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A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders

探索表观遗传异质性并解释精神疾病非编码变异的大数据方法

基本信息

批准号：
10039384
负责人：
JING ZHANG
金额：
$ 9.43万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-17 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10039384
关键词：
Affect Award Big Data Binding Biochemistry Biological Assay Biological Process Biophysics Brain ChIP-seq Chromatin Communities Computer software Computing Methodologies Data Data Scientist Databases Dependence Development Dimensions Disease Distal Elements Enhancers Entropy Epigenetic Process Event Gaussian model Gene Expression Regulation Genes Genetic Genetic Risk Genetic Transcription Genetic Variation Genome Genomics Heritability Heterogeneity Human Incidence Individual Knowledge Learning Link Machine Learning Maps Mental disorders Mentorship Methodology Methods Modeling Molecular Nucleic Acid Regulatory Sequences Patients Pattern Phenotype Population Prefrontal Cortex Proteins Psychiatric Diagnosis Regulator Genes Regulatory Element Reporter Reporting Research Research Personnel Resolution Resources Risk Factors Sampling Scanning Scoring Method Shapes Signal Transduction Technology Training Training Programs Transcriptional Regulation Universities Untranslated RNA Variant Work base career development cell type deep learning disorder risk epigenome experience experimental study functional genomics genetic profiling genetic variant genome-wide genomic data genomic locus genomic profiles insight machine learning method multimodality multiple omics neurogenetics neuropsychiatric disorder new therapeutic target novel novel sequencing technology open source programs promoter recruit research and development risk variant therapeutic target transcription factor transcriptomics web services

项目摘要

PROJECT ABSTRACT The incidence of diagnosed psychiatric disorders has been increasing for decades, leaving millions of afflicted individuals. Despite the high heritability, their underlying molecular mechanisms remain elusive. Most risk loci are located in noncoding genomic elements without direct effects on protein products. Comprehensive functional annotation and variant impact quantification are essential to provide new molecular insights and discover therapeutic targets. Recent advances in novel sequencing technologies and community efforts to share genomic data provide unprecedented opportunities to understand how genetic variants contribute to psychiatric diseases. This application describes the development of integrative strategies and machine learning methods to combine novel assays (such as STARR-seq) with population-scale genomic profiles to elucidate the genetic regulatory grammar in the human prefrontal cortex (PFC) and to prioritize genetic variants in psychiatric disorders. Specifically, we will (1) dissect the cis- regulatory landscape of the PFC using population-scale epigenetics data, (2) construct multi- model gene regulatory networks by linking distal cis-regulatory elements to genes using chromatin co-variability analyses, (3) integrate genetic, epigenetic, and transcriptional data to identify key transcription factors and variants that contribute to psychiatric disorders. Distinct from existing efforts focusing on one genome, this proposed work presents a truly novel big-data approach for both modeling gene regulation and investigating disease-risk factors by incorporating heterogeneous multi-omics profiles from hundreds of individuals. The resultant comprehensive list of cis-regulatory elements will expand the number of known functional regions in the human brain by at least an order. We will release our methods and resources in the form of web services, distributed open-source software, and annotation databases, which will also benefit other investigators exploring the genetic underpinnings of neuropsychiatric disorders. In addition to its scientific content, this application proposes a comprehensive training program for preparing an independent investigator in computational genomics and neurogenetics. This training will take place at Yale University (in the Dept. of Molecular Biophysics and Biochemistry) under the mentorship of Prof. Mark Gerstein (functional genomics), Prof. Nenad Sestan (neurogenetics), and Prof. Hongyu Zhao (statistical genetics and machine learning). A committee of experienced psychiatric disease experts and data scientists will also provide advice on both scientific research and career development.

项目摘要