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Modelling the co-evolution of indirect genetic effects and inherited variability

机译:建模间接遗传效应和遗传变异的共同进化

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摘要

When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of IGEs and variability, as the regression coefficient can respond to selection. Our simulations show that the model results in increased variability of body weight with increasing competition. When competition decreases, i.e., cooperation evolves, variability becomes significantly smaller. Hence, our model facilitates quantitative genetic studies on the relationship between IGEs and inherited variability. Moreover, our findings suggest that we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.
机译:当个体相互作用时,他们的表型不仅会受到他们自己基因的影响,还会受到他们社交伙伴基因的影响。这种现象称为间接遗传效应(IGE)。然而,在水产养殖物种和某些植物中,竞争不仅影响个体的性状水平,而且还加剧了个体间性状值的变异性。在定量遗传学领域中,特征值的变异性已被研究为本身的定量特征,通常被称为遗传变异性。然而,这些研究仅考虑了焦点个体对性状变异的遗传效应,而与竞争无关。尽管观察到的竞争和变异之间的表型关系暗示了潜在的遗传关系,但是当前的IGE定量遗传模型和遗传变异不允许这种关系。缺乏将IGE与遗传变异联系起来的定量遗传模型,限制了我们对变异对自然和农业中对选择作出反应的潜力的理解。例如,特征水平的模型表明,IGE可能会大大改变特征值的遗传变异。当前,我们缺乏调查此结果是否扩展到特征值变异性的工具。在这里,我们提出了一个整合了IGE和继承变异性的模型。在该模型中,目标表型(即增长率)是关注对象的遗传和环境影响以及社交伙伴和关注对象之间的性状价值差异乘以回归系数的函数。回归系数是遗传特征,是合作的量度。负值表示竞争,正值表示合作,因选择而增加的值表示协作的发展。与现有的定量遗传模型相比,我们的模型允许IGE和变异性共同进化,因为回归系数可以响应选择。我们的仿真表明,该模型会导致体重变化随竞争的加剧而增加。当竞争减少时,即合作发展时,可变性会大大减小。因此,我们的模型有助于对IGE与遗传变异之间关系的定量遗传研究。而且,我们的发现表明我们可能已经忽略了遗传变异的整个水平,这是由于IGE所致。

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