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Background: Background: Common psychiatric disorders are clinically heterogeneous and often display many overlapping symptoms as well as comorbidity across psychiatric disorders, hence complicating the genetic investigation of these disorders. To facilitate new discoveries and gain better understanding of the genetics of psychiatric problems, large well-phenotyped biobank samples are greatly needed. The Estonian Biobank (EstBB;N=200,000) is a data-rich national biobank that joins genomic, biomarker, electronic health record (EHR), national registry and questionnaire data to significantly advance the current knowledge of the genetic underpinnings of complex and rare diseases. We aimed to expand the EstBB database with rich phenotypic questionnaire-based information on a broad set of symptoms of common psychiatric disorders and related phenotypes. Methods: Methods: We compiled an online questionnaire “Wellbeing and mental health” which included self-report brief screening instruments to capture a broad range of symptoms on current and/or lifetime depression, anxiety disorders, mania, post-traumatic stress disorder, eating disorders, attention deficit/hyperactivity disorder, psychotic experiences, suicidal thoughts and behavior, cognition, substance (ab)use, problematic gambling and related risk- and protective factors, such as childhood adversity, stressful life events, social support, lifestyle, sociodemographic factors and life satisfaction. In addition, the participants were asked about the side effects, discontinuation and switching of medications prescribed for a mental health problem in their lifetime. Since the start of the data collection coincided with the peak of the second COVID-19 wave in Estonia, we included items related to current perceived stress, burnout and change in mental health during the pandemic. When selecting instruments, preference was given based on 1) reliability and validity of the instruments;2) whether an Estonian adaptation and/or validation has been conducted;3) application in other large biobanks (e.g. the UK Biobank) for future harmonization or replication;4) short length. Results: Results: The invitations to participate in the online mental health study were e-mailed out to all living EstBB participants with available e-mail addresses (N=184,622), followed by two e-mail reminders. The active data collection phase spanned over three months from March to May 2021. The preliminary response rate was 46.7% (N = 86,222) out of all who were contacted. We will present preliminary results on the comparison of respondents and non-respondents based on demographic data, psychiatric diagnoses in EHR and psychiatric polygenic risk scores. Discussion: Discussion: We have significantly expanded the EstBB resource with phenotypic information on a broad range of psychiatric symptoms and related phenotypes to facilitate future research in psychiatric genetics. Disclosure: Nothing to disclose.

The reasons for variability in treatment response in major depressive disorder (MDD) are not fully understood, but there is accumulating evidence suggesting that therapeutic outcomes of antidepressants can be influenced by genetic factors. In the present study we applied the microarray Illumina platform for whole genome expression profiling in depressive patients treated with escitalopram medication in order to identify genes underlying response to antidepressant treatment. The initial study sample consisted of 135 outpatients with major depressive disorder (mean age 31.1±11.6 years, 68% females) treated with escitalopram 10-20mg/day for 12 weeks, from which 87 patients (55 females) were included in gene expression analyzing. The gene expression profiles were measured on peripheral blood cells at baseline, at week 4 and at the end of treatment (week 12) using BeadChips Illumina. The fold change was used to demonstrate rate of changes in average gene expressions between studied groups. Statistical analyses were performed using the false discovery rate (FDR). The most interesting gene, which showed the predictive effect on treatment outcome by delineating low dose responders and treatment-resistant patients at the beginning of medication, was NLGN2, belonging to a family of neuronal cell surface proteins and involving in synapse formation. In addition, the several gene clusters, related to immune response, signal transduction and neurotrophin pathway, have distinguished responders from non-responders at the week 4 of treatment. After 4 weeks of escitalopram treatment (10mg/day), the YWHAZ gene has showed the highest transcriptional change in responders as compared with non-responders. Finally, at the end of the treatment we noticed that at least three genes (NR2C2, ZNF641, FKBP1A) have been strongly associated with resistance to escitalopram. Thus the results of this study support that exploration of peripheral gene expression is a useful tool in the further identification of novel genetic biomarkers for antidepressant treatment response.