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

项目摘要 突变和自然选择是进化的基本力量，它们在基因组中的强度是 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 这项提案的一个重要目的是构建一个详细的突变率地图，并沿着人类基因组进行沿着纯化选择。 基因组使用新的统计方法。估计突变率和选择的现有方法 通常是基于跨物种的基因组比较，但为了研究人类遗传学， 进化论，我们相信那些从人类人口推断的更相关，越来越可行 多亏了大规模测序然而，用于人体内分析的统计方法还处于起步阶段， 并面临许多挑战;例如，许多影响突变率的因素是未知的， 复杂的人口统计学变化使选择的推论复杂化。 我们提出了三个具体目标：（1）估计整个人类基因组的基本水平的突变率。我们将 使用来自系谱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。 早期的方法如DNA结构和生殖系细胞中的表观基因组信息没有利用。我们 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 在多个尺度上对邻近区域的变化率进行比较。(2)人类基因组中的纯化选择推论。 现有的检测物种内约束的方法通常依赖于选择和选择的多个签名之一， 时间（例如，与中性预期相比，变体的消耗），并且在检测方面的能力有限 选择个别元素，如推定的增强子。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本和元素级别的选择。我们的模型使用 强大的泊松随机场（PRF）模型，考虑了复杂的人口统计历史。我们 还利用Aim 1的突变率估计值，并使用许多基因组注释来设置先验 通过分层贝叶斯模型的选择效果的分布。(3)研究人的作用 疾病遗传学中的限制序列。我们假设，在选择性约束下的序列， 人类，包括编码和非编码人类，都高度富集了致病变异体。我们将测试这个 使用全基因组关联研究（GWAS）数据的假设，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限制。

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.