Interpreting non-coding variants using epigenomics, regulatory models, & validation experiments

使用表观基因组学、调控模型解释非编码变异，

• 批准号: 9616350
• 负责人: Manolis Kellis
• 金额: $ 54.63万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-11-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9616350
• 关键词: Interpreting; non; coding; variants; using

 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（由适用提供）：尽管全基因组关联研究取得了成功（GWAS），但仍然存在重要的挑战，仍然限制了它们对人类生物学和医学的影响，尤其是对于尚未理解的非编码变体而言。在该提案中，我们探讨了最近的进步（我们小组的许多率领）来克服这些挑战，并对非编码变体在人类疾病和复杂性状中的作用有系统地理解。首先，我们开发了使用高分辨率调节注释来预测与疾病相关的时机，染色质状态和调节基序的新统计方法，并优先考虑使用表观态度信息，比较基因组信息和法规模型（AIM 1）的遗传关联区域内更可能在遗传关联区域内具有调节效应的非编码变体。其次，我们开发了一种新的贝叶斯方法，用于通过在调节网络中整合所有相关区域的遗传信息，将调节区域与其上游调节剂和下游目标基因联系起来，以使用相应的活动，调节型和表达量化特征（eqtl）信息（eqtl）信息（eqtl）信息（aim aim aim aim 2）。第三，我们使用巨大的平行增强子测定法验证我们的方法和预测，以测试大量调节变体的影响。使用基因组编辑技术在其内源性环境中测试调节变体的影响；并使用细胞表型和动物模型在细胞和有机水平上测试调节变体的物理影响（AIM 3），并使用结果来完善我们的计算方法和模型。即使我们的实验验证仅针对少数适合此类研究的特征和细胞类型进行，但我们的方法是一般的，并且将应用于通过正在进行的协作和公共目录可用的所有遗传研究。