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

• 批准号: 10429414
• 负责人: Tan Hoang Nguyen
• 金额: $ 16.4万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 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/10429414
• 关键词: Affect; Alcohol abuse; Alcohols; Animal Model; Architecture; Big Data; Biological; Brain; CRISPR/Cas technology; Caenorhabditis elegans; Code; Data; Data Set; Disease; Etiology; Funding; Genes; Genetic; Genetic Diseases; Genetic Models; Genetic Predisposition to Disease; Genetic Risk; Genetic study; Genomics; Goals; Hi-C; Institutes; 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; base; biobank; career; cohort; comorbidity; cost; disease classification; disorder risk; drinking; exome sequencing; experience; experimental study; flexibility; functional genomics; genetic architecture; genetic information; genome sequencing; genome-wide; genomic data; genomic locus; heuristics; large scale data; multiple omics; neuropsychiatric disorder; non-genomic; rare variant; resilience; risk variant; statistics; trait; transcriptome; translational research program; whole genome

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.

该项目旨在进一步理解酒精使用障碍的基因组病因（AUD）。遗传 研究已经确定了与该疾病相关的基因座。但是，AUD的遗传结构并非 充分解释。当前，该疾病的全基因组变异数据可用于分析，功能 基因组数据集是由财团生成的。从生物库中进行测序数据将允许评估 通过使用替代表型，编码和功能性非编码区域对AUD风险的贡献 酒精使用障碍识别测试（审核）报告的信息。先前的工作表明1）aud 与某些神经精神疾病（NPD）和2）共同变化的研究识别出高遗传重叠 与该疾病相关的生物途径。因此，包含多摩ic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对1）增加遗传发现的功率，2）更好 了解AUD的基因组架构。 AUD与基于审计的遗传相关性很高 表型（审核-P）评估饮酒的问题后果。 AUD和AUDIT-P有 与其他NPD相似的遗传相关性。多个审核P数据集可公开使用，最近 AUD和AUDIT-P数据集的荟萃分析显示，风险基因识别识别的统计能力增加。为了 这个原因，该提议集中于确定有问题的饮酒基因组协会，这 包括aud本身和/或审核-P。为了实现这一研究目标，候选人提出了以下目标。 首先，他计划扩展他以前的方法，以共同分析稀有的外显子变体和其他OMICS数据集。 其次，他将开发整合常见变体，OMIC信息和遗传重叠的方法 信息增加功率以进行多特征分析。第三，他提出了一种综合方法来分析稀有 全基因组测序数据的变体。第四，他将应用这些方法分析大规模变体 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）更好地了解结果 从通过RNA干扰和CRISPR-CAS9实验的计算方法。 Nguyen博士有一个 数学，统计和统计遗传学的背景；但是，他在 AUD遗传学领域。包括该领域的主要专家在内的导师团队将帮助他1）获得专业知识 在心理病理学的基本原理中，与酒精相关的表型，NPD和酒精相关的疾病学 表型； 2）了解遗传模型生物，RNAi和CRISPR-Cas9实验 验证计算结果； 3）在其他类型的OMIC数据中获得专业知识； 4）需要开发 他的职业生涯中的独立性。这些培训目标将有助于他成功地进行拟议的工作，并 建立了一项由NIH资助的转化研究计划，重点介绍了AUD及其合并条件。