Identifying disease-relevant cell types by integrating genetic and functional genomics data

项目摘要：大规模数据集，如由GTEx、路线图和表观基因组学生成的数据集 联合会和ENCODE项目是了解遗传病遗传基础的宝贵新资源 疾病。我们现在有了关于基因表达和许多功能元件的数据，比如组蛋白修饰 以及人类各种细胞类型和组织中的DNase-I超敏位点(DHS)。对这些问题的分析 数据集，连同来自全基因组关联研究(GWAS)的数据，有可能导致 在我们对疾病原因的理解上取得了突破。虽然统计和计算方法用于 对这些数据集与GWAS数据集的综合分析已经导致了许多有趣的进展， 将这些丰富的数据转化为具体数据，还需要进一步的方法进步。 机械的洞察力。我们将专注于识别与疾病相关的细胞类型和 组织通过对这些数据集的综合分析。我们工作的动机是绝大多数人 疾病的遗传性存在于非编码区，并且调节元件通常表现出强烈的细胞类型 专一性。因此，为了理解遗传变异的机制后果，无论是通过计算还是通过 实验手段，我们需要确定相关过程所在的细胞类型和组织 发生的。虽然这些都是已知的一些复杂的表型，但对许多人来说，它们是不确定的或未知的； 例如，虽然精神分裂症是一种脑部疾病，但最近的证据表明，补体 系统通过其在突触修剪中的作用参与精神分裂症的发病机制，以及相关的细胞 类型仍未解析。尽管这个问题很重要，但开发一种强大的方法来 使用GWAS数据鉴定细胞类型和组织仍然是开放的。我们的方法将有两个 组成部分：首先，我们将开发使用遗传数据来评估给定基因组是否 注释--即基因组的一个子集--对我们正在研究的表型很重要。我们将建立在 我们之前开发的用于富集化分析的方法，有力地利用了多基因信号， 对其进行扩展，以便它可以分析罕见的变异数据，将来自多个数据源的信号组合在一起 单一细胞类型/组织，并研究不同性状的共同细胞类型/组织。第二，我们将使用基因 用于为每个候选细胞类型/组织构建基因组的表达数据和功能基因组数据 有关细胞类型特定活动的最大信息量的注释。我们将通过使用专门的 表达的基因，这些基因在这方面还没有得到充分的利用，我们还将开发新的方法 用于从染色质数据构建信息量最大的基因组注释 路线图。我们将继续我们的做法，发布开源、用户友好的软件和数据。一起， 我们的新方法和注释将允许强大的疾病相关细胞类型和 来自Gwas数据、功能基因组数据和基因表达数据的组织。

Project Summary: Large-scale datasets such as those generated by GTEx, the Roadmap Epigenomics Consortium, and the ENCODE project are valuable new resources for understanding the genetic basis of disease. We now have data on gene expression and many functional elements such as histone modifications and DNase-I Hypersensitivity Sites (DHS) in a variety of cell types and tissues in humans. Analysis of these datasets, together with data from genome-wide association studies (GWAS), has the potential to lead to breakthroughs in our understanding of the causes of disease. While statistical and computational methods for integrative analysis of these datasets with GWAS datasets have already led to many interesting advances, there is a great need for further methodological progress to translate this abundance of data into concrete mechanistic insights. We will focus on the fundamental problem of identifying disease-relevant cell types and tissues via integrative analysis of these datasets. Our work is motivated by the fact that the substantial majority of disease heritability lies in non-coding regions, and regulatory elements often exhibit strong cell-type specificity. Thus, to understand the mechanistic consequences of genetic variation by either computational or experimental means, we need to identify the cell types and tissues in which the relevant processes are occurring. While these are known for some complex phenotypes, they are uncertain or unknown for many; for example, while it is known that schizophrenia is a brain disease, recent evidence indicates that the complement system is involved in schizophrenia pathogenesis through its role in synaptic pruning, and the relevant cell types remain unresolved. Despite the importance of this problem, developing a powerful method for identification of cell types and tissues using GWAS data remains open. Our approach will have two components: first, we will develop methods for using genetic data to assess whether a given genomic annotation—i.e. a subset of the genome—is important for the phenotype we are studying. We will build on a method we previously developed for enrichment analysis that powerfully leverages polygenic signal, extending it so that it can analyze rare variant data, combine signal from multiple sources of data about a single cell type/tissue, and investigate shared cell types/tissues across traits. Second, we will use gene expression data and functional genomics data to construct, for each candidate cell type/tissue, genomic annotations that are maximally informative about cell-type specific activity. We will begin by using specifically expressed genes, which have not been fully leveraged in this context, and we will also develop new methods for constructing maximally informative genomic annotations from chromatin data like that available from Roadmap. We will continue our practice of releasing open-source, user-friendly software and data. Together, our new methods and annotations will allow for powerful identification of disease-relevant cell types and tissues from GWAS data, functional genomic data, and gene expression data.