Genetic Vulnerability for Sustained Multi-Substance Use in MVP

有害物质的使用（酒精、烟草和/或处方阿片类药物）很常见，双胞胎研究表明 显着的遗传作用。此外，酒精与烟草以及烟草与阿片类药物的联合使用，通常 发生表明这些行为的环境和遗传风险是重叠的。然而，已查明 遗传变异只能解释个体或组合表型变异的一小部分 物质的使用。旨在识别跨物质的共享遗传途径（多效性）的研究已经 产生不一致的结果。这些性状基因发现的主要挑战是表型 模糊性、测量偏差和统计能力不足以检测相关的微小遗传效应 患有复杂的疾病。单独的临床评估通常无法捕获所有感兴趣的物质或 相关的临床因素（例如慢性疼痛），并且可能会存在很大的变化和偏差，具体取决于 患者的健康状况、进行评估的临床环境以及进行评估的临床医生 评估。源自这些的国际疾病管理分类 (ICD) 代码 评估经常被使用，因为它们很容易适用于大量受试者，但它们 可能会增加另一层不准确性和偏见。独特、丰富、纵向的临床数据 退伍军人医疗保健管理局 (VA) 结合百万退伍军人计划提供的数据 (MVP) 使我们能够克服这些限制。我们从广泛使用且重复的电子产品开始 基于健康记录 (EHR) 的指标：有害酒精的 AUDIT-C；目前/过去/从未吸烟状况 烟草；处方阿片类药物的药房填充/补充记录中的吗啡当量日剂量 (MEDD)。 从这些措施得出的纵向汇总指标最初在退伍军人老龄化中得到验证 队列研究 (VACS)，然后扩展到 MVP，根据其他标准对它们进行验证，并在 一个更大、更普遍的样本。重要的是，MVP 还允许我们针对遗传进行验证 标准，先前确定的单核苷酸多态性（SNP）。这产生了电子 基于健康记录 (EHR)、CritErion 验证的纵向 (ExCEL) 表型 与酒精 (1, 2)、烟草 (3) 和处方的标准和含量标准关联性更强 阿片类药物[贝克尔，准备中]比替代表型。全基因组关联研究（GWAS） 使用 ExCEL 表型对酒精、烟草和阿片类药物的研究正在进行中，并且都重现了先前的发现 并产生了许多 SNP 和基因与这些病症的新关联。我们分享了ExCEL 通过 MVP wiki 和 MVP 表型工作组与 Alpha 和 Beta 项目组进行表型。我们是 目前正在进行烟草和酒精 ExCEL 表型（Zhao 和 Dao）的联合 GWAS，并将很快 启动烟草和阿片类药物 ExCEL 表型的联合 GWAS。因为慢性疼痛很强烈 与物质使用相关的，我们建议开发、验证和应用慢性病的 ExCEL 表型 使用数字疼痛评定量表（NRS）重复测量疼痛，并根据功能进行验证 由于 MVP 调查的疼痛和基于先前确定的 SNP 的遗传风险评分而导致的损害。在 未来四年，我们将使用 ExCEL 表型进行物质使用（酒精、烟草、 和处方阿片类药物）和慢性疼痛，将慢性疼痛视为混杂因素，作为必要的暴露， 并作为统一的遗传纽带。我们希望我们的分析能够揭示遗传因素的影响程度 慢性疼痛和物质使用之间共有的，并揭示了疼痛如何影响慢性疼痛的表达 物质相关特征的遗传风险因素。

Harmful substance use (alcohol, tobacco, and/or prescription opioids) is common and twin studies suggest a substantial genetic role. Further, combined use of alcohol with tobacco and tobacco with opioids, commonly occurs suggesting that environmental and genetic risks for these behaviors overlap. However, identified genetic variation explains only a small proportion of the phenotypic variation for individual or combined substance use. Studies aiming to identify shared genetic pathways across substances (pleiotropy) have yielded inconsistent results. Among the major challenges to gene finding for these traits are phenotypic ambiguity, measurement bias, and inadequate statistical power to detect the small genetic effects associated with complex disorders. Individual clinical assessments often do not capture all substances of interest or relevant clinical factors (e.g., chronic pain) and are subject to substantial variation and bias depending upon the patient's health state, the clinical setting in which the assessment occurs, and the clinician making the assessment. Administrative International Classification of Diseases (ICD) codes derived from these assessments are frequently used because they are readily available for large numbers of subjects, but they can add another layer of inaccuracy and bias. The unique, rich, longitudinal clinical data available within the Veterans Healthcare Administration (VA) combined with data available from the Million Veteran Program (MVP) is enabling us to overcome these limitations. We began with widely available and repeated electronic health record (EHR)-based metrics: AUDIT-C for hazardous alcohol; current/past/never smoking status for tobacco; and morphine equivalent daily dose (MEDD) from pharmacy fill/refill records for prescription opioids. Longitudinal summary metrics derived from these measures were initially validated in the Veterans Aging Cohort Study (VACS) and then extended to MVP, validating them against additional criterion standards and in a much larger, more generalizable, sample. Importantly, MVP also allowed us to validate against genetic criterion standards, previously identified single nucleotide polymorphisms (SNPs). This yielded Electronic Health Record (EHR)-based, CritErion-validated Longitudinal (ExCEL) phenotypes that were substantially more strongly associated with criterion and content standards for alcohol (1, 2), tobacco (3), and prescription opioids [Becker, in preparation] than alternative phenotypes. Genome-wide association studies (GWASs) of alcohol, tobacco, and opioids using ExCEL phenotypes are underway and have both reproduced prior findings and yielded many novel associations of SNPs and genes with these conditions. We have shared ExCEL phenotypes with Alpha and Beta project groups via the MVP wiki and the MVP Phenotype Workgroup. We are currently conducting joint GWASs of ExCEL phenotypes for tobacco and alcohol (Zhao and Dao) and will soon initiate joint GWASs of ExCEL phenotypes for tobacco and opioids. Because chronic pain is strongly associated with substance use, we propose to develop, validate, and apply an ExCEL phenotype of chronic pain using repeated measures of the Numeric Pain Rating Scale (NRS) and validating it against functional impairment due to pain from the MVP survey and a genetic risk score based on previously identified SNPs. In the next four years, we will use ExCEL phenotypes to conduct GWASs of substance use (alcohol, tobacco, and prescription opioids) and chronic pain, treating chronic pain as a confounder, as a necessary exposure, and as a unifying genetic link. We expect that our analyses will reveal the extent to which genetic factors are shared between chronic pain and substance use and shed light on how pain may influence the expression of genetic risk factors for substance-related traits.