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：
10640918
负责人：
JING ZHANG
金额：
$ 11.3万
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/10640918
关键词：
Acceleration Affect Autopsy 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 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 Methods Modeling Molecular Nucleic Acid Regulatory Sequences Patients Pattern Phenotype Population Prefrontal Cortex Proteins Psychiatric Diagnosis 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 career development cell type deep learning disorder risk epigenome experience experimental study functional genomics gene regulatory network 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.

项目摘要数十年来，被诊断的精神疾病的发生率一直在增加留下数百万痛苦的人。尽管具有较高的遗传力，但它们的基本分子机制仍然难以捉摸。大多数风险基因座位于非编码基因组元素中直接影响蛋白质产品。全面的功能注释和变体影响定量对于提供新的分子见解并发现治疗靶标至关重要。新颖的测序技术和社区努力分享的最新进展基因组数据提供了前所未有的机会，以了解遗传变异如何贡献到精神病。该应用程序描述了综合策略的发展和将新颖测定法（例如Starr-Seq）与种群规模相结合的机器学习方法基因组谱以阐明人前额叶皮层（PFC）中的遗传调节语法并优先考虑精神疾病中的遗传变异。具体而言，我们将（1）剖析顺式 PFC使用种群尺度表观遗传学数据的调节景观，（2）构建多使用染色质将远端顺式调节元素与基因联系起来，模型基因调节网络共同变异性分析，（3）整合遗传，表观遗传和转录数据以识别密钥导致精神疾病的转录因素和变体。与现有不同这项拟议的工作着重于一个基因组，为一种真正新颖的大数据介绍了通过纳入基因调控和研究疾病风险因素的建模来自数百个人的异质多派。最终的全面顺式调节元素列表将扩大人类已知功能区域的数量大脑至少订单。我们将以Web服务的形式发布我们的方法和资源，分布式开源软件和注释数据库，这也将受益其他研究人员探索神经精神疾病的遗传基础。除了其科学内容外，本应用程序还提出了全面的培训准备计算基因组学和神经遗传学独立研究者的计划。该培训将在耶鲁大学（分子生物物理学系和生物化学）在Mark Gerstein教授（功能基因组学）的指导下，Nenad教授 Sestan（神经遗传学）和Hongyu Zhao教授（统计遗传学和机器学习）。一个经验丰富的精神病专家和数据科学家委员会还将提供建议关于科学研究和职业发展。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography.

DOI：
10.3389/fmolb.2020.613347
发表时间：
2020
期刊：
Frontiers in molecular biosciences
影响因子：
5
作者：
Zhou B;Yu H;Zeng X;Yang X;Zhang J;Xu M
通讯作者：
Xu M

CryoETGAN: Cryo-Electron Tomography Image Synthesis via Unpaired Image Translation.

DOI：
10.3389/fphys.2022.760404
发表时间：
2022
期刊：
Frontiers in physiology
影响因子：
4
作者：
Wu X;Li C;Zeng X;Wei H;Deng HW;Zhang J;Xu M
通讯作者：
Xu M

STARRPeaker: uniform processing and accurate identification of STARR-seq active regions.

DOI：
10.1186/s13059-020-02194-x
发表时间：
2020-12-08
期刊：
Genome biology
影响因子：
12.3
作者：
Lee D;Shi M;Moran J;Wall M;Zhang J;Liu J;Fitzgerald D;Kyono Y;Ma L;White KP;Gerstein M
通讯作者：
Gerstein M

Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data.