Prediction and Stratification of Substance Use in Bipolar Disorder to Improve Disease Outcome (PASS-BD)

Bipolar disorder is ranked as one of the leading sources of disability among all diseases worldwide, with many comorbidities including substance use disorders (SUDs) which are especially common (prevalence 20-70%). Identifying the underlying pathophysiology is imperative and can lead to major health benefits through better treatment and prevision strategies. This research project focuses on advancing our knowledge on the genetic architecture of bipolar disorder by large scale genome-wide association studies (GWAS). We also aim to identify shared genetic risk factors between bipolar disorder and substance use disorders. Finally we aim to combine this genetic data with other clinical and environmental data to develop novel algorithms to predict development, and age of onset, of bipolar disorder and substance use comorbidities, and then to stratify affected individuals based on their individual risk profiles. This will be achieved by adopting a novel big-data approach that will integrate the strengths and advantages of large-scale consortia and biobanks with extensive registry and questionnaire data, and process these data using novel statistical tools.

Individuals with severe mental illness, such as bipolar disorder (BD), have significantly higher mortality rates than the general population. Moreover, BD is ranked as one of the leading sources of disability among all diseases worldwide, with ubiquitous comorbidities including substance use disorders (SUDs) which are especially common (prevalence rates 20-70%). Recent large scale genetic studies have shed some light on the underlying genetic architecture and aetiology of BD. However, these findings only account for a small proportion of the variance in the disorder, are limited to samples of European ancestry, lack information relating to the clinical course of the disorder and do not account for environmental and other non-genetic contributors to the disorder. Opportunely, PASS-BD has access to unprecedented multi-ancestry global BD case/control samples (~3mill), the world’s largest birth cohort (~255k; including ~55k trios (father, mother and child, who will reach BD onset age over the course of the project) and other longitudinally characterized Nordic population cohorts, and novel statistical methods which provide an ideal juncture to comprehensively understand BD-SUD comorbidity aetiology, and to translate these findings for potential clinical utility. To address the critical knowledge gaps and to exploit recent advances, PASS-BD will identify risk factors (genes, environment and their interplay) for BD, and BD-SUD comorbidity, and translate its findings to develop prediction and stratification algorithms with the ultimate goal of ‘precision psychiatry’. This will be achieved by adopting a novel big-data approach that will integrate the strengths and advantages of large-scale consortia and biobanks with extensive registry and questionnaire data, and process these data using novel statistical tools.