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Interpreting non-coding variants using epigenomics, regulatory models, & validati

使用表观基因组学、调控模型解释非编码变异，

基本信息

批准号：
9349567
负责人：
Manolis Kellis
金额：
$ 16.27万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-18 至 2017-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9349567
关键词：
Address Adipocytes Affect Alleles Animal Model Bayesian Method Biological Assay Cardiac Catalogs Cells Chromatin Collaborations Complex Computer Simulation Computing Methodologies Diabetes Mellitus Disease Dissection Elements Enhancers Frequencies Gene Expression Gene Targeting Genes Genetic Genetic Variation Genetic study Genome Genome engineering Genomics Genotype Haplotypes Heart Diseases Hepatocyte High Density Lipoprotein Cholesterol Human Human Biology Lead Learning Length Link Linkage Disequilibrium Location Malignant Neoplasms Measurement Medicine Methods Modeling Modification Molecular Mus Nature Non-Insulin-Dependent Diabetes Mellitus Nucleic Acid Regulatory Sequences Nucleosomes Nucleotides PPARG gene Pathway interactions Patients Pattern Phenotype Physiological Positioning Attribute Probability Publishing Quantitative Trait Loci Randomized Regulation Regulatory Element Regulatory Pathway Reporter Resolution Role Scheme Statistical Methods Statistical Models Techniques Technology Testing Tissues Untranslated RNA Validation Variant Work Zebrafish base bone cell type comparative genomics epigenomics expectation experimental study functional genomics genetic association genetic information genome annotation genome editing genome wide association study human disease information model insight molecular phenotype network models neuropsychiatric disorder next generation novel programs public health relevance rare variant success tool trait

项目摘要

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.