Genetic Vulnerability for Sustained Multi-Substance Use in MVP

有害物质使用（酒精，烟草和/或处方阿片类药物）很常见，双胞胎研究表明， 重要的基因作用。此外，酒精与烟草和烟草与阿片类药物的联合使用，通常 这表明这些行为的环境和遗传风险重叠。然而，经鉴定 遗传变异只能解释一小部分个体或组合的表型变异。 物质使用。旨在确定跨物质共享遗传途径（多效性）的研究 产生了不一致的结果。这些性状的基因发现面临的主要挑战之一是表型 模糊性、测量偏差和统计功效不足，无法检测相关的小遗传效应。 复杂的疾病。个体临床评估通常不能捕获所有感兴趣的物质， 相关临床因素（例如，慢性疼痛），并且取决于 患者的健康状态、进行评估的临床环境以及进行评估的临床医生。 考核国际疾病分类（ICD）代码来源于这些 评估经常使用，因为它们随时可用于大量受试者，但它们 可能会增加另一层不准确和偏见。独特的，丰富的，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。我们开始广泛使用和重复的电子 基于健康记录（EHR）的指标：危险酒精的AUDIT-C; 烟草;以及处方阿片类药物药房填充/再填充记录中的吗啡等效日剂量（MEDD）。 从这些措施中得出的纵向汇总指标最初在退伍军人老龄化 队列研究（VACS），然后扩展到MVP，根据其他标准标准验证它们，并在 一个更大更普遍的样本重要的是，MVP还允许我们验证遗传 标准，先前鉴定的单核苷酸多态性（SNP）。这产生了电子 基于健康记录（EHR）的、Criteron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相关性更强 阿片类药物[贝克尔，准备]比替代表型。全基因组关联研究（GWAS） 使用ExCEL表型的酒精、烟草和阿片类药物正在进行中，并且都复制了先前的发现 并发现了许多新的SNP和基因与这些疾病的关联。我们分享了Excel 通过MVP wiki和MVP表型工作组与Alpha和Beta项目组进行表型分析。我们 目前正在进行烟草和酒精（Zhao和Dao）的ExCEL表型联合GWAS，并将很快进行 启动烟草和阿片类药物ExCEL表型联合GWAS。因为慢性疼痛 与物质使用相关，我们建议开发，验证和应用慢性 使用数字疼痛评定量表（NRS）重复测量疼痛，并根据功能 根据MVP调查和基于先前鉴定的SNP的遗传风险评分，评估由于疼痛导致的损伤。在 在接下来的四年里，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，将慢性疼痛作为混杂因素治疗，作为必要的暴露， 作为一个统一的基因链。我们希望我们的分析将揭示遗传因素在多大程度上影响了 慢性疼痛和物质使用之间的共享，并阐明疼痛如何影响 物质相关性状的遗传风险因素。

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.