtained even in the case of incomplete, censored and relatively infrequent measurements of individuals’ infection or survival status, albeit requiring more individuals to yield equivalent precision. SIRE represents a new tool for analysing a wide range of experimental and field disease data with the aim of discovering and validating SNPs and other factors controlling infectious disease transmission.

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Section: Introductionmentioning

confidence: 99%

Estimating individuals’ genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data

et al. 2020

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“…Anche et al [2] have described how estimated breeding values (EBV) for susceptibility and infectivity can be combined into a single EBV for R 0 . Furthermore, the accuracy of EBV for susceptibility improves when infectivity is included in the model for genetic evaluation [34,35]. The inclusion of infectivity in genetic evaluation requires testing designs that differ from the currently used challenge tests, as discussed in the following.…”

Section: Genetic Evaluation Of Rmentioning

confidence: 99%

The economic value of R0 for selective breeding against microparasitic diseases

Janssen

Bijma

2020

Genet Sel Evol

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Background: Microparasitic diseases are caused by bacteria and viruses. Genetic improvement of resistance to microparasitic diseases in breeding programs is desirable and should aim at reducing the basic reproduction ratio R 0 . Recently, we developed a method to derive the economic value of R 0 for macroparasitic diseases. In epidemiological models for microparasitic diseases, an animal's disease status is treated as infected or not infected, resulting in a definition of R 0 that differs from that for macroparasitic diseases. Here, we extend the method for the derivation of the economic value of R 0 to microparasitic diseases. Methods: When R 0 ≤ 1 , the economic value of R 0 is zero because the disease is very rare. When R 0 . is higher than 1, genetic improvement of R 0 can reduce expenditures on vaccination if vaccination induces herd immunity, or it can reduce production losses due to disease. When vaccination is used to achieve herd immunity, expenditures are proportional to the critical vaccination coverage, which decreases with R 0 . The effect of R 0 on losses is considered separately for epidemic and endemic disease. Losses for epidemic diseases are proportional to the probability and size of major epidemics. Losses for endemic diseases are proportional to the infected fraction of the population at the endemic equilibrium. Results:When genetic improvement reduces expenditures on vaccination, expenditures decrease with R 0 at an increasing rate. When genetic improvement reduces losses in epidemic or endemic diseases, losses decrease with R 0 at an increasing rate. Hence, in all cases, the economic value of R 0 increases as R 0 decreases towards 1. Discussion: R 0 and its economic value are more informative for potential benefits of genetic improvement than heritability estimates for survival after a disease challenge. In livestock, the potential for genetic improvement is small for epidemic microparasitic diseases, where disease control measures limit possibilities for phenotyping. This is not an issue in aquaculture, where controlled challenge tests are performed in dedicated facilities. If genetic evaluations include infectivity, genetic gain in R 0 can be accelerated but this would require different testing designs. Conclusions: When R 0 ≤ 1 , its economic value is zero. The economic value of R 0 is highest at low values of R 0 and approaches zero at high values of R 0 . © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article' Publisher's NoteSpringer Nature remains neutral with ...

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“…Biemans et al. ( 2018 ) developed a generalized linear mixed model for bovine digital dermatitis (DD). Bovine DD is an endemic infectious claw disease, found predominantly in the hind feet of dairy cattle and was first documented by Cheli and Mortellaro ( 1974 ).…”

Section: Introductionmentioning

confidence: 99%

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

Kulkarni

Biemans

Jong

et al. 2021

J Animal Breeding Genetics

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

Abstract: Biemans, F. (2018). Transmission of digital dermatitis in dairy cattle: Population dynamics and host quantitative genetics. PhD thesis,

Cited by 4 publications

References 78 publications

Estimating individuals’ genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data

Estimating individuals’ genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data

The economic value of R0 for selective breeding against microparasitic diseases

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

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