Quantitative Veterinary Epidemiology, Wageningen University and Research, 6700 AH Wageningen, the Netherlands; Animal Breeding and Genomics, Wageningen University and Research, 6700 AH Wageningen, the Netherlands.
Quantitative Veterinary Epidemiology, Wageningen University and Research, 6700 AH Wageningen, the Netherlands.
J Dairy Sci. 2019 Jul;102(7):6248-6262. doi: 10.3168/jds.2018-15876. Epub 2019 May 15.
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.
选择和培育可以用于对抗家畜传染病的传播。疾病在人群中的流行程度取决于动物的易感性和传染性。了解这些性状的遗传背景将有助于更有效地选择发病率较低的动物。我们通过全基因组关联研究（GWAS），利用基于流行病学理论的简单线性混合模型或广义线性混合模型，研究了奶牛数字性皮炎（DD）的宿主易感性和感染性的遗传背景，DD 是一种地方性传染性蹄病。在 12 个荷兰奶牛场的 1513 头荷斯坦-弗里森奶牛中，每两周对 DD 感染状况和等级（M0 至 M4.1）进行一次评分，共 11 次；其中 1401 头奶牛使用 75k SNP 芯片进行了基因分型。我们对 10 个宿主疾病状态性状进行了线性混合模型的 GWAS，并对宿主易感性和宿主感染性的 SNP 效应进行了广义线性混合模型（GLMM）与互补对数对数链接函数（GLMM）的拟合，同时考虑了易感牛接触传染性牛群的变化。使用 GLMM 检测到 4 个提示 SNP（错误发现率<0.20），其中 2 个位于第 1 和第 14 号染色体上观察到牛有活动性病变的比例，1 个位于第 1 号染色体上观察到牛至少有一个蹄子有 M2 病变的比例（与观察到有活动性病变的比例相同的 SNP），以及 1 个位于第 10 号染色体上观察到牛至少有一个蹄子有 M4.1 病变的比例。遗传力估计值范围为 0.09 至 0.37。使用 GLMM，我们未检测到有意义或提示性 SNP。线性模型分析的疾病状态 SNP 效应与 GLMM 中宿主易感性 SNP 效应的相关系数仅为 0.70，表明这两个模型都捕捉到了部分不同的效应。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，因此尽管没有提示性关联，但 GLMM 的结果可能更可靠。我们预计，通过 GLMM 扩展，通过环境更好地解释感染性的全基因组变异，SNP 效应的准确性可能会提高。
