Genomic prediction in a wild mammal

想象一下这样一个世界，科学家可以对一种动物或植物进行采样，通过DNA分析，预测它的样子，它能活多久，它能有多少后代，以及它是否会在竞争中超过种群中的其他成员。虽然这个想法似乎是幻想，但它已经成为一种可能性，即使是对复杂生态系统中的野生种群也是如此。该计划的目的是开发、测试和应用所谓的“基因组预测”方法，用于进化生态学。在过去的十年中，基因组学方法的显著进步，尤其是下一代测序，已经彻底改变了生物学研究的所有领域。现在可以在任何生物体的基因组中产生数十万个可变位点的DNA图谱。这些位点（称为单核苷酸多态性或SNP）中的许多位点将位于引起表型变异的基因内或非常接近。传统上，寻找这些基因或数量性状基因座（QTL），涉及单独测试每个SNP，然后识别那些具有统计学意义的SNP。然而，这种方法是有问题的，因为它偏向于寻找大作用的基因，而对于许多表型来说，这些基因根本不存在。更常见的情况是，如果有许多基因的影响较小，则QTL将无法检测到。在动物和植物育种中，通过同时考虑所有SNP的表型效应已经解决了这个问题。首先，使用具有已知表型的基因分型样本的“训练群体”来估计每个SNP的效应量。然后对“测试”个体的第二个样品进行基因分型，并且基因型用于预测表型;即进行基因组预测。这种方法是成功的现代人工选择计划的基础，并将用于个性化医疗。然而，基因组预测从来没有被应用到野生种群，尽管它有可能革命性的进化生态genetics.We将测试和应用基因组预测的野生种群的Soay羊在希尔塔岛（圣基尔达，苏格兰）;在世界上研究最深入的脊椎动物种群之一。自1985年以来，超过95%的动物出生在村湾研究区已监测其整个生命周期，详细的生活史（如出生日期，死亡日期，性别，双胞胎状态，形态测量，免疫测定，寄生虫负荷和终身健身）描述了超过7000只绵羊。在整个发展过程中，许多特征已经被测量了无数次。此外，绵羊基因组已被测序，大多数Soay研究群体已被国际绵羊基因组学联盟发现的38 K SNP分型。使Soay绵羊成为测试基因组预测的理想系统的其他特征是：（i）不同的性状得到了很好的描述，并且具有非常不同的遗传结构。eg.毛色和角型有一个简单的遗传基础，而骨骼测量是多基因的（但仍然高度遗传）和（ii）连锁不平衡在基因组中延伸很长的距离，使芯片上的SNPs '标签'大部分基因组。使用2010年之前出生的所有动物的“训练群体”，我们将估计单个SNP的影响，然后使用这些估计来预测2010年之后出生的动物的表型。我们将把预测结果与观察值进行比较;这是基因组预测首次在野生种群中进行测试或应用。我们还将使用基因组预测来确定哪些性状对自然选择做出了进化反应。我们预测基因组预测在我们的研究人群中是可以实现的，并且它将优于传统的基于谱系的方法来研究自然界中的微观进化。

Imagine a world where a scientist could sample an animal or plant and, by DNA profiling, predict what it would look like, how long it would live, how many offspring it would have, and whether or not it would out-compete other members of its population. Although the idea seems fanciful, it has become a possibility, even for wild populations within complex ecological systems. The aim of this proposal is to develop, test and apply so called 'genomic prediction' methods for use in evolutionary ecology.In the last decade remarkable advances in genomics methods, most notably next-generation sequencing, have revolutionised all areas of biological research. It is now possible to generate DNA profiles at hundreds of thousands of variable sites across the genome, in any organism. Many of these sites (known as single nucleotide polymorphisms, or SNPs) will reside within, or very close to, genes that cause phenotypic variation. Traditionally, the search for these genes, or quantitative trait loci (QTL), has involved testing each SNP individually and then identifying those which are statistically significant. However, this approach is problematic, in that it is biased towards finding genes of large effect, which for many phenotypes simply do not exist. If, as is more common, there are many genes of small effect then QTL will remain undetected. In animal and plant breeding, the problem has been solved by considering the phenotypic effect of all SNPs simultaneously. First a 'training population' of genotyped samples with known phenotype are used to estimate effect sizes of each SNP. Then a second sample of 'test' individuals is genotyped, and the genotypes are used to predict phenotype; i.e. perform genomic prediction. This approach underpins successful modern artificial selection programmes and is set to be used in personalised medicine. However, genomic prediction has never been applied to wild populations, despite its potential to revolutionise evolutionary ecological genetics.We will test and apply genomic prediction in the feral population of Soay sheep on the island of Hirta (St Kilda, Scotland); one of the most intensively studied vertebrate populations in the world. Since 1985, over 95% of animals born in the Village Bay study area have been monitored over their entire lifetimes, such that detailed life histories (e.g. date of birth, date of death, sex, twin status, morphological measurements, immunological assays, parasite loads and lifetime fitness) are described for over 7000 sheep. Many traits have been measured numerous times across development. Furthermore, the sheep genome has been sequenced and most of the Soay study population has been typed at 38K SNPs discovered by the International Sheep Genomics Consortium. Additional features that make Soay sheep the ideal system for testing genomic prediction are: (i) different traits have well described and very different genetic architectures. eg. coat colour and horn type have a simple genetic basis while skeletal measurements are far more polygenic (but still highly heritable) and (ii) linkage disequilibrium extends for long distances in the genome, so that the SNPs on the chip 'tag' most of the genome. Using a 'training population' of all animals born until 2010 we will estimate the effects of individual SNPs, and then use these estimates to predict the phenotype of animals born after 2010. We will compare the predictions to observed values; the first time genomic prediction has been tested or applied in a wild population. We will also use genomic predictions to establish which traits have made an evolutionary response to natural selection.We predict that genomic prediction will be achievable in our study population and that it will outperform traditional pedigree-based approaches to studying micro-evolution in nature.