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

进化人类基因组学：人口学、自然选择和转录调控

• 批准号: 10551645
• 负责人: Adam Charles Siepel
• 金额: $ 57.6万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10551645
• 关键词: Address; Alleles; Area; Artificial Intelligence; Birds; CRISPR screen; Chiroptera; Computing Methodologies; Data; Demography; Detection; Enhancers; Essential Genes; Event; Evolution; Funding; Gene Expression; Genes; Genetic Drift; Genetic Recombination; Genetic Variation; Genome; Genomics; Goals; Graph; Health; Human; Human Biology; Immunity; Light; Link; Malignant Neoplasms; Mammals; Measures; Methodology; Methods; Modernization; Molecular Evolution; Mutation; Natural Selections; Paper; Pattern; Phenotype; Phylogeny; Polygenic Traits; Population; Population Genetics; Printing; Productivity; Publishing; RNA; Radiation; Regulatory Element; Research; Sampling; Series; Shapes; Software Tools; South American; Techniques; Training; Transcriptional Regulation; Variant; Work; biophysical model; comparative genomics; deep neural network; epigenomics; fitness; genomic data; graph neural network; human disease; human genomics; innovation; insight; intelligence genetics; lens; novel; programs; promoter; reconstruction; statistics; transcriptome sequencing

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.

项目概要 我的研究项目旨在通过分子进化的视角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，并利用进化论 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 我们的工作特别关注三个主要领域：（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；以及（3）研究 哺乳动物的转录调控及其进化。 在上一个资助期间（2018-2022），我们在每个领域都取得了重大进展，包括 方法论的进步以及新方法在重要而及时的科学问题上的应用。 例如，我们最近开发了用于推断祖先重组图的创新新方法 (ARG) 来自多群体序列数据；用于使用 ARG 和深度神经元检测选择性扫描 网络；用于从 CRISPR-Cas9 筛选中鉴定必需基因，以估计相对 RNA； 基于广泛可用的RNA测序数据类型的半衰期，并用于检测RNA的增益和丢失； 这些方法都具有基于表观基因组数据的系统发育分支的顺式调控元件。 基于这些和其他方法，我们发布了一个公开的软件工具。 各种新颖的科学发现，包括以前未知的发现和表征 现代人类和古代人类之间的基因渗入事件；南美鸟类的基因组分析； 表明它们的辐射主要是由最近的选择性扫描驱动的；一项分析表明广泛的 蝙蝠免疫和癌症相关基因快速进化的证据以及表明的分析； 在哺乳动物进化中，增强子的获得和丢失速度大约是启动子的两倍。 总共 17 篇原始论文和预印本中进行了描述。 对于本次续展申请，我们建议继续在这三个关键领域进行研究。 制定的目标包括使用域扩展到非常大的 ARG 的新统计抽样方法； 适应以减少群体基因组学中的训练偏差；测量纯化选择的极端水平； 人类群体中罕见变异的模式；描述多基因适应度效应的分布； 性状；识别和表征与有利等位基因相关的有害物质，并开发统一的等位基因； 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 R35 资金的更新将使我们能够保持高生产力的贡献者。 到这个至关重要的研究领域。