The spread of infectious diseases in populations is controlled by the susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals. Estimating genetic risk factors for these three underlying host epidemiological traits can help reduce disease spread through genetic control strategies. Previous studies have identified important ‘disease resistance single nucleotide polymorphisms (SNPs)’, but how these affect the underlying traits is an unresolved question. Recent advances in computational statistics make it now possible to estimate the effects of SNPs on host traits from epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known about how to effectively design disease transmission experiments or field studies to maximise the precision with which these effects can be estimated. In this paper, we develop and validate analytical expressions for the precision of the estimates of SNP effects on the three above host traits for a disease transmission experiment with one or more non-interacting contact groups. Maximising these expressions leads to three distinct ‘experimental’ designs, each specifying a different set of ideal SNP genotype compositions across groups: (a) appropriate for a single contact-group, (b) a multi-group design termed “pure”, and (c) a multi-group design termed “mixed”, where ‘pure’ and ‘mixed’ refer to groupings that consist of individuals with uniformly the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than estimates for infectivity. Whereas the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferred because it uses information from naturally-occurring rather than artificial infections. The same design principles apply to estimates of the epidemiological impact of other categorical fixed effects, such as breed, line, family, sex, or vaccination status. Estimation of SNP effect precisions from a given experimental setup is implemented in an online software tool SIRE-PC. Methodology was developed to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables that underlie host susceptibility, infectivity and recoverability. Designs that maximize the precision of estimates were derived.

中文翻译：

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估计传染病传播的遗传和非遗传效应的最佳实验设计

传染病在人群中的传播受个体的易感性（获得感染的倾向）、传染性（传播感染的倾向）和可恢复性（恢复/死亡的倾向）控制。估计这三种潜在宿主流行病学特征的遗传风险因素有助于通过遗传控制策略减少疾病传播。以前的研究已经确定了重要的“抗病单核苷酸多态性 (SNPs)”，但这些如何影响潜在性状是一个尚未解决的问题。计算统计的最新进展使得现在可以根据流行病数据（例如个体的感染和/或恢复时间或诊断测试结果）估计 SNP 对宿主特征的影响。然而，关于如何有效地设计疾病传播实验或实地研究以最大限度地提高估计这些影响的精度，人们知之甚少。在本文中，我们针对具有一个或多个非相互作用接触组的疾病传播实验开发并验证了 SNP 对上述三种宿主性状影响的估计精度的分析表达式。最大化这些表达导致三种不同的“实验”设计，每一种都指定一组不同的跨组的理想 SNP 基因型组成：（a）适用于单个接触组，（b）称为“纯”的多组设计，和(c) 称为“混合”的多组设计，其中“纯”和“混合”指的是由分别具有相同或不同 SNP 基因型的个体组成的组。发现对敏感性和可恢复性的精确估计对实验设计的敏感性低于对传染性的估计。尽管分析表达式表明多组纯设计和混合设计以相似的精度估计 SNP 效应，但混合设计是首选，因为它使用来自自然发生而非人工感染的信息。相同的设计原则适用于其他分类固定效应（例如品种、品系、家庭、性别或疫苗接种状况）的流行病学影响的估计。从给定的实验设置中估计 SNP 效应精度是在在线软件工具 SIRE-PC 中实现的。开发了方法来帮助设计疾病传播实验，以估计个体 SNP 和其他分类变量的影响，这些变量是宿主易感性、传染性和可恢复性的基础。推导出了最大化估计精度的设计。