权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Quantifying the genetic diversity of human regulatory element activity

量化人类调控元件活性的遗传多样性

基本信息

批准号：
10404498
负责人：
ANDREW S ALLEN
金额：
$ 76.48万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-29 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10404498
关键词：
Alleles Bioinformatics Biological Assay Biological Models CRISPR/Cas technology Catalogs Communities Complex Computer software Data Data Set Disease Epigenetic Process Future Gene Expression Gene Expression Regulation Genes Genetic Genetic Variation Genome Genomics Goals Haplotypes Health Heritability Human Human Genetics Individual Interdisciplinary Study Investigation Investments Maps Methods Modeling Molecular Outcome Patients Pharmaceutical Preparations Phenotype Population Population Heterogeneity Protocols documentation Publishing Reagent Regulation Regulator Genes Regulatory Element Reporter Research Personnel Resources Statistical Data Interpretation Statistical Methods Statistical Models Techniques Technology Testing Time Translating Untranslated RNA Variant Work base cell type cohort design disease phenotype epigenome editing experience genetic architecture genetic association genetic variant genome editing genome sequencing genome wide association study genome-wide human disease improved interest molecular phenotype new technology novel rare variant response single-cell RNA sequencing targeted treatment tool trait user-friendly web site whole genome

项目摘要

Understanding the genetic causes of human disease has immense potential to benefit human health. The human genetics community has devoted tremendous resources to identifying those causes, including, most recently, whole genome sequencing of patient cohorts. Those studies have found genetic variation in non-coding regions of the genome to be most often associated with diseases and drug responses. Unfortunately, since the effects of genetic variation on gene regulation remain poorly understood and difficult to study at the genome-wide scale, the full benefit of most of those studies has yet to be realized. Our long-term goal is to understand how non-coding genetic variants act through gene regulatory elements to influence phenotypes. The objective of this proposal, a step towards that long-term goal, is to develop a platform of empirical and statistical methods to reliably and systematically determine the regulatory mechanisms underlying human traits and diseases. Specifically, in Aim 1, we will use high- throughput reporter assays to quantify the effects of millions of human genetic variants on regulatory element activity. Those variants will represent diverse human ancestries, and will cover over 60% of all regions associated with a trait or disease via GWAS. The outcome will be the most extensive catalog of human regulatory variation every created. In Aim 2, we will develop new technologies to systematically relate those changes in regulatory element activity to changes in gene expression. That technology will combine our previous work developing CRISPR-Cas9- based epigenome editing screens with targeted single-cell RNA-seq. In Aim 3 we will develop statistical analyses to integrate the effects of regulatory variants to infer changes in gene expression and differences in phenotypes between individuals. The resulting method will be analogous to gene based association tests, but for the noncoding genome. The expected outcomes of this project are (i) dramatically improved ability to establish mechanisms underlying non-coding associations with human traits and diseases; (ii) better understanding of the genetic architecture of regulatory element activity and gene regulation that will guide the design and interpretation of future genetic association studies; and (iii) novel reagents, protocols, and software that other labs can use to complete similar investigations of their own model systems of interest. Taken together, we expect that this project will be a major step towards fully realizing the potential of genome wide and whole genome association studies.

了解人类疾病的遗传原因对人类有巨大的潜在益处