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.

项目概要/摘要 美国大规模流行病学研究表明，酒精使用障碍非常普遍， 与其他精神疾病高度共存，致残，并且与美国其他地区相比常常得不到治疗。 种族群体中，美洲原住民的酒精和其他药物依赖率最高，而且 与特别严重的残疾和死亡率相关，因此确定特定遗传风险的研究。 在美国普通人群中，尤其是美洲原住民中，造成酒精使用障碍的因素非常多 酒精使用障碍是对环境敏感的复杂遗传性疾病。 尽管近年来，这种情况需要复杂的数据策略来揭示潜在的风险因素。 我们对这些疾病的生物学和遗传学的理解取得了重大进展，确切地说 这些因素如何在个体中相互作用，从而带来酒精使用障碍的风险或提供保护，目前尚不清楚。 此外，迄今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 解释了这些疾病的总体遗传力的一小部分。 该研究计划的总体目标是确定复杂的遗传和基因组因素， 通过新颖和创新影响酒精使用障碍和相关合并症的易感性 拟议的研究将利用全基因组序列（WGS）。 来自独特的高风险美洲原住民群体和欧洲裔美国人群体的数据以及基因 该项目将开发分析 WGS 数据的方法。 与酒精研究的独特相关性。选定的多变量、图形和降维建模工具。 将与适合人群和家族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和酒精使用的共同遗传风险因素的结构 将采用混合模型和聚类方法来揭示疾病和共病。 将测试不同水平异质性的差异遗传效应。 该项目将通过严格的统计方法确定人群和血统特定的遗传风险因素。 以及不同人群共有的危险因素，并确定它们对易感性的不同影响 还将研究酒精使用障碍的内表型，以帮助识别酒精的独特危险因素。 该项目将进一步确定与酒精和成瘾相关的途径和网络。 在酗酒者大脑中差异表达，并通过组合基因确定因果关系的方向 表达数据与 WGS 数据，并应用工具变量方法，最后形成一个集成系统。 将通过进一步利用公共领域的表观基因组图谱和注释数据库来采取方法 建立预测和潜在因果模型，旨在更加“个性化”的预防和干预 特定高危人群和个人的酒精使用障碍。