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

基本信息

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
项目类别：
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

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

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项目摘要

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.

项目摘要我的研究计划旨在通过分子进化的角度来理解现代基因组数据。从统计，人工智能和人口遗传学中的思想和技术中汲取灵感，我们都试图了解已经塑造了当今基因组序列并使用进化的进化力模式以深入了解特定基因组序列的表型重要性，具有广泛的含义为人类健康。我们的工作特别关注三个主要领域：（1）基于进化重建基因组序列；（2）推断人类突变的适应性后果；（3）研究转录调节及其在哺乳动物中的进化。在最后一个资金期（2018-2022）中，我们在每个领域都取得了重大进步，包括新方法在重要及时的科学问题上的方法论进步和应用。为了例如，我们最近开发了用于推断祖先重组图的创新新方法（args）来自多人群序列数据；为了检测选择性扫描，使用ARG和深神经元网络；从CRISPR-CAS9筛选中鉴定基本基因；为了估计相对RNA 基于广泛可用的RNA序列数据类型的半衰期；并检测到收益和损失基于表观基因组数据的系统发育分支的顺式调节元件。这些方法具有全部被用作公开可用的软件工具。基于这些方法和其他方法，我们发布了一个各种新颖的科学发现，包括以前未知的发现和表征现代和古代人类之间的渗入事件；南美鸟类的基因组分析表明它们的辐射主要由最近的选择性糖果驱动。表明广泛的分析蝙蝠中免疫和癌症相关基因的快速进化的证据；并表明增强子在哺乳动物进化中的启动子速率的速度约为两倍。这些发现是总共描述了17篇原始论文和预印本。对于此续订应用程序，我们建议在这三个关键领域中的每个领域中继续进行研究。具体的目标包括开发新的统计抽样方法，以扩展到非常大的ARGS；使用域适应以减少人群基因组学的培训偏见；测量从人口罕见变化的模式；表征多基因适应性效应的分布特质；识别和表征与有利等位基因相关的有害变体；并建立统一新生RNA测序数据的生物物理建模框架与比较基因组学应用和伸长率估计。 R35资金的续签将使我们能够保持高效的贡献者到这个至关重要的研究领域。