Evolutionary Human Genomics: Demography, Natural Selection, and Transcriptional Regulation

要充分理解人类基因组，必须在进化的背景下考虑。主导人类基因组学三十年的活动--如基因组测序和注释、高通量生化分析的询问，以及基因变异和疾病之间的联系的鉴定--提供了大量的信息，但这些描述性研究最终必须在进化遗传学的理论框架内得到理解。我们必须从什么继续向前推进？原因何在？又是如何做到的？人类遗传学的。 我的实验室的目标是从进化的角度解释高通量的基因组数据。借鉴分子进化、种群遗传学、统计学和计算机科学中的思想和技术，我们的目标是了解塑造人类基因组的进化力量，并利用进化来阐明特定序列的表型重要性。我们最近的活动集中在三个主要领域： (1)基于基因组序列重建人类进化的特征；(2)预测人类突变的适合性结果；(3)研究灵长类动物的转录调控及其进化。我们已经报告了这些领域中的每个领域的主要发现，包括从早期现代人到东部尼安德特人的基因流的存在，整个人类基因组突变的适合性后果图，以及一项分析表明，转录起始的架构在人类基因组的增强子和启动子上非常相似。 在这里，我们建议在这些领域中的每一个领域大幅扩展我们的研究，与广泛的实验和理论合作者合作。我们的新目标包括开发重建人类人口学的改进方法，重点是古代基因流；扩展我们的祖先重组图(ARG)采样方法，以适应更大的样本量，并应用于关联映射和自然选择的检测；两种互补的机器学习方法，用于改进从序列数据预测适合性结果；实验合作，以利用CRISPR-Cas9屏幕表征非编码突变；多管齐下研究RNA稳定性的序列决定因素及其对转录单位进化的影响；以及开发新的调节元件更替的概率模型。这些项目将共同解决关于人类基因组中序列的功能和进化的各种基本问题。

To be fully understood, the human genome must be considered in the context of evolution. The activities that have dominated human genomics for three decades — such as genome sequencing and annotation, interrogation with high-throughput biochemical assays, and the identification of associations between genetic variants and diseases — have been enormously informative, but these descriptive studies must eventually be understood within the theoretical framework of evolutionary genetics. We must continue to press forward from the what? to the why? and how? of human genetics. The goal of my laboratory is to interpret high-throughput genomic data from an evolutionary perspective. Drawing from ideas and techniques in molecular evolution, population genetics, statistics, and computer science, we aim both to understand the evolutionary forces that have shaped human genomes, and to use evolution to shed light on the phenotypic importance of particular sequences. Our recent activities have focused in three major areas: (1) reconstruction of features of human evolution based on genome sequences; (2) prediction of the fitness consequences of human mutations; and (3) the study of transcriptional regulation and its evolution in primates. We have reported major findings in each of these areas, including the existence of gene flow from early modern humans to Eastern Neandertals, a map of fitness consequences for mutations across the human genome, and an analysis showing that the architecture of transcription initiation is highly similar at enhancers and promoters in the human genome. Here we propose to extend our research substantially in each of these areas, working together with a broad range of experimental and theoretical collaborators. Our new goals include the development of improved methods for reconstructing human demography, with a focus on ancient gene flow; extensions of our ancestral recombination graph (ARG) sampling methods to accommodate much larger samples sizes, with applications in association mapping and the detection of natural selection; two complementary machine-learning approaches for improving the prediction of fitness consequences from sequence data; an experimental collaboration to leverage CRISPR-Cas9 screens in characterizing noncoding mutations; a multi-pronged study of the sequence determinants of RNA stability and their implications for the evolution of transcription units; and development of a new probabilistic model for turnover of regulatory elements. Together, these projects will address a wide variety of fundamental questions about the function and evolution of sequences in the human genome.