A model to estimate effects of SNPs on host susceptibility and infectivity for an endemic infectious disease
A model to estimate effects of SNPs on host susceptibility and infectivity for an endemic infectious disease
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DOI：
10.1186/s12711-017-0327-0
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发表时间：
2017-06-30
影响因子：
4.1
通讯作者：
Bijma, Piter
中科院分区：
文献类型：
--
作者：
Biemans, Floor;de Jong, Mart C. M.;Bijma, Piter
背景：家畜传染病影响动物健康，降低动物福利，并可影响人类健康。选择和培育具有感染性疾病所需性状的宿主个体可以帮助对抗疾病传播，这受到两种类型的（遗传）性状的影响：宿主易感性和宿主感染性。传染病的定量遗传学研究通常将个体的疾病状态与其自身的基因型联系起来，因此仅捕获遗传对易感性的影响。然而，他们通常忽略了暴露于传染性畜群中的变异，这可能会限制易感性遗传效应估计的准确性。此外，遗传对传染性的影响也将存在。因此，为了设计最佳的育种策略，量化遗传对感染性的影响是至关重要的。鉴于遗传效应对感染性的潜在重要性，我们着手开发一个模型来估计单核苷酸多态性（SNPs）对宿主易感性和宿主感染性的影响。为了评估SNP效应估计的质量，我们模拟了10组100人的地方病，并记录了个体疾病状态的时间序列数据。我们量化了不同大小的SNP效应的估计偏差和精度，并确定了最佳的记录间隔时，记录的数量是limited.Results：我们提出了一个广义线性混合模型来估计SNP对宿主易感性和宿主感染性的影响。SNP效应平均略被低估，即。e.估计是保守的。传染性的估计不如易感性精确。考虑到我们的样本量，对于因子为1.56或更高的基因型之间的差异，估计SNP对易感性影响的能力为100%，对于因子为4或更高的基因型之间的差异，估计SNP对感染性影响的能力高于60%。当疾病状态记录在每只动物上11次时，最佳记录间隔为平均感染期的25 - 50%。结论：我们的模型能够估计易感性和传染性的遗传效应。在未来的全基因组关联研究中，它可以作为识别影响疾病传播和疾病流行的基因的起点。
Background: Infectious diseases in farm animals affect animal health, decrease animal welfare and can affect human health. Selection and breeding of host individuals with desirable traits regarding infectious diseases can help to fight disease transmission, which is affected by two types of (genetic) traits: host susceptibility and host infectivity. Quantitative genetic studies on infectious diseases generally connect an individual's disease status to its own genotype, and therefore capture genetic effects on susceptibility only. However, they usually ignore variation in exposure to infectious herd mates, which may limit the accuracy of estimates of genetic effects on susceptibility. Moreover, genetic effects on infectivity will exist as well. Thus, to design optimal breeding strategies, it is essential that genetic effects on infectivity are quantified. Given the potential importance of genetic effects on infectivity, we set out to develop a model to estimate the effect of single nucleotide polymorphisms (SNPs) on both host susceptibility and host infectivity. To evaluate the quality of the resulting SNP effect estimates, we simulated an endemic disease in 10 groups of 100 individuals, and recorded time-series data on individual disease status. We quantified bias and precision of the estimates for different sizes of SNP effects, and identified the optimum recording interval when the number of records is limited.Results: We present a generalized linear mixed model to estimate the effect of SNPs on both host susceptibility and host infectivity. SNP effects were on average slightly underestimated, i. e. estimates were conservative. Estimates were less precise for infectivity than for susceptibility. Given our sample size, the power to estimate SNP effects for susceptibility was 100% for differences between genotypes of a factor 1.56 or more, and was higher than 60% for infectivity for differences between genotypes of a factor 4 or more. When disease status was recorded 11 times on each animal, the optimal recording interval was 25 to 50% of the average infectious period.Conclusions: Our model was able to estimate genetic effects on susceptibility and infectivity. In future genomewide association studies, it may serve as a starting point to identify genes that affect disease transmission and disease prevalence.