Predicting Phenotype by Deep Learning Heterogeneous Multi-Omics Data

通过深度学习异构多组学数据预测表型

基本信息

批准号：
10449376
负责人：
Zhongming Zhao
金额：
$ 31.86万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-14 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10449376
关键词：
3-Dimensional Affect Automobile Driving Bioinformatics Biological Brain Diseases Cell physiology Cells Code Communities Complex Computerized Medical Record Computing Methodologies DNA Data Data Science Data Set Databases Development Dimensions Disease Elements Environmental Risk Factor Epigenetic Process Gene Expression Genes Genetic Genetic Transcription Genetic study Genome Genomics Genotype Genotype-Tissue Expression Project Human Human Genome Individual Informatics Link Maps Medical Informatics Messenger RNA Methods Mining Multiomic Data Neurodegenerative Disorders Neurodevelopmental Disorder Other Genetics Phenotype Play Population Proteins Regulation Reporting Research Research Personnel Resolution Role Sampling Signal Transduction Single Nucleotide Polymorphism Statistical Methods Technology Time Tissue-Specific Gene Expression Tissues Untranslated RNA Variant base biobank cell type cohort computerized tools data mining deep learning disease phenotype disorder risk epigenomics functional genomics gene environment interaction gene interaction genetic architecture genetic variant genome wide association study genome-wide heterogenous data human study innovation learning strategy neural network neuropsychiatric disorder novel programs single cell sequencing single-cell RNA sequencing spatiotemporal trait transcriptome web server

项目摘要

Project Summary Complex disease and traits are caused by dynamic genetic regulation and environmental interactions. Numerous genetic, genomic, and phenotypic datasets have been generated, including genotypes, gene expression, epigenetic changes, and electronic medical records (EMRs). Currently, there is main challenge on development of novel informatic approaches to effectively link phenotype with genomic information. Specifically, genome-wide association studies (GWAS) have reported several thousand single nucleotide polymorphisms (SNPs) that are significantly associated with the disease and traits; however, more than 80% of them are noncoding variants, making it difficult to interpret their potential disease-causal roles. We and others have systematically examined how phenotypic variability in disease risk for a broad spectrum of disease phenotypes can be explained by regulatory variants. Now, we hypothesize that such regulation will be in a tissue-specific, cell type-specific and developmental stage-specific (TCD-specific) manner. Importantly, large genomic consortia, like ENCODE, FANTOM5, the Roadmap Epigenomics, and GTEx have continuously generated high-quality functional data for annotating genome-wide variants. The emerging single-cell sequencing technologies have enabled us to examine how genetic variants affect cellular functions within individual cells or specific cell types. This brings us an unprecedented opportunity to develop novel statistical and computational approaches for deep understanding of the genetic architecture of phenotype. In this proposal, we combine bioinformatics, single cell omics, deep learning, and phenotype and EMR data mining to develop novel analytical strategies that maximally leverage information from both genotype and expression from massive heterogeneous data, aiming to predict phenotype by functional assessment of DNA variation at the TCD-specific levels. We propose the following three specific aims. (1) To develop a deep learning method for variant impact predictor, DeepVIP, that maximally utilizes functional and regulatory data to predict the causal roles of variants in complex disease and traits. (2) To develop phenotype-specific network approaches to resolve genotype-phenotype relationships in the spatiotemporal manner and single-cell resolution. We will develop a novel method, single cell dense module search of GWAS signals (scGWAS) and also a graphical neural network approach, GNN-scTP, to detect driving roles of genes from single cell RNA-seq data. These methods can effectively identify critical regulatory modules and genes in complex disease in the TCD-specific manner. (3) To apply the methods to 16 neurodevelopmental and neurodegenerative disorders and related traits, as well as broad phenotypes using Vanderbilt biobank (BioVU) and UK Biobank data – both have genotypes linked with rich phenotypic information. Our proposal is timely and innovative to study the genetic architecture in human complex diseases and traits by dissecting important genetic components, especially noncoding variants, at the functional, regulatory, spatial, temporal, and single cell levels.

项目概要复杂的疾病和性状是由动态遗传调控和环境相互作用引起的。已经生成了大量的遗传、基因组和表型数据集，包括基因型、基因目前，主要挑战在于表达、表观遗传变化和电子病历（EMR）。开发新的信息学方法以有效地将表型与基因组信息联系起来。具体来说，全基因组关联研究（GWAS）已报告了数千个单核苷酸然而，超过 80% 的多态性 (SNP) 与疾病和性状显着相关；它们是非编码变异，因此很难解释它们潜在的致病作用。系统地研究了多种疾病的疾病风险表型变异如何表型可以通过调控变异来解释。现在，我们认为这种调控将在一个过程中进行。组织特异性、细胞类型特异性和发育阶段特异性（TCD 特异性）方式。基因组联盟，如 ENCODE、FANTOM5、Roadmap Epigenomics 和 GTEx 不断生成了用于注释新兴单细胞变异的高质量功能数据。测序技术使我们能够检查遗传变异如何影响细胞功能单个细胞或特定细胞类型给我们带来了前所未有的机会来开发新的统计数据。和计算方法来深入理解表型的遗传结构。建议，我们将生物信息学、单细胞组学、深度学习、表型和 EMR 数据挖掘结合起来开发新颖的分析策略，最大限度地利用基因型和表达的信息来自大量异质数据，旨在通过 DNA 变异的功能评估来预测表型我们提出以下三个具体目标： (1) 开发深度学习方法。对于变异影响预测器 DeepVIP，它最大限度地利用功能和监管数据来预测 (2) 开发表型特异性网络方法以时空方式和单细胞分辨率解决基因型-表型关系。开发一种新方法，GWAS 信号的单细胞密集模块搜索（scGWAS）以及图形化神经网络方法 GNN-scTP，用于检测单细胞 RNA-seq 数据中基因的驱动作用。方法可以有效地识别 TCD 特异性复杂疾病中的关键调控模块和基因 (3)将该方法应用于16种神经发育和神经退行性疾病及相关疾病。性状以及使用范德比尔特生物库 (BioVU) 和英国生物库数据的广泛表型 - 两者都具有我们的建议对于研究遗传来说是及时且创新的。通过剖析重要的遗传成分，尤其是人类复杂疾病和特征的结构非编码变异，在功能、调节、空间、时间和单细胞水平上。