Towards personalized medicine in psychiatry : focus on suicide

Abstract

Psychiatric disorders cost an estimated $273 billion annually. This cost comes largely in the form of lost income and the chronic disability that often strikes people when they are young and can last decades. While the monetary costs are quantifiable, the suffering of each individual patient is no less vital. As many as 1 in 5 persons diagnosed with mental illness will commit suicide, a contributing factor in suicide being the second leading cause of death of people age 15-34. There is a critical need to find better ways to identify and help those who are at risk. Understanding mental illness and improving treatment has been difficult due to the heterogeneous and complex etiology of these illnesses. A significant challenge for the field is integrating findings from diverse laboratories all over the world contributing to the ever expanding literature and translating them into actionable treatment. Our lab employs a convergent functional genomics approach which incorporates multiple independent lines of evidence provided by genetic and functional genomic data published in the primary literature as a Bayesian strategy to prioritize experimental findings. Heritability and genetics clearly play an important role in psychiatric disorders. We looked at schizophrenia and alcoholism in separate case-control analyses in order to identify and prioritize genes related to these disorders. We were able to reproduce these findings in additional independent cohorts using polygenic risk scores. We found overlap in these disorders, and identified possible underlying biological processes. Genetics play an important role in identifying clinical risk, particularly at the population level. At the level of the individual, gene expression may provide more proximal association to disease state, assimilating environmental, genetic, as well as epigenetic influence. We undertook N of 1 analyses in a longitudinally followed cohort of psychiatric participants, identifying genes which change in expression tracking an individual’s change in suicidal ideation. These genes were able to predict suicidal behavior in independent cohorts. When combined with simple clinical instruments these predictions were improved. This work shows how multi level integration of genetic, gene expression, and clinical data could be used to enable precision medicine in psychiatry.