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.

有一个快速增长的需要，公共卫生的重要性，整合人类遗传信息 分析人口流行病学数据，以更好地了解生物环境 药物使用与精神疾病共病的潜在机制对RFA-DA- 05-005，此R 01应用程序请求5年的支持，以进行NLAES的协调分析， NESARC，添加健康和NHSDA/NSDUH重复横断面和/或纵向青少年和 成人全国调查表（各有不同的长处和短处）， NESARC的未来人类基因组流行病学（HuGE）方面的结果。种族/民族，移民 文化适应、家族史和性别被概念化为关键的“低分辨率” 遗传-行为-社会（G-B-S）标记（反映分子进化历史和最近的 种群动态），以捕捉遗传和环境致病因素的相互作用。主要 表型是相对常见物质使用的横截面或纵向共病， 精神障碍或综合征;多效性的概念适用于无关的个体。添加 候选基因信息有限的健康数据将用于指导NESARC分析;当 基因型信息可以从NESARC获得， 种族/民族和个体应该通过候选基因主效应，上位性， 基因与环境的相互作用，以及已经可以测量的独立环境效应 从目前的NESARC档案中。具体的分析目的是：1）选择适合的表型 通过检查两种疾病共患病的种族/民族差异进行多表型分析 识别可能受相对较新的多态性或相对较新的多态性影响的表型， 局部环境因素或两者兼而有之; 2）证实了在 目的1：从遗传学的角度; 3）描述主要的性别和种族/民族特异性环境 对目标1中选择的表型的影响; 4）改进多基因措施， 用于目的6）、7）和8）的候选基因; 5）开发物质使用滥用的多效性模型， 精神科合并症（SAPC），以指导目标6），7）和8）; 6）开发和测试横截面 NESARC的多效性模型，包括目标3）中确定的环境措施; 7）制定 NESARC的SAPC纵向多效性模型; 8）复制 目的6）和7）通过用候选基因型代替替代多基因措施， 来自NESARC的基因型数据的可用性。