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

使用共同基金数据集对与疾病相关的遗传变异进行优先排序

基本信息

批准号：
10585864
负责人：
Ferhat Ay
金额：
$ 36.6万
依托单位：
LA JOLLA INSTITUTE FOR IMMUNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-20 至 2024-09-19
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10585864
关键词：
3-Dimensional Alleles Area Atopic Dermatitis Beta Cell Binding Binding Sites Biological Process Catalogs Cell Line Cell Nucleus Cells Chromatin Chromatin Loop Collection Data Data Set Databases Development Disease Distal Encyclopedia of DNA Elements Enhancers Follow-Up Studies Funding Gene Expression Gene Targeting Genes Genetic Genetic Transcription Genome Genotype Goals Heritability Hi-C Human Genetics Immune Individual Inflammatory Insulin-Dependent Diabetes Mellitus Investigation Link Linkage Disequilibrium Maps Methods Motivation Nature Nucleic Acid Regulatory Sequences Process Psoriasis Publishing Quantitative Trait Loci Regulatory Element Research Research Personnel Resolution Rheumatoid Arthritis Role Scleroderma Signal Transduction Single Nucleotide Polymorphism Map Specificity Structure Surface Testing Tissues United States National Institutes of Health Untranslated RNA Variant Work base causal variant cell type epigenomics genetic analysis genetic variant genome annotation genome wide association study interest keratinocyte novel online resource programs promoter skin disorder statistics transcription factor web server

项目摘要

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.

抽象的全基因组关联研究（GWAS）强调，与疾病相关的人类遗传变异是普遍存在于非编码区，其中大多数的生物学功能或基因靶标仍然存在没有特征的。为了更好地注释此类疾病变异，美国国立卫生研究院 (NIH) 资助的联盟创建了综合地图推定的调控元件并确定了与不同基因表达（eQTL）相关的 SNP 组织和原代细胞类型。与此同时，捕获 3D 基因组结构的突破也取得了进展。证明了基因及其调控之间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，通过该方法可以确定某些变异的疾病关联。解释道。人们对利用染色质环进行 GWAS 变异注释越来越感兴趣，但是，据我们所知，还没有结合 eQTL 数据和高分辨率的综合研究跨越许多不同匹配/相关细胞类型和组织的染色质循环信息来解释 GWAS 为大量疾病确定的变异。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更好地注释与疾病相关的遗传变异的综合方法。这将导致开发循环、eQTL 和 GWAS 信号综合分析的框架和最佳实践。这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。对于我们正在研究的一部分疾病研究兴趣，我们将进一步分析已识别的新基因、遗传变异和重叠监管确定需要进一步调查的潜在目标的要素。