Reverse engineering the genotype-phenotype map for parasite resistance in natural populations of red grouse.

个体受寄生虫影响的程度极大地影响了他们的健康，健身，活力和生殖成功。为了试图了解寄生虫感染如何影响个体，识别影响抗性水平的基因以及这些基因的DNA序列差异在多大程度上解释了个体与个体之间寄生虫负担的差异是非常重要的。有了这些信息，人们就可以预测在一个群体中谁最适合和最成功，以及这对一个群体的持久性意味着什么。本研究将探讨遗传差异，解释在寄生虫负载之间的变化，红松鸡。松鸡是一个很好的例子，寄生虫负荷对适应性有很大的影响，因此提供了一个模型系统，其中遗传差异将是明显的，很容易识别。我们将使用一种称为数量性状基因座（QTL）定位的程序来识别这些基因。QTL定位寻找相似的寄生虫负担和遗传组成在一个大的松鸡家谱。如果一个特定的基因组成是一贯通过自己的一代又一代，这导致那些携带者有大量的寄生虫，那么这提供了一个很好的论据，即影响抗性的基因在物理上位于非常接近它在松鸡遗传蓝图。现在还知道，基因开启的程度可以影响寄生虫负荷的水平以及个体DNA蓝图的内在差异。我们将使用类似的QTL类型的分析来确定控制另一个基因区域被打开的程度的DNA区域。关于哪些基因在使一个个体比其他个体更具抵抗力方面很重要的信息将与打开这些开关的知识相结合，以获得更广泛的基因组成，从而确定个体松鸡具有抵抗力。然后，我们将看到在不同人群中，我们可以看到这些适应性差异的程度。人们可以预测，如果X基因使个体比Y基因更具抵抗力，那么我们可以预期，在寄生虫水平较高的人群中，X基因将更常见，因为携带Y基因的健康状况较差的个体将死亡。我们将在有大量寄生虫的人群和没有寄生虫的人群中寻找不同遗传类型发生的差异。总的来说，我们的工作将有助于理解寄生虫及其宿主如何通过军备竞赛共同进化，其中一方希望通过磨练其基因组成尽可能有效（就宿主而言）或最有能力逃避宿主的免疫防御（就寄生虫而言）来击败另一方。

The extent to which individuals are affected by parasites greatly influences their health, fitness, vigour and reproductive success. To try and understand how parasites infection will affect individuals, it is extremely important to identify the genes that influence levels of resistance and the extent to which differences in the DNA sequence of these genes explain the differences in parasite burdens from individual to individual. Armed with this information, one can predict who might be most fit and successful within a group of individuals and what that may mean for the persistence of a population. This study will examine the genetic differences that explain variation in parasite load among red grouse. Grouse are a good example where parasite load has a large effect on fitness, so provides a model system where genetic differences will be pronounced and easy to identify. We will use a procedure called quantitative trait locus (QTL) mapping to identify these genes. QTL mapping looks for similarity in both parasite burden and genetic makeup in a large family tree of grouse. If a particular piece of the genetic makeup is consistently passed own from generation to generation, and this leads to those carriers having a high number of parasites, then this provides a good argument that the gene affecting resistance is physically located quite close to it in the grouse genetic blueprint. It is also now know that the extent to which a gene is switched on can affect levels of parasite load as well as inherent differences in the DNA blueprint of individuals. We will identify regions of DNA that control the extent to which another gene region is switched on using a similar QTL type of analysis. The information on what genes are important in making one individual more resistant than others will be combined with a knowledge of the switches that turn these on, to get a broader picture of the genetic makeup that identifies an individual grouse as being resistant. We will then see the extent to which we can see these differences in fitness operating in different populations. One would predict that if gene X makes individuals more resistant than gene Y then we would expect that gene X will be more common in populations where parasites are at high levels, as the less fit individuals carrying gene Y will have died. We will look for differences in the occurence of different genetic types in populations where there are lots of parasites and populations where parasites are absent. Overall, our work will go a long way to understanding how parasites and their hosts have co-evolved through an arms-race where one side is looking to outwit the other by honing its genetic makeup to be as efficient as possible (in terms of the host) or most capable of evading the immune defences of hosts (in the case of the parasite).