Interpreting non-coding variants using epigenomics, regulatory models, & validati

使用表观基因组学、调控模型解释非编码变异，

• 批准号: 9349567
• 负责人: Manolis Kellis
• 金额: $ 16.27万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-18 至 2017-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9349567
• 关键词: Address; Adipocytes; Affect; Alleles; Animal Model; Bayesian Method; Biological Assay; Cardiac; Catalogs; Cells; Chromatin; Collaborations; Complex; Computer Simulation; Computing Methodologies; Diabetes Mellitus; Disease; Dissection; Elements; Enhancers; Frequencies; Gene Expression; Gene Targeting; Genes; Genetic; Genetic Variation; Genetic study; Genome; Genome engineering; Genomics; Genotype; Haplotypes; Heart Diseases; Hepatocyte; High Density Lipoprotein Cholesterol; Human; Human Biology; Lead; Learning; Length; Link; Linkage Disequilibrium; Location; Malignant Neoplasms; Measurement; Medicine; Methods; Modeling; Modification; Molecular; Mus; Nature; Non-Insulin-Dependent Diabetes Mellitus; Nucleic Acid Regulatory Sequences; Nucleosomes; Nucleotides; PPARG gene; Pathway interactions; Patients; Pattern; Phenotype; Physiological; Positioning Attribute; Probability; Publishing; Quantitative Trait Loci; Randomized; Regulation; Regulatory Element; Regulatory Pathway; Reporter; Resolution; Role; Scheme; Statistical Methods; Statistical Models; Techniques; Technology; Testing; Tissues; Untranslated RNA; Validation; Variant; Work; Zebrafish; base; bone; cell type; comparative genomics; epigenomics; expectation; experimental study; functional genomics; genetic association; genetic information; genome annotation; genome editing; genome wide association study; human disease; information model; insight; molecular phenotype; network models; neuropsychiatric disorder; next generation; novel; programs; public health relevance; rare variant; success; tool; trait

 DESCRIPTION (provided by applicant): Despite the successes of genome-wide association studies (GWAS), important challenges remain that still limit their impact on human biology and medicine, especially for non-coding variants which remain poorly understood. In this proposal, we exploit recent advances (many pioneered by our group) to overcome these challenges and gain a systematic understanding of the role of non-coding variants in human disease and complex traits. First, we develop new statistical methods that utilize high-resolution regulatory annotations to predict disease-relevant tissues, chromatin states, and regulatory motifs, and to prioritize non-coding variants more likely to have regulatory effects within regions of genetic association using epigenomic state information, comparative genomic information, and regulatory motif analysis (Aim 1). Second, we develop a new Bayesian methods for linking regulatory regions to their upstream regulators and downstream target genes by integrating genetic information across all associated regions in the context of regulatory networks that link regulators and regulatory regions using their correlated activity, regulatory motifs, and expression quantitative trait locus (eQTL) information (Aim 2). Third, we validate our methods and predictions using massively-parallel enhancer assays to test the effect of large number of regulatory variants in isolation; using genome editing technologies to test the effects of regulatory variants in their endogenous context; and using cellular phenotypes and animal models to test the physiological effects of regulatory variants at the cellular and organismal levels (Aim 3), and use the results to refine our computational methods and models. Even though our experimental validations are only performed for a small number of traits and cell types that are amenable to such studies, our methods are general and will be applied to all genetic studies available through ongoing collaborations and public catalogs.

 描述（由申请人提供）：尽管全基因组关联研究（GWAS）取得了成功，但仍然存在重要的挑战，这些挑战仍然限制了它们对人类生物学和医学的影响，特别是对于仍然知之甚少的非编码变体。在这项提案中，我们利用最新的进展（许多是我们小组开创的）来克服这些挑战，并系统地了解非编码变异在人类疾病和复杂性状中的作用。首先，我们开发了新的统计方法，利用高分辨率的监管注释来预测疾病相关的组织，染色质状态和监管基序，并优先考虑非编码变体更有可能在遗传关联区域内使用表观基因组状态信息，比较基因组信息和监管基序分析（目标1）。第二，我们开发了一种新的贝叶斯方法，通过整合所有相关区域的遗传信息，将调控区域与其上游调控因子和下游靶基因连接起来，这些调控网络使用相关活性，调控基序和表达数量性状基因座（eQTL）信息将调控区域与调控区域联系起来（目标2）。第三，我们验证了我们的方法和预测，使用并行增强子测定来测试大量单独的调控变体的效果;使用基因组编辑技术来测试调控变体在其内源性背景下的效果;并使用细胞表型和动物模型来测试调节变体在细胞和生物体水平上的生理效应（目标3），并使用结果来改进我们的计算方法和模型。尽管我们的实验验证仅针对少数适合此类研究的性状和细胞类型进行，但我们的方法是通用的，并将适用于通过正在进行的合作和公共目录提供的所有遗传研究。