Enabling Precision Genomics Using Adaptive Variation

项目概要 自然选择下的基因可能与遗传性疾病以及更普遍的健康变异有关。 例如，与病原体感染期间不同死亡率相关的遗传变异将受到关注。 当传染源存在于人群中时，自然选择。关于选择的推论 因此，人类基因组水平提供了关于功能的新的可检验假设的丰富来源 关系。然而，虽然有很多方法可以在基因水平上检测自然选择，但 通常很难准确确定哪些遗传变异是选择的目标。我们的目标 研究的目的是提供新的计算方法来识别因果突变，并应用这些方法 方法，以便更好地理解基因座的基因型和表型之间的图谱，这些基因座是或已经 已经成为自然选择的目标。我们将该方法应用于 FADS 基因，该基因具有遗传性 与脂肪酸代谢相关的变异，并已在欧洲进行选择 农业引入后的人口。我们将通过实验测试计算预测 使用 CRISPR/Cas9 技术修饰的人类细胞系。这将导致对 人类之间的遗传差异在于这些生理上非常重要的基因。 在目标 1 中，我们将开发新的计算方法，可以从 DNA 序列数据推断出 突变已成为自然选择的目标。这些方法将能够纳入可能性 不止一种突变正在被选择，并且也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。除了识别功能突变之外，我们还将测试有关的假设 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其生长的基质中的分布。 在目标 3 中，我们将扩展这些方法，以便能够在复杂的人口统计模型中对选择进行建模。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 测试由目标 2 结果得出的关于 FADS 中导致选择的因素的假设 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.