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:
    10429414
  • 负责人:
  • 金额:
    $ 16.4万
  • 依托单位:
  • 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

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 研究已经确定了与该疾病相关的基因座;然而,AUD的遗传结构尚未被确定。 充分解释。目前,该疾病的全基因组变异数据可用于分析,并且功能性分析可以在基因组中进行。 基因组数据集是由联合体产生的。生物库的测序数据将允许评估 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。此前的研究表明,1)澳元 与一些神经精神疾病(NPD)有很高的遗传重叠,2)共同变异研究确定 与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疾病相关的其他基因座。因此,在本发明中, 候选人计划使用综合方法来1)增加基因发现的能力,2)更好地 了解AUD的基因组结构。AUD与基于AUDIT的 表型(AUDIT-P),评估饮酒的问题后果。AUD和AUDIT-P 与其他NPD有相似的遗传相关性。多个AUDIT-P数据集是公开的,最近的 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增加。为 因此,这项建议的重点是确定有问题的酒精使用的基因组关联, 包括AUD本身和/或AUDIT-P。为了实现这一研究目标,候选人提出了以下目标。 首先,他计划扩展他以前的方法,以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,以增加多性状分析的能力。第三,他提出了一种分析罕见的综合方法, 全基因组测序数据的变体。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来自 这些方法。此外,他建议使用遗传模型生物(C。elegans)来更好地理解结果 通过RNA干扰和CRISPR-Cas9实验的计算方法。阮医生 数学、统计学和统计遗传学背景;但他以前没有这方面的经验。 澳大利亚的遗传学领域。包括该领域顶尖专家在内的导师团队将帮助他获得专业知识 在精神病理学的基本原理,酒精相关的表型,NPD的疾病分类学和酒精相关的 表型; 2)获得遗传模型生物,RNAi和CRISPR-Cas9实验的理解, 验证计算结果; 3)获得其他类型组学数据的专业知识; 4)开发所需的 在职业生涯中的独立性。这些培训目标将帮助他成功地开展拟议的工作, 建立一个NIH资助的转化研究项目,重点关注AUD及其共病状况。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
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Tan Hoang Nguyen其他文献

Tan Hoang Nguyen的其他文献

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{{ truncateString('Tan Hoang Nguyen', 18)}}的其他基金

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:
    10611490
  • 财政年份:
    2022
  • 资助金额:
    $ 16.4万
  • 项目类别:

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