Enabling Precision Genomics Using Adaptive Variation

项目摘要 自然选择下的基因可能与可遗传疾病有关，更广泛地说，适应能力的变化也与此有关。 例如，与病原体感染期间不同死亡率有关的遗传变异将受到 当传染病病原体存在于种群中时的自然选择。关于选择的推论 因此，人类的基因组水平提供了关于功能的新的可检验的假说的丰富来源 两性关系。然而，尽管有许多方法可以在遗传水平上检测自然选择，但它 通常很难准确地确定哪些遗传变异是选择的目标。我们的目标是 研究是为识别因果突变提供新的计算方法，并应用这些方法 方法：为了更好地了解等位基因座的基因和表型之间的映射 一直是自然选择的目标。我们将把这种方法应用于含有遗传基因的FADS基因。 与脂肪酸代谢相关的变异，在欧洲一直处于选择之中 农业引入后的人口数量。我们将在实验中测试计算预测 用CRISPR/Cas9技术修饰的人细胞系。这将使我们更深入地理解 人类在这些生理上非常重要的基因上存在遗传差异。 在目标1中，我们将开发新的计算方法，可以从DNA序列数据中推断出 突变一直是自然选择的目标。这些方法将能够结合这种可能性 不止一个突变正在被选择，并且还将能够利用各种形式的 表型和功能数据。 在目标2中，我们将使用CRISPR/Cas9测试有关FADS基因选择的计算预测 在人类细胞系中。除了确定功能突变之外，我们还将测试关于 突变之间以及突变与环境之间的相互作用，如 细胞可利用的脂肪酸在它们生长的底物中的分布。 在目标3中，我们将扩展这些方法，使其能够在复杂的人口统计模型中进行模型选择。我们会 还将该方法扩展到能够包括环境协变量和古代DNA。这将使我们能够 检验由目标2的结果所提供的关于在时尚中引起选择的因素的假设 基因。

Project Summary Genes under natural selection may be related to heritable diseases, and variation in fitness more generally. For example, genetic variants related to differential mortality rates during pathogenic infections will be under natural selection when the infectious agents are present in the population. Inferences about selection at the genomic level in humans, therefore, provide a rich source of new testable hypotheses about functional relationships. However, while there are many methods for detecting natural selection at the genetic level, it is often very hard to determine exactly which genetic variants were targeted by selection. The aim of our study is to provide new computational methods for identifying causal mutations, and to apply these methods, in order to better understand the map between genotype and phenotype of loci that are, or have been, targeted by natural selection. We will apply the method to FADS genes, which harbor genetic variation associated with fatty acid metabolism and which have been under selection in European populations after the introduction of agriculture. We will test computational predictions experimentally in human cell lines modified using CRISPR/Cas9 technology. This will lead to a deeper understanding of the genetic differences among humans in these physiologically very important genes. In Aim 1 we will develop new computational methods that can infer, from DNA sequence data, which mutations have been targeted by natural selection. The methods will be able to incorporate the possibility that more than one mutation has been under selection and will also be able to leverage various forms of phenotypic and functional data. In Aim 2, we will test computational predictions regarding selection in the FADS genes using CRISPR/Cas9 in human cell lines. In addition to identifying the functional mutations, we will test hypotheses about interaction between mutations and between mutations and the environment, as represented by the distribution of fatty acids available to the cells in the substrate they are growing on. In Aim 3 we will extend the methods to be able to model selection in complex demographic models. We will also extend the method to be able to include environmental co-variates and ancient DNA. This will allow us to test hypotheses informed by the results of Aim 2 regarding the factors causing selection in the FADS genes.