Refining mutation rates and measures of purifying selection with an application to understanding the impact of non-coding variation on neuropsychiatric diseases

改进突变率和纯化选择的措施，并应用于了解非编码变异对神经精神疾病的影响

• 批准号: 10665606
• 负责人: Xin He
• 金额: $ 43.67万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10665606
• 关键词: Affect; Area; Bayesian Modeling; Cell Line; Chromatin; Code; Communities; Complex; Computer software; DNA Sequence; DNA Structure; DNA sequencing; Data; Demography; Disease; Elements; Enhancers; Evolution; Face; Family; Frequencies; Genetic Diseases; Genetic Variation; Genome; Genomics; Germ Lines; Germ-Line Mutation; Goals; Historical Demography; Human; Human Genetics; Human Genome; Individual; Large-Scale Sequencing; Maps; Measures; Methodology; Methods; Modeling; Mutation; Natural Selections; Nucleotides; Pattern; Phenotype; Population; Population Genetics; Procedures; Process; Research; Resources; Role; Sampling; Signal Transduction; Site; Software Tools; Statistical Methods; Statistical Models; Testing; Time; Untranslated RNA; Variant; Work; base; cell type; computerized tools; de novo mutation; detection method; epigenomics; expectation; functional genomics; genetic pedigree; genome wide association study; genomic data; human disease; improved; infancy; neuropsychiatric disorder; neuropsychiatry; novel; population based; rare variant; secondary outcome; tool

Project Summary Mutation and natural selection are fundamental forces of evolution, and their intensities across the genome are key factors in determining the genomic landscape of human genetic disease variation and evolution. The goal of the proposal is to construct a detailed map of mutation rates and purifying selection along the human genome using novel statistical methodologies. Existing approaches to estimating mutation rates and selection are often based on genome comparison across species, but for the purpose of studying human genetics and evolution, we believe those inferred from the human population are more relevant and increasingly feasible thanks to large-scale sequencing. Statistical methods for intra-human analysis, however, are in their infancy, and face a number of challenges; for example, many factors affecting mutation rates are unknown and complex human demographic changes complicate the inference of selection. We propose three specific aims: (1) Estimation of base-level mutation rates across the human genome. We will use de novo mutations from pedigree sequencing data to directly estimate germline mutation rates. Our model will incorporate a large set of genomic features potentially associated with mutation rates, including novel ones not utilized by earlier methods such as DNA structure and epigenomic information in germ line cells. Our statistical model also incorporates a random effect component and captures spatial correlations of mutation rates between nearby regions at multiple scales. (2) Inference of purifying selection in the human genome. Existing methods for detecting intra-species constraint often rely on one of multiple signatures of selection a time (e.g. depletion of variants comparing with neutral expectation), and have limited power in detecting selection on individual elements, such as a putative enhancer.! We will develop a unified statistical model that leverages several major signals to detect selection at both base and element levels. Our model uses the powerful Poisson Random Field (PRF) model, taken complex human demographic history into account. We also leverage mutation rates estimates from Aim 1 and use a number of genomic annotations to set prior distribution of selection effects through a hierarchical Bayesian model. (3) Studying the role of human constrained sequences in disease genetics. We hypothesize that sequences under selective constraint in human, both coding and noncoding ones, are highly enriched with disease causing variants. We will test this hypothesis using data from Genome-wide Association studies (GWAS), with a special focus on neuropsychiatric phenotypes. We will develop procedures that leverage both functional genomic data and selective constraints to prioritize disease variants.

项目概要 突变和自然选择是进化的基本力量，它们在整个基因组中的强度是 决定人类遗传疾病变异和进化的基因组景观的关键因素。目标 该提案的目的是沿着人类基因构建突变率和纯化选择的详细图谱 基因组使用新颖的统计方法。估计突变率和选择的现有方法 通常基于跨物种的基因组比较，但目的是研究人类遗传学和 进化论，我们相信从人类群体中推断出的结论更具相关性且越来越可行 得益于大规模测序。然而，用于人体内部分析的统计方法还处于起步阶段， 并面临诸多挑战；例如，许多影响突变率的因素是未知的， 复杂的人口统计变化使选择的推论变得复杂。 我们提出了三个具体目标：（1）估计整个人类基因组的基础水平突变率。我们将 使用谱系测序数据中的从头突变来直接估计种系突变率。我们的模型 将包含大量可能与突变率相关的基因组特征，包括新的特征 早期方法（例如生殖细胞中的 DNA 结构和表观基因组信息）未利用。我们的 统计模型还包含随机效应成分并捕获突变的空间相关性 多个尺度附近区域之间的比率。 (2)人类基因组纯化选择的推论。 用于检测种内约束的现有方法通常依赖于选择a的多个特征之一 时间（例如，与中性期望相比变体的耗尽），并且检测能力有限 对单个元素的选择，例如假定的增强子。！我们将开发一个统一的统计模型 利用几个主要信号来检测基础和元素级别的选择。我们的模型使用 强大的泊松随机场 (PRF) 模型，考虑了复杂的人类人口统计历史。我们 还利用目标 1 的突变率估计，并使用大量基因组注释来设置先验 通过分层贝叶斯模型分布选择效应。 (3)研究人的作用 疾病遗传学中的受限序列。我们假设在选择性约束下的序列 人类，无论是编码基因还是非编码基因，都富含致病变异。我们将测试这个 使用全基因组关联研究（GWAS）的数据进行的假设，特别关注 神经精神表型。我们将开发利用功能基因组数据和 优先考虑疾病变异的选择性约束。