Genetic architecture of substance use disorders and major depression

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.