The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, United Kingdom.
PLoS One. 2012;7(6):e39551. doi: 10.1371/journal.pone.0039551. Epub 2012 Jun 29.
Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence.
通过选择宿主抗性来降低疾病流行率是一种理想的替代化学处理的方法。然而,由于遗传力估计值较低,选择宿主抗性一直很困难。这些低估计可能是由于未能捕捉到疾病抗性的所有相关遗传变异,因为遗传分析目前还没有针对传染性的遗传变异进行定制。宿主传染性是指在与易感个体接触时传播感染的倾向,可以被视为疾病状态的间接影响。它可能是由生理和行为特征的组合引起的。尽管传染性的遗传变异难以直接测量,但间接遗传效应 (IGE) 模型,也称为关联效应或社会相互作用模型,允许从更容易获得的二元疾病数据(感染/未感染)中估计这种变异。因此,我们从具有已知易感性和传染性变异量的模拟种群中生成二元疾病数据,以测试传统和 IGE 模型的充分性。我们的结果表明,传统模型无法捕捉到具有模拟传染性的种群中固有的传染性遗传变异。另一方面,IGE 模型确实可以捕捉到传染性的一些变异。与预期遗传方差的比较表明,进一步改进方法具有潜力,并且潜在的选择反应可能大于这里提出的值。尽管如此,使用估计的直接和间接育种值指数进行选择被证明比仅针对抗性的传统选择具有更大的遗传选择差异,并降低了未来的疾病风险。这些发现表明,如果传染性的遗传变异对疾病传播有很大贡献,那么明确纳入 IGE 的育种设计可能有助于降低疾病流行率。