Understanding the genomic basis of problematic alcohol use through integrative analysis of multi-omics data

通过多组学数据的综合分析了解有问题的饮酒的基因组基础

基本信息

批准号：
10611490
负责人：
Tan Hoang Nguyen
金额：
$ 15.97万
依托单位：
VIRGINIA COMMONWEALTH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10611490
关键词：
Affect Alcohol abuse Alcohols Architecture Big Data Biological Brain CRISPR/Cas technology Caenorhabditis elegans Code Data Data Set Disease Dryness Etiology Funding Genes Genetic Genetic Diseases Genetic Models Genetic Predisposition to Disease Genetic Risk Genetic study Genomics Goals Hi-C Investigation Knowledge Mathematics Mental Health Mentors Mentorship Meta-Analysis Methods Multiomic Data National Institute on Alcohol Abuse and Alcoholism Nucleotides Pathway interactions Persons Phenotype Proteome Proxy Psychopathology Public Health RNA Interference Reporting Research Resources Risk Sequence Analysis Signal Transduction Systems Biology Testing Time Training United States United States National Institutes of Health Untranslated RNA Validation Variant Work alcohol effect alcohol misuse alcohol use disorder biobank career cohort comorbidity cost data integration disease classification disorder risk drinking exome sequencing experience experimental study flexibility functional genomics genetic architecture genetic information genome sequencing genome-wide genomic data heuristics large scale data model organism neuropsychiatric disorder non-genomic phenotypic data rare variant resilience risk variant statistics trait transcriptome translational research program whole genome

项目摘要

This project seeks to further the understanding of the genomic etiology of alcohol use disorder (AUD). Genetic studies have identified loci associated with the disorder; however, the genetic architecture of AUD has not been fully explained. Currently, genome-wide variant data of the disorder are available for analysis, and functional genomic datasets are being generated from consortia. Sequencing data from biobanks would allow assessment of the contribution of coding and functional non-coding regions to AUD risk by using a proxy phenotype from the information of the Alcohol Use Disorder Identification Test (AUDIT) report. Prior work has shown that 1) AUD has a high genetic overlap with some neuropsychiatric disorders (NPDs), and 2) common-variant studies identify biological pathways associated with the disorder. Therefore, integrative analyses which incorporate multi-omics datasets and overlapping genetic information can help identify additional loci associated with the disorder. Thus, the candidate plans to use integrative methods to 1) increase power for genetic discovery, and 2) better understand the genomic architecture for AUD. AUD has a high genetic correlation with the AUDIT-based phenotype (AUDIT-P) which assesses the problematic consequences of drinking. AUD and AUDIT-P have similar genetic correlations with other NPDs. Multiple AUDIT-P datasets are publicly available, and a recent meta-analysis of AUD and AUDIT-P datasets shows increased statistical power for risk loci identification. For this reason, this proposal centers on identifying the genomic association for problematic alcohol use which includes AUD itself and/or AUDIT-P. To achieve this research goal, the candidate proposes the following aims. First, he plans to extend his previous methods to jointly analyze rare exonic variants and other omics datasets. Second, he will develop methods to integrate common variants, omics information and genetic overlap information to increase power for multi-trait analysis. Third, he proposes an integrative method to analyze rare variants from whole-genome sequencing data. Fourth, he will apply these methods to analyze large-scale variant and functional-genomic datasets. He plans to use systems biology approaches to elucidate analysis results from these methods. Also, he proposes using genetic model organisms (C. elegans) to better understand the results from computational approaches via RNA interference and CRISPR-Cas9 experiments. Dr. Nguyen has a background in mathematics, statistics and statistical genetics; however, he has not had prior experience in the field of genetics of AUD. The mentor team including leading experts in the field will help him to 1) gain expertise in the basic principles of psychopathology, alcohol related phenotypes, the nosology of NPDs and alcohol related phenotypes; 2) acquire understanding of genetic model organisms, RNAi and CRISPR-Cas9 experiments for validation of computational results; 3) gain expertise in additional types of omics data; and 4) develop needed independence in his career. These training goals will aid him in successfully conducting the proposed work, and building a NIH-funded translational research program focused on AUD and its comorbid conditions.

该项目旨在进一步了解酒精使用障碍（AUD）的基因组病因学。遗传研究已确定与该疾病相关的位点；然而，AUD 的遗传结构尚未被阐明。充分解释。目前，该疾病的全基因组变异数据可用于分析，并且功能基因组数据集是由联盟生成的。来自生物库的测序数据将允许评估通过使用来自 AUD 风险的代理表型来评估编码区和功能性非编码区对 AUD 风险的贡献酒精使用障碍识别测试 (AUDIT) 报告的信息。先前的工作表明 1) 澳元与某些神经精神疾病 (NPD) 具有高度遗传重叠，并且 2) 常见变异研究发现与该疾病相关的生物学途径。因此，结合多组学的综合分析数据集和重叠的遗传信息可以帮助识别与该疾病相关的其他基因座。因此，候选人计划使用综合方法来 1) 提高基因发现的能力，2) 更好了解 AUD 的基因组结构。 AUD 与基于 AUDIT 的遗传相关性很高表型（AUDIT-P）评估饮酒的问题后果。 AUD 和 AUDIT-P 有与其他 NPD 具有相似的遗传相关性。多个 AUDIT-P 数据集是公开可用的，最近的一个 AUD 和 AUDIT-P 数据集的荟萃分析显示风险位点识别的统计功效有所提高。为了因此，该提案的重点是确定有问题的饮酒的基因组关联，包括 AUD 本身和/或 AUDIT-P。为了实现这一研究目标，候选人提出以下目标。首先，他计划扩展之前的方法来联合分析罕见的外显子变异和其他组学数据集。其次，他将开发整合常见变异、组学信息和遗传重叠的方法信息以增强多性状分析的能力。第三，他提出了一种分析稀有物质的综合方法。来自全基因组测序数据的变异。第四，他将应用这些方法来分析大规模变异和功能基因组数据集。他计划使用系统生物学方法来阐明分析结果这些方法。此外，他建议使用遗传模型生物（秀丽隐杆线虫）来更好地理解结果来自 RNA 干扰和 CRISPR-Cas9 实验的计算方法。阮博士有一个数学、统计学和统计遗传学背景；然而，他之前并没有这方面的经验 AUD 遗传学领域。包括该领域领先专家在内的导师团队将帮助他1）获得专业知识精神病理学的基本原理、酒精相关表型、NPD 的疾病分类学和酒精相关表型； 2) 了解遗传模型生物、RNAi 和 CRISPR-Cas9 实验计算结果的验证； 3) 获得其他类型组学数据的专业知识； 4）开发所需的事业上的独立性。这些培训目标将帮助他成功开展拟议的工作，并且建立一个由 NIH 资助的转化研究项目，重点关注 AUD 及其合并症。