Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission.
Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission.
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DOI：
10.1186/s12711-022-00747-1
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发表时间：
2022-09-05
影响因子：
4.1
通讯作者：
Doeschl-Wilson, Andrea
中科院分区：
文献类型：
--
作者：
Pooley, Christopher;Marion, Glenn;Bishop, Stephen;Doeschl-Wilson, Andrea
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。估计这三个潜在的宿主流行病学特征的遗传风险因素可以帮助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的性状是一个悬而未决的问题。计算统计学的最新进展使得现在有可能从流行病数据（例如个体的感染和/或恢复时间或诊断测试结果）估计SNP对宿主性状的影响。然而，很少有人知道如何有效地设计疾病传播实验或实地研究，以最大限度地提高精度，这些影响可以估计。在本文中，我们开发和验证分析表达式的精度的估计SNP对上述三个主机性状的疾病传播实验与一个或多个非相互作用的接触组的影响。最大化这些表达导致三种不同的“实验”设计，每种设计指定各组中不同的理想SNP基因型组成：（a）适合于单个接触组，（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”是指由具有一致相同或不同SNP基因型的个体组成的分组，分别发现敏感性和可回收性的精密度估计值对实验设计的敏感性低于感染性估计值。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，但混合设计是优选的，因为它使用来自自然发生的而不是人工感染的信息。同样的设计原则也适用于其他分类固定效应（如品种、品系、家系、性别或疫苗接种状态）的流行病学影响的估计。在一个在线软件工具SIRE-PC中实现了从给定的实验设置的SNP效应精度的估计。方法学的发展，以帮助疾病传播实验的设计，估计个别SNPs和其他分类变量的影响，宿主易感性，传染性和可恢复性。最大限度地提高估计精度的设计。在线版本包含补充材料，可通过10.1186/s12711-022-00747-1获得。
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. The online version contains supplementary material available at 10.1186/s12711-022-00747-1.
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影响因子：
3.3
作者：
Hulst AD;de Jong MCM;Bijma P
通讯作者：
Bijma P
DOI：
10.1016/j.animal.2021.100286
发表时间：
2021-12
期刊：
Animal : an international journal of animal bioscience
影响因子：
--
作者：
Doeschl-Wilson A;Knap PW;Opriessnig T;More SJ
通讯作者：
More SJ
影响因子：
3.7
作者：
Bitsouni, Vasiliki;Lycett, Samantha;Doeschl-Wilson, Andrea
通讯作者：
Doeschl-Wilson, Andrea
影响因子：
4.4
作者：
Freebern, Ellen;Santos, Daniel J. A.;Ma, Li
通讯作者：
Ma, Li
影响因子：
2.4
作者：
Fife, M. S.;Howell, J. S.;Kaiser, P.
通讯作者：
Kaiser, P.