Linking disease-associated variants to transcriptional regulation using ENCODE

描述（由申请人提供）：虽然全基因组关联研究（GWAS）已经确定了3000多个与常见疾病相关的基因座，但这些基因座变异致病的机制尚不清楚。ENCODE项目提供了广泛的功能基因组数据的独特资源，可用于缩小这一知识差距。本应用程序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，同时预测特定疾病的相关组织类型和功能重要变异。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。核心假设是，通过GWAS标记鉴定的基因座通过改变转录因子结合位点功能SNPs导致疾病，从而失调相关组织类型（例如癌症的肿瘤前组织和自身免疫性疾病的免疫细胞）中的基因。这项研究的基本原理是ENCODE数据代表了理解GWAS结果的丰富资源，并且这里开发的方法将能够对其他疾病进行类似的分析。由于他们在GWAS的实施和分析，GWAS命中的功能和生物信息学随访以及理解基因组规模数据（包括转录因子结合）的机器学习方法方面的综合专业知识，研究团队已经做好了充分的准备来承担所提出的研究。中心假设将通过以下目标进行验证：1)确定在适当组织类型中产生的ENCODE数据是否可用于在疾病相关位点发现假定的功能性转录调控变异。

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.