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

Floor Biemans*, Mart C.M. De Jong, Piter Bijma

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number67
JournalGenetics Selection Evolution
Volume51
Issue number1
DOIs
Publication statusPublished - 20 Nov 2019

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