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

• 批准号: 10318084
• 负责人: Zhongming Zhao
• 金额: $ 33.26万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 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/10318084
• 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

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.

项目摘要 复杂的疾病和性状是由动态的遗传调节和环境相互作用引起的。 已经产生了大量的遗传、基因组和表型数据集，包括基因类型、基因 表达、表观遗传变化和电子病历(EMR)。目前，主要的挑战是 开发新的信息学方法，有效地将表型与基因组信息联系起来。 具体地说，全基因组关联研究报告了几千个单核苷酸。 与疾病和性状显著相关的多态(SNP)；然而，超过80%的 它们都是非编码变体，因此很难解释它们潜在的致病作用。我们和其他人 系统地研究了多种疾病的表型变异性对疾病风险的影响 表型可以用调控变异来解释。现在，我们假设这样的监管将在一个 组织特定、细胞类型特定和发育阶段特定(TCD特定)的方式。重要的是，大型 基因组联盟，如ENCODE，FANTOM5，路线图表观基因组学和GTEx不断 生成了用于注释全基因组变异的高质量功能数据。新兴的单细胞 测序技术使我们能够研究基因变异如何影响细胞功能 单个细胞或特定细胞类型。这给我们带来了一个前所未有的发展新统计学的机会 以及深入理解表型遗传结构的计算方法。在这 建议，我们结合生物信息学、单细胞组学、深度学习、表型和EMR数据挖掘来 开发新的分析策略，最大限度地利用来自基因和表达的信息 从海量的异质数据中，旨在通过对DNA变异的功能评估来预测表型 TCD特定水平。我们提出了以下三个具体目标。(1)发展深度学习方法 对于可变影响预测指标DeepVIP，它最大限度地利用功能和监管数据来预测 变异在复杂疾病和性状中的因果作用。(2)发展针对表型的网络方法 以时空方式和单细胞分辨率解析基因-表型关系。我们会 提出了一种新的方法--单细胞密集模块搜索法(ScGwas)，并给出了一种图形化的方法 神经网络方法，GNN-scTP，从单细胞RNA-SEQ数据中检测基因的驱动作用。这些 方法可以有效地识别复杂疾病中的关键调控模块和基因在TCD中的特异性 举止。(3)将该方法应用于16例神经发育和神经退行性疾病及相关疾病。 特征，以及使用Vanderbilt Biobank(BioVU)和UK Biobank数据的广泛表型-两者都具有 基因连锁有丰富的表型信息。我们的建议是及时和创新的，研究遗传 人类复杂疾病和特征中的结构通过解剖重要的遗传成分，尤其是 在功能、调节、空间、时间和单细胞水平上的非编码变体。