Predicting Phenotype by Using Transcriptomic Alteration as Endophenotype

使用转录组改变作为内表型预测表型

基本信息

批准号：
9750105
负责人：
Zhongming Zhao
金额：
$ 33.69万
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 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9750105
关键词：
Bioinformatics Biological Breathing Clinical Code Communities Complex Computerized Medical Record DNA Data Data Set Disease Disease Outcome Etiology Event Foundations Gene Expression Genetic Genetic Diseases Genetic Models Genetic Risk Genetic screening method Genetic study Genomics Genotype Genotype-Tissue Expression Project Heritability Human Link Mediating Medical Messenger RNA Methods Modeling Modernization Nucleic Acid Regulatory Sequences Outcome Pathway interactions Phenotype Proteins Quantitative Trait Loci Reporting Research Personnel Role Sampling Schizophrenia Single Nucleotide Polymorphism Statistical Methods System Technology Testing Time Tissue-Specific Gene Expression Training Variant base biobank causal variant computerized tools data mining design disease phenotype disorder risk endophenotype epigenomics falls genetic architecture genetic variant genome wide association study genome-wide improved insight next generation sequencing novel phenotypic data precision medicine predictive modeling programs rare variant tool trait transcriptome transcriptomics

项目摘要

Project Summary Modern studies of the genetic architecture underlying human complex traits or diseases generally fall into three designs of association relationship: the association between genetic variants and disease, the association between genetic variants and expression (e.g. expression quantitative trait loci, eQTL), and the association between gene expression and disease. Many promising findings are discovered, including thousands of single nucleotide polymorphisms found to be associated with common diseases. While these findings provide us with valuable insights into the genetic architecture of common diseases and the shared heritability among diseases, what missing are the mechanisms, including the exact causal variants, the direction of their effects, and the orders of events, which forms the foundational hypothesis that we would like to solve through the studies in this proposal. With the inspiration of many recent discoveries that a substantial fraction of the disease-associated genetic variants is located in regulatory regions, in this proposal, we combine bioinformatics, statistical genetics, precision medicine, and phenotype and electronic medical record (EMR) data mining to develop novel analytical strategies that maximally leverage regulatory information from both genotype and expression, aiming to predict phenotype using transcriptomic alteration with DNA variation. We propose the following three major aims. (1) To build a unified genetic model for the prediction of phenotype by combining genetic and transcriptomic associations. Functional and regulatory annotation data generated from the ENCODE, FANTOM5, GENCODE, the Epigenomic Roadmap, and GTEx will be effectively incorporated to infer an important endophenotype, the genetically determined expression component, for better prediction of phenotype or disease outcome. (2) To develop a maximum likelihood based link test and a phenotype-specific regulatory network approach to resolve genotype-phenotype causality relationships mediated by gene expression. (3) To extensively evaluate the approaches in schizophrenia and apply them to broad phenotypes using the Vanderbilt biobank (BioVU) genotype and linked electronic medical data. Building on our previous studies and strong preliminary data, this proposal is timely for studying the genetic architecture in human complex diseases and traits by dissecting the genetic components contributed from regulatory roles of variants at the gene expression level. It is highly significant because it tackles the strong limitations in numerous genome-wide association studies (GWAS) and next-generation sequencing (NGS) for inferring causality and translational potentials in the emerging fields of precision medicine. The successful completion of this project will not only advance our understanding of genetic components in schizophrenia and a broad spectrum of phenotypes or clinical outcomes, but also provide useful methods and tools to the public community for studying genetic architecture of phenotype via the linkage of genomic and medical information.

项目概要现代对人类复杂特征或疾病的遗传结构的研究通常分为三类关联关系的设计：遗传变异与疾病之间的关联、关联遗传变异和表达之间（例如表达数量性状位点，eQTL）以及关联基因表达与疾病之间的关系。发现了许多有希望的发现，包括数千个单一的发现核苷酸多态性与常见疾病相关。虽然这些发现为我们提供了对常见疾病的遗传结构和疾病之间的共同遗传性的宝贵见解，缺少的是机制，包括确切的因果变异、其影响的方向以及事件的顺序，这构成了我们希望通过本研究解决的基本假设提议。受许多最近发现的启发，很大一部分与疾病相关的疾病遗传变异位于调控区域，在这个提案中，我们结合生物信息学、统计学遗传学、精准医学、表型和电子病历 (EMR) 数据挖掘以开发最大限度地利用来自基因型和表达的监管信息的新颖分析策略，旨在利用 DNA 变异的转录组改变来预测表型。我们建议以下三点主要目标。（1）将遗传与表型相结合，建立统一的遗传模型来预测表型。转录组关联。从 ENCODE 生成的功能和监管注释数据， FANTOM5、GENCODE、表观基因组路线图和 GTEx 将有效地结合起来，以推断重要的内表型，基因决定的表达成分，用于更好地预测表型或疾病结果。 (2) 开发基于最大似然的链接测试和表型特异性解决基因介导的基因型-表型因果关系的调控网络方法表达。 (3) 广泛评估精神分裂症的治疗方法并将其应用于广泛的表型使用范德比尔特生物库 (BioVU) 基因型和链接的电子医疗数据。建立在我们之前的基础上研究和强有力的初步数据，该提案对于研究人类遗传结构来说是及时的通过剖析变异体的调控作用所贡献的遗传成分来研究复杂的疾病和性状在基因表达水平上。它非常重要，因为它解决了许多方面的严重限制用于推断因果关系的全基因组关联研究（GWAS）和下一代测序（NGS）精准医学新兴领域的转化潜力。本项目的顺利完成不仅会增进我们对精神分裂症遗传成分和广泛疾病的理解表型或临床结果，同时也为公众提供有用的方法和工具通过基因组和医学信息的联系研究表型的遗传结构。