Pharmacogenomics of Mood Disorders.

Overall Abstract Single gene and GWAS analyses of pharmacological treatment response in depression and bipolar disorder have had varying success in identifying functionally relevant genes associated with pharmacological treatment response such as response to SSRI in depression and lithium in bipolar disorder. Hence, the need to better understand the complex genetic underpinnings of pharmacoresponse may include methodologies such as RNA-seq and microarray analyses. Such approaches contribute to a more in in-depth functional characterization of risk loci related to treatment response and to characterize the genetic underpinnings of both adverse events (e.g. induction of mania by antidepressants) and of medical comorbidity contributing to poor treatment response. In addition, gene expression profile and eQTLs can assist an improved understanding of the functional genomics of mood disorders. Specifically, recent large-scale GWAS meta-analyses (ConLiGen) have implicated multiple novel loci, two of which met the threshold for genome-wide significance: GADL1 (Chen et al., 2014) and an intergenic locus on chromosome 21 (Hou et al., 2016). The functional implications of the related genes remain to be described. In a first presentation, results will be presented from whole blood RNA-seq-data in a subsample of ConLiGen to validate and characterize these associations with clinical lithium treatment response and how these loci impact gene function and lithium response (David Stacey). Addressing the need to investigate the genetic underpinning of a broader range of treatment outcomes and adverse events, the genetic contribution of a potential induction of mania by antidepressants will be discussed in a second presentation, that shows results from a GWAS of antidepressant-induced mania (Joanna Biernacka). Among other factors, the co-morbidity of depression with other psychiatric and cardiometabolic disorders may negatively influence antidepressants treatment outcome. Moreover, response to antidepressants is a complex trait with substantial contribution from a large number of common genetic variants of small effect. Hence, use of polygenic scores (PGS) could be a better alternative to estimate the overall effect of genetic factors on treatment response and assess the importance of co-morbidities and related phenotypes. To investigate this further, we evaluated whether PGS derived from 25 recognized comorbid cardiometabolic and psychiatric disorders, personality traits and educational attainment predict treatment outcome to SSRIs in major depressive disorder using the ISPC data (Bernhard Baune). Previous studies aiming to identify the transcriptome signature of depression are inconsistent with low replicability at the single gene level in both brain and peripheral tissues. In a fourth presentation, results from analyses of whole blood transcriptomes of 521 elderly people from the general population (The Sydney Memory and Age Study, MAS, Sydney) are shown to describe molecular networks involved in geriatric depression. Functional characterisation of two out of 29 modules of the co-expression network indicated that abnormalities in structural constituents of ribosome may be involved in the pathophysiology of geriatric depression. In addition, results of pathway analyses using Ingenuity Pathway Analysis software will be shown (Liliana Ciobanu). [ABSTRACT FROM AUTHOR]