Genetic architecture of substance use disorders and major depression

重度抑郁障碍(MDD)通常发生在有酒精使用障碍的个体中 (AUD)和处方阿片使用障碍(POUD)，这种共病在退伍军人中非常普遍。 鉴于结果不佳(例如，复发、放弃治疗、功能受损)和增加的死亡率 患有这些共病障碍的人，更好地了解他们的病因和 共病具有重要的临床意义。尽管这些发展的共同途径 已经提出了共病(例如，自我用药)、MDD的共同遗传途径等 物质使用障碍(SODS)尚未得到很好的描述，这一努力因表型而变得复杂 异质性。例如，并不是所有的人都有相同的物质使用率或SUD的严重程度 症状。与表型的复杂性一致，这些肥皂很可能在遗传上是不同的， 具有导致AUD或POUD的多个遗传途径。因此，通过提炼SUD表型和减少 表型的异质性，研究大量明确的病例和对照，我们可能会减少 遗传异质性，并确定真正的遗传关联。此外，百万退伍军人计划(MVP) 样本使因果路径的研究成为可能。CDA-2提案的目标是 描述AUD和POUD的遗传结构，有无MDD，确定新的关系 疾病的遗传易感性与其他表型(即多效性)之间的关系，以及具体的因果路径 使用MVP示例。具体目标是：(1)确定退伍军人患有AUD、POUD和共生MDD 并使用ICD-9/10诊断和自我诊断来表征他们的抑郁症状和药物使用情况 报告措施；(2)评估AUD和AUD的遗传结构和因果关系 MDD；(3)POUD和POD与MDD共生的遗传结构和因果关系。 使用所有可用的数据，患有AUD的退伍军人将在芬奇被识别并根据 存在共生的MDD。同样，退伍军人长期使用处方阿片类药物治疗， 将被诊断为阿片类药物使用障碍，并确定他们的MDD病史。密钥上的数据 医疗和精神并存(例如，疼痛、创伤后应激障碍)也将被提取。对照组和对照组 未合并AUD或POUD和MDD的退伍军人以及仅有MDD的退伍军人将被确定。我们还将 使用酒精使用障碍识别测试提取自我报告的酒精消费数据- 使用患者健康问卷-2的消费和自我报告的抑郁症状 在初级保健中定期实施。三项独立的全基因组关联研究 将在上执行带MDD的SUD(第一个AUD，第二个POUD)、不带MDD的SUD和MDD(不带SUD) 使用Plink的GenISIS平台。使用这些GWA的汇总统计数据，多基因风险评分(PR) 将在3个独立样本中计算(下一版本的MVP[N=~200,000]，PennMedicine 生物库[N=&gt；63,000]和耶鲁-宾夕法尼亚大学的样本[N=&gt；17,300个表型深刻的个体])。我们还将 执行下游分析(例如，SNP H2、注释、遗传关联)、表型范围关联 对独立样本进行比对分析，并采用孟德尔随机化方法评估遗传因果关系。 通过提高我们对肥皂泡和MDD共病的遗传结构和因果关系的理解， 这些发现将通过识别风险最高的个人，为预防和治疗肥胖症和MDD提供信息。 阐明药物发现的新生物途径，并告知个性化治疗。这 这一努力还将有助于退伍军人管理局进一步治疗抑郁症，抑郁症是自杀风险的主要贡献者 强调了它的临床意义。CDA-2还将为申请者提供有针对性的培训 遗传学，生物信息学，先进的统计方法，以及为成功的退伍军人管理局做准备的勇气 研究生涯专注于改善患有肥皂泡和共病障碍的退伍军人的生活质量。

Major depressive disorder (MDD) occurs commonly among individuals both with alcohol use disorder (AUD) and prescription opioid use disorder (POUD) and such comorbidity is highly prevalent in Veterans. Given the poor outcomes (e.g., relapse, treatment drop out, impaired functioning) and increased mortality of individuals with these comorbid disorders, a better understanding of their etiology and the basis for the comorbidity is of great clinical importance. Although common pathways for the development of these comorbidities have been proposed (e.g., self-medication), the shared genetic pathways of MDD and these substance use disorders (SUDs) have not been well characterized, an effort that is complicated by phenotypic heterogeneity. For example, not all individuals present with the same rate of substance use or severity of SUD symptoms. Consistent with the phenotypic complexity, these SUDs are likely to be genetically heterogeneous, with multiple genetic pathways leading to AUD or POUD. Thus, by refining the SUD phenotype and reducing the phenotypic heterogeneity, studying a large number of well-defined cases and controls, we may reduce the genetic heterogeneity and identify true genetic associations. Moreover, the Million Veteran Program (MVP) sample makes possible the investigation of causal pathways. The objectives of this CDA-2 proposal are to characterize the genetic architecture of AUD and POUD, with and without MDD, identify novel relationships between genetic liability for the disorders and other phenotypes (i.e., pleiotropy), and specify causal pathways using the MVP sample. The specific aims are to: (1) identify Veterans with AUD, POUD, and co-occurring MDD and characterize their depressive symptomatology and substance use using ICD-9/10 diagnoses and self- report measures; (2) assess the genetic architecture and causal relations of AUD and AUD with co-occurring MDD; and (3) the genetic architecture and causal relations of POUD and POUD with co-occurring MDD. Using all available data, Veterans with AUD will be identified in VINCI and categorized based on the presence co-occurring MDD. Similarly, Veterans treated chronically with prescription opioids who have been diagnosed with an opioid use disorder will be identified and their MDD history ascertained. Data on key medical and psychiatric comorbidities (e.g., pain, PTSD) will also be extracted. Control and comparison groups of Veterans without comorbid AUD or POUD and MDD and with MDD alone will be ascertained. We will also extract self-reported alcohol consumption data using the Alcohol Use Disorders Identification Test- Consumption and self-reported depressive symptoms using the Patient Health Questionnaire-2, both administered regularly in primary care. Three separate genome-wide association studies of individuals with an SUD (first AUD, secondly POUD) with MDD, an SUD without MDD, and MDD (no SUD) will be conducted on the GenISIS platform using PLINK. Using summary statistics from these GWAS, polygenic risk scores (PRS) will be calculated in 3 independent samples (the next release of MVP [N = ~200,000], the PennMedicine BioBank [N = >63,000], and the Yale-Penn sample [N = >17,300 deeply phenotyped individuals]). We will also perform downstream analyses (e.g., SNP h2, annotation, genetic correlation), phenome-wide association analyses of the PRS in independent samples, and Mendelian randomization to assess genetic causal relations. By improving our understanding of the genetic architecture and causality of comorbid SUDs and MDD, these findings will inform prevention and treatment of SUDs and MDD by identifying individuals at greatest risk, elucidating novel biological pathways for medications discovery, and informing personalized treatment. This effort will also contribute to the VA’s efforts to treat depression, a leading contributor to suicide risk, further underscoring its clinical implications. This CDA-2 will also provide the applicant with focused training in genetics, bioinformatics, advanced statistical methods, and grantsmanship to prepare her for a successful VA research career focused on improving the quality of life for Veterans with SUDs and comorbid disorders.