On the genetic interpretation of disease data.
On the genetic interpretation of disease data.
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DOI：
10.1371/journal.pone.0008940
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发表时间：
2010-01-28
期刊：
影响因子：
3.7
通讯作者：
Woolliams JA
中科院分区：
文献类型：
--
作者：
Bishop SC;Woolliams JA
要了解寄主在抗病方面的遗传变异，越来越需要使用田间数据来获得足够数量的表型。对于由细菌或病毒等微寄生虫引起的疾病，我们引入了对田间疾病数据进行遗传解释所必需的概念。我们的重点是方差分量估计，我们将流行病学概念引入数量遗传学。我们已经推导出简单的确定性公式来预测不完全暴露于感染或不完美的诊断试验敏感性和特异性对疾病抵抗力遗传力的影响。我们发现，这些因素都降低了可估量的可继承性。不完全暴露的影响取决于疾病流行率，但与暴露概率相对线性。对于小于0.5的患病率，不完美的诊断试验敏感性会导致对遗传度的小幅低估，而不完美的特异性会导致更大的低估，其影响随着患病率的下降而增加。当流行率超过0.5时，这些影响就会逆转。不完全的数据记录，即没有观察到感染或患病的个体，例如，数据记录的时间太短，其影响类似于不完全的敏感性。这些结果有助于解释在田间条件下观察到的通常较低的抗病遗传力。他们还证明，不完全暴露于感染或次优诊断，并不是证明宿主抗药性遗传差异的致命缺陷，它们只是降低了数据集的力量。最后，鉴于疾病生物学的知识，它们为推断抗病基因变异的真实程度提供了一种工具。
The understanding of host genetic variation in disease resistance increasingly requires the use of field data to obtain sufficient numbers of phenotypes. We introduce concepts necessary for a genetic interpretation of field disease data, for diseases caused by microparasites such as bacteria or viruses. Our focus is on variance component estimation and we introduce epidemiological concepts to quantitative genetics. We have derived simple deterministic formulae to predict the impacts of incomplete exposure to infection, or imperfect diagnostic test sensitivity and specificity on heritabilities for disease resistance. We show that these factors all reduce the estimable heritabilities. The impacts of incomplete exposure depend on disease prevalence but are relatively linear with the exposure probability. For prevalences less than 0.5, imperfect diagnostic test sensitivity results in a small underestimation of heritability, whereas imperfect specificity leads to a much greater underestimation, with the impact increasing as prevalence declines. These impacts are reversed for prevalences greater than 0.5. Incomplete data recording in which infected or diseased individuals are not observed, e.g. data recording for too short a period, has impacts analogous to imperfect sensitivity. These results help to explain the often low disease resistance heritabilities observed under field conditions. They also demonstrate that incomplete exposure to infection, or suboptimal diagnoses, are not fatal flaws for demonstrating host genetic differences in resistance, they merely reduce the power of datasets. Lastly, they provide a tool for inferring the true extent of genetic variation in disease resistance given knowledge of the disease biology.
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