Disentangling Substance Use and Psychiatric Disorder Comorbidity for Future HuGE

解开未来 HuGE 的药物使用和精神疾病合并症

• 批准号: 7500010
• 负责人: RUMI KATO PRICE
• 金额: $ 4.89万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-30 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7500010
• 关键词: Acculturation; Address; Adolescent; Adult; Alcohol or Other Drugs use; Alcohols; Applied Genetics; Asians; Behavioral Genetics; Biology; Candidate Disease Gene; Capital; Clinical; Comorbidity; Condition; Cross-Sectional Studies; Data; Data Collection; Data Set; Databases; Diagnostic and Statistical Manual; Disease; Doctor of Philosophy; Drug abuse; Environment; Environmental Risk Factor; Epidemiology; Ethnic Origin; Extramural Activities; Family history of; Figs - dietary; Future; Gender; General Population; Genes; Genetic; Genetic Epistasis; Genetic Polymorphism; Genome; Genome Scan; Genotype; Goals; Health; Hispanics; Household; Human Genetics; Human Genome; Illicit Drugs; Immigration; Individual; Localized; Longitudinal Studies; Measurable; Measures; Mental disorders; Modeling; Molecular; National Institute on Alcohol Abuse and Alcoholism; Personal Communication; Pharmaceutical Preparations; Phenotype; Population; Population Database; Population Dynamics; Price; Public Domains; Public Health; Race; Recording of previous events; Request for Applications; Research Personnel; Resolution; Sample Size; Sampling; Series; Standards of Weights and Measures; Structure; Subgroup; Surveys; Syndrome; Testing; Variant; base; concept; ethnic difference; gene environment interaction; genetic epidemiology; genetic variant; improved; interest; pleiotropism; social

There is a rapidly increasing need of public health significance to integrate human genetic information into analyses of population epidemiologic data to provide better understanding of biology-environment mechanisms underlying comorbidity ofsubstanceuse and psychiatric disorders. Inresponse toRFA-DA- 05-005, this R01 application requests support for 5 years to conduct coordinated analyses of NLAES, NESARC,Add Health and NHSDA/NSDUH repeatedcross-sectional and/or longitudinal adolescent and adult national survey datafiles (each with different strengths and weaknesses) to provide informative resultsforfuture human-genome epidemiology (HuGE) aspectsof NESARC.Race/ethnicity, immigration and acculturation, and family history, as well as gender, are conceptualized as key "low-resolution" genetic-behavioral-social (G-B-S) markers (reflecting molecular evolutionary history and recent population dynamics) to capture the interplay of genetic and environmentaletiological factors. The main phenotypes are cross-sectional or longitudinal comorbidity of relatively-common substance use and psychiatric disorders or syndromes; the pleiotropy concept is applied to unrelated individuals. Add Health data with limited candidate gene information will be used to guide analyses for NESARC; when genotype information becomes available from NESARC, a portion of the phenotypic variance across race/ethnicity and individuals should be "explained away" by candidate gene main effects, epistasis, gene -environment interactions, in addition to independent environmental effects already measurable from the current NESARC datafiles. Specific analysis aims are to: 1) select phenotypes suitable for multiple-phenotype analysesbyexaminingrace/ethnicitydifferences onthe comorbidity of two disorders that identify phenotypes that are likely to be influenced by relatively new polymorphisms or by relatively localized environmental factors or both; 2) corroborate cross-sectional phenotype selection achieved in Aim 1 from a genetic perspective; 3) delineate major gender- and race-/ethnic-specific environmental influences on the phenotypes selected in Aim 1; 4) improve polygenic measures standing-in for candidate genes for use inAims 6), 7) and 8); 5)develop pleiotropy models of substance use abuse and psychiatric comorbidity (SAPC) to guide Aims 6), 7), and 8); 6) develop and test cross-sectional pleiotropy models for NESARC including environmental measures identified in Aim 3); 7) develop longitudinal pleiotropy models of SAPC for NESARC; 8) replicate the pleiotropy models developed in Aims 6) and 7) by replacing stand-in polygenic measures with candidate geneotypes, pending on the availability of genotype data from NESARC.

具有公共卫生意义的整合人类基因信息的需求迅速增加 对人口流行病学数据进行分析，以更好地了解生物环境 药物使用和精神障碍共病的潜在机制。回应Torfa-DA- 05-005，此R01应用程序请求5年支持，以对NLAE进行协调分析， NESARC，增加Health和NHSDA/NSDUH重复横断面和/或纵向青少年和 成人国家调查数据文件(每个文件都有不同的优点和缺点)，以提供信息 NESARC未来人类基因组流行病学(GHIGH)方面的结果种族/民族、移民 而文化适应、家族史以及性别被认为是关键的“低分辨率”。 遗传-行为-社会(G-B-S)标记(反映分子进化史和近期 种群动态)，以捕捉遗传和环境致病因素之间的相互作用。主 表型是相对常见的物质使用的横断面或纵向共病 精神障碍或综合症；多效性的概念适用于无关的个体。添加 具有有限候选基因信息的健康数据将用于指导NESARC的分析；何时 基因信息可从NESARC获得，这是表型差异的一部分 种族/民族和个人应该用候选基因的主效应、上位性、 基因-环境相互作用，以及已经可以测量的独立环境影响 从当前的NESARC数据文件中。具体的分析目标是：1)选择适合于 通过检查种族/民族差异对两种疾病的共病进行多表型分析 识别可能受相对较新的多态或相对 局部环境因素或两者兼而有之；2)证实在 目标1从遗传学的角度；3)描述主要的性别和种族/族裔环境 对目标1中选择的表型的影响；4)改进多基因替代措施 候选基因用于AIM 6)、7)和8)；5)开发药物滥用的多效性模型和 精神科共病(SAPC)以指导AIMS 6)、7)和8)；6)开发和测试横断面 NESARC的多效性模型，包括目标3)中确定的环境措施；7)开发 用于NESARC的SAPC的纵向多变性模型；8)复制在 目标6)和7)用候选基因取代替代多基因措施，等待 来自NESARC的基因数据的可用性。