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: 10058223
• 负责人: Xin He
• 金额: $ 41.62万
• 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/10058223
• 关键词: Affect; Area; Bayesian Modeling; Chromatin; Code; Communities; Complex; Computer software; DNA Sequence; DNA Structure; DNA sequencing; Data; Demography; Disease; Elements; Enhancers; Evolution; Face; Family; Frequencies; Genetic Diseases; Genome; Genomics; Germ Cells; Germ-Line Mutation; Goals; Human; Human Genetics; Human Genome; Individual; Large-Scale Sequencing; Maps; Measures; Methodology; Methods; Modeling; Mutation; Natural Selections; Nucleotides; Pattern; Phenotype; Population; Population Analysis; Procedures; Process; Recording of previous events; 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; epigenomics; expectation; functional genomics; genetic evolution; genetic pedigree; genome wide association study; genomic data; human disease; improved; infancy; man; 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.

项目摘要 突变和自然选择是进化的基本力，它们在整个基因组中的强度是 确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 该提议的内容是构建一个详细的突变率图和沿着人类净化的选择 使用新型统计方法论的基因组。现有的估计突变率和选择的方法 通常是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，我们认为从人口推断的人更相关，越来越可行 多亏了大规模测序。然而，用于人类内分析的统计方法仍处于起步阶段， 并面临许多挑战；例如，许多影响突变率的因素是未知的，并且 复杂的人口变化使选择的推断变得复杂。 我们提出了三个特定的目的：（1）估计人类基因组中基本水平突变率。我们将 使用来自血统测序数据的从头突变来直接估计种系突变率。我们的模型 将结合一套可能与突变率相关的基因组特征，包括新型基因组特征 不受早期方法（例如DNA结构和生殖系细胞中的表观基因组信息）的使用。我们的 统计模型还结合了随机效应成分并捕获突变的空间相关性 在多个尺度上附近地区之间的速率。 （2）人类基因组中纯化选择的推断。 现有用于检测物种内部约束的方法通常依赖于选择的多个签名之一 时间（例如，比较中性期望的变体的耗尽），并且检测功率有限 选择单个元素，例如推定的增强器。我们将开发一个统一的统计模型 利用几个主要信号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口历史。我们 还利用AIM 1的突变率估计，并使用许多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 疾病遗传学的序列受到约束。我们假设在选择性约束下的序列 人类（无论是编码还是非编码）高度富含引起变异的疾病。我们将测试这个 使用来自全基因组关联研究（GWAS）的数据的假设，特别关注 神经精神病表型。我们将制定利用功能基因组数据和 选择性限制以优先考虑疾病变体。