A genome-wide association study for susceptibility and infectivity of Holstein Friesian dairy cattle to digital dermatitis

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Selection and breeding can be used to fight transmission of infectious diseases in livestock. The prevalence in a population depends on the susceptibility and infectivity of the animals. Knowledge on the genetic background of those traits would facilitate efficient selection for lower disease prevalence. We investigated the genetic background of host susceptibility and infectivity for digital dermatitis (DD), an endemic infectious claw disease in dairy cattle, with a genome-wide association study (GWAS), using either a simple linear mixed model or a generalized linear mixed model based on epidemiological theory. In total, 1,513 Holstein-Friesian cows of 12 Dutch dairy farms were scored for DD infection status and class (M0 to M4.1) every 2 wk for 11 times; 1,401 of these cows were genotyped with a 75k SNP chip. We performed a GWAS with a linear mixed model on 10 host disease status traits, and with a generalized linear mixed model with a complementary log-log link function (GLMM) on the probability that a cow would get infected between 2 scorings. With the GLMM, we fitted SNP effects for host susceptibility and host infectivity, while taking the variation in exposure of the susceptible cow to infectious herd mates into account. With the linear model we detected 4 suggestive SNP (false discovery rate < 0.20), 2 for the fraction of observations a cow had an active lesion on chromosomes 1 and 14, one for the fraction of observations a cow had an M2 lesion on at least one claw on chromosome 1 (the same SNP as for the fraction of observations with an active lesion), and one for the fraction of observations a cow had an M4.1 lesion on at least one claw on chromosome 10. Heritability estimates ranged from 0.09 to 0.37. With the GLMM we did not detect significant nor suggestive SNP. The SNP effects on disease status analyzed with the linear model had a correlation coefficient of only 0.70 with SNP effects on susceptibility of the GLMM, indicating that both models capture partly different effects. Because the GLMM better accounts for the epidemiological mechanisms determining individual disease status and for the distribution of the y-variable, results of the GLMM may be more reliable, despite the absence of suggestive associations. We expect that with an extended GLMM that better accounts for the full genetic variation in infectivity via the environment, the accuracy of SNP effects may increase.

LanguageEnglish
Pages6248-6262
JournalJournal of Dairy Science
Volume102
Issue number7
Early online date15 May 2019
DOIs
Publication statusPublished - Jul 2019

Fingerprint

Digital Dermatitis
Genome-Wide Association Study
dermatitis
dairy cattle
Single Nucleotide Polymorphism
Holstein
pathogenicity
cows
Linear Models
Hoof and Claw
claws
chromosomes
Chromosomes, Human, Pair 1
lesions (animal)
genetic background
linear models
foot diseases
Chromosomes, Human, Pair 14
disease prevalence
Chromosomes, Human, Pair 10

Keywords

  • claw health
  • generalized linear mixed model
  • genetic parameter
  • heritability
  • transmission

Cite this

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title = "A genome-wide association study for susceptibility and infectivity of Holstein Friesian dairy cattle to digital dermatitis",
abstract = "Selection and breeding can be used to fight transmission of infectious diseases in livestock. The prevalence in a population depends on the susceptibility and infectivity of the animals. Knowledge on the genetic background of those traits would facilitate efficient selection for lower disease prevalence. We investigated the genetic background of host susceptibility and infectivity for digital dermatitis (DD), an endemic infectious claw disease in dairy cattle, with a genome-wide association study (GWAS), using either a simple linear mixed model or a generalized linear mixed model based on epidemiological theory. In total, 1,513 Holstein-Friesian cows of 12 Dutch dairy farms were scored for DD infection status and class (M0 to M4.1) every 2 wk for 11 times; 1,401 of these cows were genotyped with a 75k SNP chip. We performed a GWAS with a linear mixed model on 10 host disease status traits, and with a generalized linear mixed model with a complementary log-log link function (GLMM) on the probability that a cow would get infected between 2 scorings. With the GLMM, we fitted SNP effects for host susceptibility and host infectivity, while taking the variation in exposure of the susceptible cow to infectious herd mates into account. With the linear model we detected 4 suggestive SNP (false discovery rate < 0.20), 2 for the fraction of observations a cow had an active lesion on chromosomes 1 and 14, one for the fraction of observations a cow had an M2 lesion on at least one claw on chromosome 1 (the same SNP as for the fraction of observations with an active lesion), and one for the fraction of observations a cow had an M4.1 lesion on at least one claw on chromosome 10. Heritability estimates ranged from 0.09 to 0.37. With the GLMM we did not detect significant nor suggestive SNP. The SNP effects on disease status analyzed with the linear model had a correlation coefficient of only 0.70 with SNP effects on susceptibility of the GLMM, indicating that both models capture partly different effects. Because the GLMM better accounts for the epidemiological mechanisms determining individual disease status and for the distribution of the y-variable, results of the GLMM may be more reliable, despite the absence of suggestive associations. We expect that with an extended GLMM that better accounts for the full genetic variation in infectivity via the environment, the accuracy of SNP effects may increase.",
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A genome-wide association study for susceptibility and infectivity of Holstein Friesian dairy cattle to digital dermatitis. / Biemans, F.; de Jong, M.C.M.; Bijma, P.

In: Journal of Dairy Science, Vol. 102, No. 7, 07.2019, p. 6248-6262.

Research output: Contribution to journalArticleAcademicpeer-review

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N2 - Selection and breeding can be used to fight transmission of infectious diseases in livestock. The prevalence in a population depends on the susceptibility and infectivity of the animals. Knowledge on the genetic background of those traits would facilitate efficient selection for lower disease prevalence. We investigated the genetic background of host susceptibility and infectivity for digital dermatitis (DD), an endemic infectious claw disease in dairy cattle, with a genome-wide association study (GWAS), using either a simple linear mixed model or a generalized linear mixed model based on epidemiological theory. In total, 1,513 Holstein-Friesian cows of 12 Dutch dairy farms were scored for DD infection status and class (M0 to M4.1) every 2 wk for 11 times; 1,401 of these cows were genotyped with a 75k SNP chip. We performed a GWAS with a linear mixed model on 10 host disease status traits, and with a generalized linear mixed model with a complementary log-log link function (GLMM) on the probability that a cow would get infected between 2 scorings. With the GLMM, we fitted SNP effects for host susceptibility and host infectivity, while taking the variation in exposure of the susceptible cow to infectious herd mates into account. With the linear model we detected 4 suggestive SNP (false discovery rate < 0.20), 2 for the fraction of observations a cow had an active lesion on chromosomes 1 and 14, one for the fraction of observations a cow had an M2 lesion on at least one claw on chromosome 1 (the same SNP as for the fraction of observations with an active lesion), and one for the fraction of observations a cow had an M4.1 lesion on at least one claw on chromosome 10. Heritability estimates ranged from 0.09 to 0.37. With the GLMM we did not detect significant nor suggestive SNP. The SNP effects on disease status analyzed with the linear model had a correlation coefficient of only 0.70 with SNP effects on susceptibility of the GLMM, indicating that both models capture partly different effects. Because the GLMM better accounts for the epidemiological mechanisms determining individual disease status and for the distribution of the y-variable, results of the GLMM may be more reliable, despite the absence of suggestive associations. We expect that with an extended GLMM that better accounts for the full genetic variation in infectivity via the environment, the accuracy of SNP effects may increase.

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