Using Common Fund datasets for prioritization of disease-associated genetic variants

摘要 全基因组关联研究（GWAS）强调，疾病相关的人类遗传变异是 在非编码区中普遍存在，并且对于它们中的大多数， 没有特征的为了更好地注释这些疾病变异，NIH资助的财团创建了全面的地图 的推定的调控元件和确定的SNPs与基因表达（eQTL）的不同， 组织和原代细胞类型。与此同时，在捕捉3D基因组结构方面的突破， 证明了基因和它们的调节基因之间的细胞类型特异性物理接近的重要性。 元素这种3D视图提供了一种新的方法，通过这种方法可以确定某些变体的疾病相关性。 解释道然而，人们对利用染色质环进行GWAS变体注释的兴趣越来越大， 据我们所知，目前还没有综合eQTL数据和高分辨率的研究。 跨许多不同的匹配/相关细胞类型和组织的染色质循环信息，以解释GWAS 为大量疾病鉴定的变异。该提案的目标是利用NIH共同基金数据集 (GTEx和4D Nucleome）以及其他已发表的染色质环和eQTL数据来进行不同的分析。 整合方法，以更好地注释疾病相关的遗传变异。这会导致 为环、eQTL和GWAS信号的综合分析开发框架和最佳实践。的 开发的框架将在大量疾病和疾病相关细胞类型上进行测试，以创建一个 为研究人员提供大量在线资源。对于一个研究疾病的子集，我们正在进行 研究兴趣，我们将进一步分析确定的新基因，遗传变异和重叠调控 以确定值得进一步调查的潜在目标。

Abstract Genome-wide association studies (GWAS) have highlighted that disease-associated human genetic variants are prevalent in noncoding regions and for most of them the biological function or gene target remain uncharacterized. To better annotate such disease variants, NIH-funded consortia created comprehensive maps of putative regulatory elements and identified SNPs associated with gene expression (eQTLs) for different tissues and primary cell types. In parallel, breakthroughs in capturing the 3D genome structure have demonstrated the importance of cell-type-specific physical proximity between genes and their regulatory elements. This 3D view provided a new way through which disease-associations of certain variants can be explained. There is an increasing interest in utilization of chromatin loops for GWAS variant annotation, however, to the best of our knowledge, there is no comprehensive study incorporating eQTL data and high-resolution chromatin looping information across many different matched/related cell types and tissues to interpret GWAS variants identified for a large set of diseases. To goal of this proposal is to utilize NIH Common Fund datasets (GTEx and 4D Nucleome) as well as other published chromatin loop and eQTL data to carry out different integrative approaches for better annotation of disease-associated genetic variants. This will lead to the development of a framework and best practices for integrative analysis of loops, eQTLs and GWAS signals. The developed framework will be tested on a large number of diseases and disease-relevant cell types to create a substantial online resource for researchers. For a subset of the studied diseases, for which we have ongoing research interests, we will further analyze the identified novel genes, genetic variants and overlapping regulatory elements to determine potential targets that warrant further investigation.