Big data analytics for the evaluation of whole genome sequence and transcriptome data in alcohol research

大数据分析用于评估酒精研究中的全基因组序列和转录组数据

• 批准号: 9753834
• 负责人: Qian Peng
• 金额: $ 16.15万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9753834
• 关键词: Affect; Alcohol dependence; Alcoholism; Alcohols; American; Autopsy; Behavioral Sciences; Big Data; Big Data Methods; Bioinformatics; Biology; Brain; Communities; Comorbidity; Complex; Data; Data Set; Databases; Dimensions; Disease; Drug Addiction; Ethnic group; Etiology; European; Evaluation; Family Study; Gene Expression; Genes; Genetic; Genetic Diseases; Genomics; Genotype; Goals; Heritability; Heterogeneity; Human; Human Genome; Individual; Machine Learning; Maps; Mental disorders; Methodology; Methods; Modeling; Native Americans; New South Wales; Pathway interactions; Phenotype; Population; Predisposition; Preventive Intervention; Protocols documentation; Public Domains; Public Health; Quantitative Trait Loci; Randomized; Research; Risk; Risk Factors; Sampling; San Francisco; Siblings; Statistical Methods; Structure; Substance Use Disorder; System; Testing; Variant; addiction; alcohol and other drug; alcohol intervention; alcohol research; alcohol use disorder; base; causal model; cohort; differential expression; disability; endophenotype; epidemiology study; epigenomics; family structure; genetic risk factor; genome wide association study; genomic data; high risk; high risk population; individualized prevention; innovation; mortality; non-alcoholic; novel; pleiotropism; predictive modeling; problem drinker; programs; risk sharing; statistics; tissue resource; tool; trait; transcriptome; transcriptomics; whole genome

PROJECT SUMMARY/ABSTRACT Large-scale U.S. epidemiological studies demonstrate that alcohol use disorders are highly prevalent, highly co-morbid with other psychiatric disorders, disabling, and often go untreated. Compared with other U.S. ethnic groups, Native Americans have the highest rates of alcohol and other drug dependence, and it is associated with particularly significant disability and mortality. Thus studies that identify specific genetic risk factors for alcohol use disorders in the general U.S. population, and especially in Native Americans, are of high public health importance. Alcohol use disorders are complex genetic diseases sensitive to environmental conditions that require complex data strategies to uncover the underlying risk factors. Although recent years have seen significant advancement in our understanding in the biology and genetics of the disorders, exactly how these factors interact in an individual to confer risk or protection from alcohol use disorders is still unclear. Further, the genetic factors identified in the human genome thus far by conventional methods appear to only explain a very small fraction of the overall heritability for the disorders. The overall objective of this research program is to identify the complex genetic and genomic factors that affect susceptibility to alcohol use disorders and related comorbidities through novel and innovative quantitative methods and big data analytics. The proposed study will utilize whole-genome sequence (WGS) data from a unique high-risk Native American population and a European American population along with gene expression data of alcoholic human brains. The project will develop methodology to analyze WGS data with unique relevance to alcohol research. Selected multivariate, graphical, and dimension-reduction modeling tools will be used in combination with mixed models suitable for genomic data with both population and family structures to dissect polygenic basis for alcohol use disorders and shared genetic risk factors for alcohol use disorders and comorbid disorders. Mixture models and clustering methods will be employed to uncover heterogeneous genetic influences. Differential genetic effects at various levels of heterogeneity will be tested with rigorous statistical methods. The project will identify population and ancestry-specific genetic risk factors and shared risk factors across populations and determine their differential influences on susceptibility to alcohol use disorders. Endophenotypes will also be investigated to help identify unique risk factors for alcohol use disorder traits. The project will further identify alcohol- and addiction-relevant pathways and networks that are differentially expressed in alcoholic brains, and establish directions of causations by combining gene expression data with WGS data, and applying instrumental variable approaches. Finally, an integrated system approach will be taken by further leveraging epigenomic maps and annotation databases in the public domain to build predictive and potentially causal models aimed at more “personalized” prevention and intervention for alcohol use disorders in specific high-risk groups and individuals.

项目摘要/摘要 美国大规模的美国流行病学研究表明，酒精使用障碍非常普遍， 与其他精神病障碍，致残，经常不受治疗。与其他美国相比 族裔，美洲原住民的酒精和其他毒品依赖性最高，这是 与特别重要的残疾和死亡率相关。确定特定遗传风险的研究 美国普通人群，尤其是美洲原住民的饮酒障碍因素很高 公共卫生重要性。酒精使用障碍是对环境敏感的复杂遗传疾病 需要复杂的数据策略来揭示潜在风险因素的条件。虽然近年来 我们对疾病的生物学和遗传学的理解有了显着的进步，正是 这些因素如何在个人中与会议风险或免受酒精使用障碍的保护相互作用尚不清楚。 此外，到目前为止，通过常规方法在人类基因组中鉴定的遗传因素似乎仅是 解释疾病总体遗传力的一小部分。 该研究计划的总体目的是确定复杂的遗传和基因组因素 通过新颖而创新的 定量方法和大数据分析。拟议的研究将利用全基因组序列（WGS） 来自独特的高风险原住民和欧美人口的数据以及基因 酒精人类大脑的表达数据。该项目将开发方法来分析WGS数据 与酒精研究的独特相关性。选定的多元，图形和尺寸还原建模工具 将与适用于人口和家族的基因组数据的混合模型结合使用 剖析酒精使用障碍的多基因基础的结构和饮酒的共享遗传风险因素 疾病和合并症。混合模型和聚类方法将被雇用以发现 异质遗传影响。将测试各种异质性的差异遗传效应 使用严格的统计方法。该项目将确定人口和特定于祖先的遗传危险因素 并共享人群之间的风险因素，并确定其对易感性的影响 酒精使用障碍。还将研究内型型，以帮助确定酒精的独特风险因素 使用障碍特征。该项目将进一步识别与酒精和成瘾的途径和网络 通过结合基因，在酒精大脑中的表达方式不同，并建立了因果关系的方向 用WGS数据表达数据，并应用仪器可变方法。最后，一个集成的系统 将通过进一步利用公共领域中的表观基因组图和注释数据库来采取方法 建立预测性和潜在的因果模型，旨在更“个性化”的预防和干预措施 特定高危群体和个人的酒精使用障碍。