Biotechnology and Food Research, Biometrical Genetics, MTT Agrifood Research Finland Jokioinen, Finland.
Front Genet. 2012 Dec 14;3:262. doi: 10.3389/fgene.2012.00262. eCollection 2012.
Tolerance to infections is defined as the ability of a host to limit the impact of a given pathogen burden on host performance. Uncoupling resistance and tolerance is a challenge, and there is a need to be able to separate them using specific trait recording or statistical methods. We present three statistical methods that can be used to investigate genetics of tolerance-related traits. Firstly, using random regressions, tolerance can be analyzed as a reaction norm slope in which host performance (y-axis) is regressed against an increasing pathogen burden (x-axis). Genetic variance in tolerance slopes is the genetic variance for tolerance. Variation in tolerance can induce genotype re-ranking and changes in genetic and phenotypic variation in host performance along the pathogen burden trajectory, contributing to environment-dependent genetic responses to selection. Such genotype-by-environment interactions can be quantified by combining random regressions and covariance functions. To apply random regressions, pathogen burden of individuals needs to be recorded. Secondly, when pathogen burden is not recorded, the cure model for time-until-death data allows separating two traits, susceptibility and endurance. Susceptibility is whether or not an individual was susceptible to an infection, whereas endurance denotes how long time it took until the infection killed a susceptible animal (influenced by tolerance). Thirdly, the normal mixture model can be used to classify continuously distributed host performance, such as growth rate, into different sub-classes (e.g., non-infected and infected), which allows estimation of host performance reduction specific to infected individuals. Moreover, genetics of host performance can be analyzed separately in healthy and affected animals, even in the absence of pathogen burden and survival data. These methods provide novel tools to increase our understanding on the impact of parasites, pathogens, and production diseases on host traits.
耐受力是指宿主限制特定病原体负担对宿主性能影响的能力。将抗性和耐受力解耦是一个挑战,需要能够使用特定的性状记录或统计方法将它们分开。我们提出了三种可用于研究与耐受力相关性状的遗传的统计方法。首先,使用随机回归,可以将耐受力分析为反应规范斜率,其中宿主性能(y 轴)与增加的病原体负担(x 轴)进行回归。耐受力斜率的遗传方差是耐受力的遗传方差。耐受力的变异可以诱导基因型重新排序,并沿病原体负担轨迹改变宿主性能的遗传和表型变异,从而导致对选择的环境依赖性遗传反应。这种基因型与环境的相互作用可以通过随机回归和协方差函数的组合来量化。为了应用随机回归,需要记录个体的病原体负担。其次,当未记录病原体负担时,用于直至死亡时间数据的矫正模型允许分离两个性状,即易感性和耐力。易感性是指个体是否易受感染,而耐力则表示感染使易感动物死亡所需的时间(受耐受力影响)。第三,正态混合模型可用于将连续分布的宿主性能(例如增长率)分类为不同的子类(例如未感染和感染),从而可以估计特定于感染个体的宿主性能降低。此外,即使没有病原体负担和生存数据,也可以分别在健康和受影响的动物中分析宿主性能的遗传。这些方法提供了新的工具,可以帮助我们更好地理解寄生虫、病原体和生产疾病对宿主性状的影响。