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

项目摘要 我的研究计划旨在通过分子进化的透镜来理解现代基因组数据。 借鉴统计学、人工智能和群体遗传学的思想和技术，我们寻求 了解塑造当今基因组序列的进化力量，并使用进化 模式，以深入了解特定基因组序列的表型重要性，具有广泛的影响 为了人类健康。我们的工作特别集中在三个主要领域：（1）基于进化的重建 基因组序列;（2）推断人类突变的适应性后果;（3）研究 转录调控及其在哺乳动物中的进化。 在上一个资助期（2018-2022年），我们在上述每个领域都取得了重大进展，包括 方法的进步和新方法在重要和及时的科学问题上的应用。为 例如，我们最近开发了创新的新方法，用于推断祖先重组图 （ARG）;用于使用ARG和深度神经网络检测选择性扫描。 网络;用于从CRISPR-Cas9筛选中鉴定必需基因;用于估计相对RNA 基于广泛可用的RNA测序数据类型的半衰期;以及 顺式调控元件沿着分支的表观基因组数据的基础上的一个基因组。这些方法都有 已作为公开可用的软件工具实施。基于这些和其他方法，我们发表了一份 各种新的科学发现，包括发现和表征以前未知的 现代人与古代人之间的基因渗入事件;南美洲鸟类的基因组分析 表明它们的辐射主要是由最近的选择性扫描驱动的;分析表明广泛的 蝙蝠免疫和癌症相关基因快速进化的证据;以及一项分析表明， 在哺乳动物进化中，增强子的获得和丢失的速率大约是启动子的两倍。这些发现 在总共17篇原始论文和预印本中描述。 对于这次更新申请，我们建议继续在这三个关键领域进行研究。具体 目标包括开发新的统计抽样方法，可扩展到非常大的ARG;使用域 适应，以减少人口基因组学的培训偏差;测量极端水平的纯化选择， 人类群体中罕见变异的模式;表征多基因适应性效应的分布 性状;识别和表征与有利等位基因连锁的有害变体;以及开发统一的 新生RNA测序数据的生物物理建模框架及其在比较基因组学中的应用 和伸长速率估计。R35资金的更新将使我们能够保持高生产力的贡献者 这个至关重要的研究领域。

Project Summary My research program aims to make sense of modern genomic data through the lens of molecular evolution. Drawing from ideas and techniques in statistics, artificial intelligence, and population genetics, we seek both to understand the evolutionary forces that have shaped present-day genome sequences, and to use evolutionary patterns to gain insight into the phenotypic importance of particular genomic sequences, with broad implications for human health. Our work focuses in particular on three major areas: (1) evolutionary reconstruction based on genome sequences; (2) inference of the fitness consequences of human mutations; and (3) the study of transcriptional regulation and its evolution in mammals. In the last funding period (2018–2022), we achieved major advances in each of these areas, including both methodological advances and applications of new methods to important and timely scientific questions. For example, we recently developed innovative new methods for the inference of ancestral recombination graphs (ARGs) from multi-population sequence data; for the detection of selective sweeps using ARGs and deep neural networks; for the identification of essential genes from CRISPR-Cas9 screens; for the estimation of relative RNA half-lives based on widely available RNA-sequencing data types; and for the detection of gains and losses of cis-regulatory elements along the branches of a phylogeny based on epigenomic data. These methods have all been implemented as publicly available software tools. Based on these and other methods, we published a variety of novel scientific findings, including the discovery and characterization of previously unknown introgression events between modern and archaic hominins; a genomic analysis of South American birds indicating their radiation was primarily driven by recent selective sweeps; an analysis indicating extensive evidence of rapid evolution in immunity- and cancer-related genes in bats; and an analysis indicating that enhancers are gained and lost at about twice the rate of promoters in mammalian evolution. These findings were described in a total of 17 original papers and preprints. For this renewal application, we propose to continue our research within each of these three key areas. Specific goals include developing new statistical sampling methods that scale to very large ARGs; using domain adaptation to reduce training bias in population genomics; measuring extreme levels of purifying selection from patterns of rare variation in human populations; characterizing the distributions of fitness effects for polygenic traits; identifying and characterizing deleterious variants linked to advantageous alleles; and developing a unified biophysical modeling framework for nascent RNA sequencing data with applications to comparative genomics and elongation-rate estimation. A renewal of R35 funding will enable us to remain highly productive contributors to this critically important research area.