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: 7126368
• 负责人: RUMI KATO PRICE
• 金额: $ 22.41万
• 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/7126368
• 关键词: Disentangling; Substance; Use; Psychiatric; Disorder

DESCRIPTION (provided by applicant): 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 of substance use and psychiatric disorders. In response to RFA-DA- 05-005, this R01 application requests support for 5 years to conduct coordinated analyses of NLAES, NESARC, Add Health and NHSDA/NSDUH repeated cross-sectional and/or longitudinal adolescent and adult national survey datafiles (each with different strengths and weaknesses) to provide informative results for future human-genome epidemiology (HuGE) aspects of 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 environmental etiological 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 analyses by examining race/ethnicity differences on the 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 in Aims 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 genotypes, pending on the availability of genotype data from NESARC.

描述（由申请人提供）： 有一个快速增长的公共卫生意义的需要，将人类遗传信息纳入人口流行病学数据的分析，以提供更好地了解物质使用和精神疾病共病的生物环境机制。作为对RFA-DA- 05-005的回应，本R 01申请请求提供5年的支持，以对NLAES、NESARC、Add Health和NHDA/NSDUH重复的横断面和/或纵向青少年和成人国家调查样本（各有不同的优势和劣势）进行协调分析，为NESARC未来的人类基因组流行病学（HuGE）方面提供信息性结果。种族/民族、移民和文化适应、家族史以及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（反映分子进化史和最近的人口动态），以捕获遗传和环境病因因素的相互作用。主要表型是相对常见的物质使用和精神疾病或综合征的横截面或纵向共病;多效性概念适用于无关个体。添加候选基因信息有限的健康数据将用于指导NESARC的分析;当基因型信息可从NESARC获得时，除了已从当前NESARC样本中测量的独立环境效应外，人种/种族和个体间的一部分表型方差应通过候选基因主效应、上位性、基因-环境相互作用“解释”。具体分析目标是：1）通过检查两种疾病的共病的种族/民族差异来选择适合于多表型分析的表型，所述共病鉴定可能受相对新的多态性或受相对局部化的环境因素或两者影响的表型; 2）从遗传学角度证实目标1中实现的横截面表型选择; 3）描述对目标1中选择的表型的主要性别和种族/民族特异性环境影响; 4）改进用于目标6）、7）和8）的候选基因的多基因测量; 5）开发药物滥用和精神病合并症的多效性模型（SAPC），以指导目标6）、7）和8）; 6）开发和测试NESARC的横截面多效性模型，包括目标3）中确定的环境措施; 7）开发NESARC的纵向多效性模型; 8）通过用候选基因型代替替代多基因测量来复制目标6）和7）中开发的多效性模型，这取决于来自NESARC的基因型数据的可用性。