Integration of functional data and GWAS to elucidate genetic basis of diseases

整合功能数据和 GWAS 阐明疾病的遗传基础

基本信息

批准号：
9072264
负责人：
JONATHAN K PRITCHARD
金额：
$ 78.62万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-06-23 至 2019-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9072264
关键词：
ATAC-seq Affect Architecture Asthma Autoimmune Diseases Biological Assay Biological Models Blood CD4 Positive T Lymphocytes Cell Separation Cells Charge Chromatin Clustered Regularly Interspaced Short Palindromic Repeats Complex Computing Methodologies DNA Data Data Set Detection Development Disease Effector Cell Gene Expression Regulation Genes Genetic Genetic Variation Hereditary Disease Human Human Genetics Immune Individual Insulin-Dependent Diabetes Mellitus Intestines Lead Lupus Maps Measures Methods Modeling Multiple Sclerosis Pathway interactions Patients Phenotype Play Population Positioning Attribute Recruitment Activity Research Personnel Rheumatoid Arthritis Role Side Site Sorting - Cell Movement Statistical Methods T-Lymphocyte Techniques Technology Testing Validation Variant Work base cell type disease phenotype disorder risk functional genomics genome editing genome wide association study genome-wide genomic data improved novel novel strategies public health relevance research study trait transcriptome sequencing

项目摘要

DESCRIPTION (provided by applicant): One of the central problems in modern human genetics is to understand the functional impact of genetic variation. Of the millions of DNA positions that vary among humans, which sites actually impact human phenotypes and disease? It is becoming increasingly clear that noncoding variants that impact gene regulation play central roles in the genetics of disease, yet these variants remain difficult to interpret. Inthis project we will use rheumatoid arthritis as a model system for tackling these problems, using a combination of computational and experimental techniques. On the computational side, we will develop statistical approaches for integrating functional information from genome-wide assays with data from GWAS studies for complex traits. Our work will allow us to infer which cell-types are most important for any given disease, to improve mapping power, and to improve our ability to identify the most likely causal variants. We also propose novel approaches to validation, based on measuring cellular phenotypes in sorted immune cells and performing genome editing of specific sites. We anticipate that our work will lead to detection and validation of novel RA loci, as well as new techniques of general utility in this field.

描述（由适用提供）：现代人类遗传学的核心问题之一是了解遗传变异的功能影响。在人类之间有数百万种DNA位置中，哪些地点实际上会影响人类的表型和疾病？越来越清楚的是，影响基因调节的非编码变体在疾病的遗传学中起着核心作用，但是这些变体仍然难以解释。在这个项目中，我们将使用类风湿关节炎作为模型系统来解决这些问题，结合使用计算和实验技术。在计算方面，我们将开发统计方法，以将全基因组暗示的功能信息与GWAS研究的数据集成为复杂性状的数据。我们的工作将使我们能够推断出哪些细胞类型对于任何给定疾病，以提高映射能力，并提高我们确定最可能的因果变异的能力。同样，基于测量分类的免疫细胞中的细胞表型并对特定部位进行基因组编辑，也提出了新的验证方法。我们预计我们的工作将导致对新型RA基因座的检测和验证，以及该领域的一般实用程序的新技术。