A genome-wide association study for susceptibility and infectivity of Holstein Friesian dairy cattle to digital dermatitis
A genome-wide association study for susceptibility and infectivity of Holstein Friesian dairy cattle to digital dermatitis
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DOI：
10.3168/jds.2018-15876
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发表时间：
2019-07-01
影响因子：
3.5
通讯作者：
Bijma, P.
中科院分区：
文献类型：
--
作者：
Biemans, F.;de Jong, M. C. M.;Bijma, P.
选择和育种可用于防止传染病在牲畜中的传播。人群中的流行率取决于动物的易感性和传染性。了解这些性状的遗传背景有助于进行有效的选择，以降低疾病流行率。本文采用全基因组关联研究（GWAS），基于流行病学理论，采用简单线性混合模型和广义线性混合模型，研究了奶牛地方性传染性爪病--指皮炎（DD）的宿主易感性和传染性的遗传背景。总共对12个荷兰奶牛场的1，513头荷斯坦-弗里斯兰奶牛进行了11次DD感染状态和分类（M0至M4.1）评分，每2周一次;其中1，401头奶牛使用75 k SNP芯片进行基因分型。我们使用线性混合模型对10种宿主疾病状态特征进行了GWAS，并使用具有互补双对数链接函数（GLMM）的广义线性混合模型对奶牛在2个评分之间被感染的概率进行了GWAS。使用GLMM，我们拟合了SNP对宿主易感性和宿主感染性的影响，同时考虑了易感奶牛对感染性畜群伴侣的暴露变化。用线性模型检测到4个提示性SNP（错误发现率< 0.20），2为奶牛在1号和14号染色体上有活动性病变的观察分数，1为奶牛在1号染色体上至少一个爪上有M2病变的观察分数（与具有活动性病变的观察结果的分数相同的SNP），并且一个针对奶牛在染色体10上的至少一个爪上具有M4.1病变的观察结果的分数。遗传力估计值范围为0.09至0.37。使用GLMM，我们没有检测到显著的或提示性的SNP。用线性模型分析的SNP对疾病状态的影响与SNP对GLMM易感性的影响的相关系数仅为0.70，表明两种模型捕获部分不同的影响。由于GLMM更好地解释了决定个体疾病状态的流行病学机制和y变量的分布，因此GLMM的结果可能更可靠，尽管缺乏提示性关联。我们预计，随着扩展的GLMM更好地解释了通过环境的感染性的全部遗传变异，SNP效应的准确性可能会增加。
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, arid 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 arid 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 riot 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.