Linking disease-associated variants to transcriptional regulation using ENCODE

描述(申请人提供)：尽管全基因组关联研究已经确定了3000多个与常见疾病相关的基因座，但这些基因座上的变异是致病的机制仍不清楚。ENCODE项目提供了广泛的功能基因组数据的独特资源，可用于弥合这一知识差距。这项应用的总体目标是开发计算方法，将ENCODE项目的数据与Gwas数据相结合，以同时预测特定疾病的相关组织类型和重要功能变异。第二个目标是通过分析癌症和自身免疫性疾病的数据来验证这些方法。中心假设是，通过Gwas识别的基因座通过改变转录因子结合位点来标记导致疾病的功能性SNPs，从而使相关组织类型中的基因失调(例如，癌症的癌前组织和自身免疫性疾病的免疫细胞)。这项研究的基本原理是，ENCODE数据是理解GWAS结果的丰富资源，这里将要开发的方法将使对其他疾病的类似分析成为可能。研究小组已经为开展拟议的研究做好了充分的准备，因为他们在进行和分析GWAs、GWAHITS的功能和生物信息学后续行动以及理解包括转录因子结合在内的基因组规模数据的机器学习方法方面具有共同的专业知识。中心假设将通过以下目的进行检验：1)确定在适当的组织类型中产生的ENCODE数据是否可以用于在疾病相关基因座寻找假定的功能转录调控变量。 这将通过询问淋巴瘤风险SNPs是否倾向于改变转录因子结合部位并与淋巴母细胞系中邻近基因的表达相关联来实现。2)使用ENCODE数据，确定对特定疾病重要的细胞类型和组织(S)。相关的细胞类型将通过确定在ENCODE数据中疾病风险位点附近更有可能表达基因的细胞类型来确定。3)当无法获得适当组织类型的完整功能基因组数据时，使用ENCODE确定GWAS中可能的功能SNPs。为了将这些分析扩展到在ENCODE中广泛研究的少数几种细胞类型之外，来自相关组织的DNA酶超敏数据将与来自其他组织的ChIP-Seq转录因子结合数据相关联，以便能够识别改变的变种 转录因子结合。这项研究具有重要意义，因为它将为 癌症和自身免疫性疾病的生物学。更重要的是，它将提供必要的工具，利用ENCODE数据将疾病风险基因与功能变异和潜在的机制解释联系起来。

DESCRIPTION (provided by applicant): While genome-wide association studies (GWAS) have identified over 3000 loci associated with common disease, the mechanism by which variation at these loci are pathogenic remains unclear. The ENCODE project provides a unique resource of extensive functional genomic data that can be used to close this knowledge gap. The overall objective of this application is to develop computational methods to integrate data from the ENCODE project with GWAS data to predict simultaneously the relevant tissue type and functionally important variants for a given disease. A secondary objective is to validate these approaches through the analysis of data on cancer and autoimmune disease. The central hypothesis is that loci identified through GWAS tag functional SNPs that cause disease by altering transcription factor binding sites, thereby dysregulating genes in the relevant tissue typ (e.g. pre-neoplastic tissue for cancer and immune cells for autoimmune disease). The rationale that underlies this research is that the ENCODE data represents a rich resource for understanding GWAS results and that the methods to be developed here will enable similar analysis on other diseases. The research team is well prepared to undertake the proposed research because of their combined expertise in the conduct and analysis of GWAS, functional and bioinformatics follow-up of GWAS hits, and machine learning approaches to understanding genome-scale data including transcription factor binding. The central hypothesis will be tested through these aims: 1) Determine if ENCODE data generated in the appropriate tissue type can be used to find putative functional transcriptional regulatory variants at disease-associated loci. This will be achieved by asking if lymphoma risk SNPs tend to alter transcription factor binding sites and associate with expression of nearby genes in lymphoblastoid cell lines. 2) Using ENCODE data, identify the cell types and tissue(s) important for a given disease. Relevant cell types will be identified by determining those cell types in which genes are more likely to be expressed near disease risk loci in the ENCODE data. 3) Identify putative functional SNPs in GWAS using ENCODE when complete functional genomic data is not available for the appropriate tissue type. To extend these analyses beyond the few cell types extensively studied in ENCODE, DNase hypersensitivity data from the relevant tissue will be linked with ChIP-Seq transcription factor binding data from other tissues to allow identification of variants that alter transcription factor binding. This research is significant because it will provide new insight into the biology of cancer and autoimmune disease. More importantly, it will provide the tools necessary to use the ENCODE data to link disease risk loci with functional variants and potential mechanistic explanations.