TY - JOUR AU - Comes, A.* AU - Adorjan, K.* AU - Andlauer, T.* AU - Budde, M.* AU - Degenhardt, F.* AU - Forstner, A.J.* AU - Gade, K.* AU - Heilbronner, U.* AU - Kalman, J.* AU - Kondofersky, I. AU - Papiol, S.* AU - Senner, F.* AU - Sivalingam, S.* AU - Falkai, P.* AU - Schulze, T.* C1 - 56756 C2 - 47286 CY - Radarweg 29, 1043 Nx Amsterdam, Netherlands SP - 1300-1301 TI - The role of environmental stress and DNA methylation in the longitudinal course of bipolar disorder. JO - Eur. Neuropsychopharmacol. VL - 29 PB - Elsevier PY - 2019 SN - 0924-977X ER - TY - JOUR AU - Cruceanu, C.* AU - Dony, L. AU - Kontira, A.C.* AU - Fischer, D.S. AU - Roeh, S.* AU - DiGiaimo, R.* AU - Cappello, S.* AU - Theis, F.J. AU - Binder, E.B.* C1 - 57846 C2 - 47950 CY - Radarweg 29, 1043 Nx Amsterdam, Netherlands SP - S7-S8 TI - Brain organoids as models of the developing human brain: deciphering the molecular signature of prenatal stress. JO - Eur. Neuropsychopharmacol. VL - 29 PB - Elsevier PY - 2019 SN - 0924-977X ER - TY - JOUR AU - Czamara, D.* AU - Eraslan, G. AU - Lahti, J.* AU - Figueiredo, A.S.* AU - Girchenko, P.* AU - Lahti-Pulkkinen, M.* AU - Hämäläinen, E.* AU - Kajantie, E.* AU - Laivuori, H.* AU - Villa, P.* AU - Reynolds, R.* AU - Müller, N.S. AU - Theis, F.J. AU - Räikkönen, K.* AU - Binder, E.* C1 - 56753 C2 - 47283 CY - Radarweg 29, 1043 Nx Amsterdam, Netherlands SP - 1037-1037 TI - Genotype, prenatal environment or both-what shapes the newborn´s epigenome? JO - Eur. Neuropsychopharmacol. VL - 29 PB - Elsevier PY - 2019 SN - 0924-977X ER - TY - JOUR AU - Giuranna, J.* AU - Jall, S. AU - Peters, T.* AU - Hebebrand, J.* AU - Müller, T.D. AU - Hinney, A.* C1 - 56752 C2 - 47282 CY - Radarweg 29, 1043 Nx Amsterdam, Netherlands SP - 1138-1138 TI - Genetic and gene expression analysis in CTBP2: A gene derived from genome-wide data in anorexia nervosa and body weight regulation. JO - Eur. Neuropsychopharmacol. VL - 29 PB - Elsevier PY - 2019 SN - 0924-977X ER - TY - JOUR AU - Schaupp, S.* AU - Budde, M.* AU - Kondofersky, I. AU - Papiol, S.* AU - Heilbronner, U.* AU - Gade, K.* AU - Anderson-Schmidt, H.* AU - Kalman, J.* AU - Senner, F.* AU - Andlauer, T.F.M.* AU - Rietschel, M.* AU - Degenhardt, F.* AU - Müller, N.S. AU - Theis, F.J. AU - Schulze, T.* C1 - 56754 C2 - 47284 CY - Radarweg 29, 1043 Nx Amsterdam, Netherlands SP - 1161-1161 TI - Polygenic risk score analysis of trajectories of cognitive performance in psychiatric patients. JO - Eur. Neuropsychopharmacol. VL - 29 PB - Elsevier PY - 2019 SN - 0924-977X ER - TY - JOUR AU - Schulte, E.* AU - Kondofersky, I. AU - Budde, M.* AU - Adorjan, K.* AU - Aldinger, F.* AU - Anderson-Schmidt, H.* AU - Gade, K.* AU - Heilbronner, U.* AU - Kalman, J.* AU - Papiol, S.* AU - Theis, F.J. AU - Falkai, P.* AU - Müller, N.S. AU - Schulze, T.G.* C1 - 56755 C2 - 47285 CY - Radarweg 29, 1043 Nx Amsterdam, Netherlands SP - 1257-1258 TI - Polygenic burden analysis of longitudinal clusters of psychopathological features in a cross-diagnostic group of individuals with severe mental illness. JO - Eur. Neuropsychopharmacol. VL - 29 PB - Elsevier PY - 2019 SN - 0924-977X ER - TY - JOUR AU - Arloth, J.* AU - Binder, E.* AU - Müller, N.S. C1 - 52347 C2 - 43905 CY - Amsterdam SP - S163-S163 TI - An integrative disease-relevant multi-omics analysis to predict risk for stress-related psychiatric disorders. JO - Eur. Neuropsychopharmacol. VL - 27 PB - Elsevier Science Bv PY - 2017 SN - 0924-977X ER - TY - JOUR AB - Background Bipolar disorder (BD), schizoaffective disorder (SZA) and schizophrenia (SZ) are severe mental illnesses that share - at least in parts - psychopathological features and an underlying polygenic nature. One characteristic of all three diagnoses is the highly variable disease course and outcome. This heterogeneity is one of the biggest challenges in studying the underlying biological mechanisms. Therefore, defining more homogeneous subgroups across diagnoses is a promising approach. However, there are no clear criteria as how to define a “good” or “poor” course of illness as different domains can be considered such as psychopathology, cognitive performance, psychosocial functioning, or quality of life. We aim to integrate these domains and define longitudinal clusters of patients across diagnoses. Furthermore, we explore the characteristics of these clusters and the association of cluster membership with the individual load on schizophrenia polygenic risk scores (SZ-PRS). Methods Participants were selected from an ongoing longitudinal project carried out at several centers in Germany and Austria (www.kfo241.de; www.PsyCourse.de). We characterize patients at four time-points over an 18-month period with a comprehensive phenotyping battery. The selected sample comprised a total of 198 participants (age(SD)=46.93(12.43); 46% females) with a DSM-IV diagnosis of SZ, SZA or BD, who completed the entire study period. DNA samples were genotyped using the Illumina PsychChip and imputed using the 1000 Genomes Phase 3 reference panel. SZ-PRS were calculated for all individuals based on the PGC2 SZ summary results. Factor analysis for mixed data (FAMD) was applied to compute abstract data dimensions in a set of 117 longitudinally measured variables, i.a. on psychopathology, cognitive performance, functioning and quality of life. Longitudinal trajectories of patients on the first dimension were used as inputs for k-mean clustering for longitudinal data. This, in turn, resulted in the identification of three distinct clusters of patients, which we used as predictive variables for SZ-PRS at 11 p-value thresholds in a linear regression model. Results Strongest loadings on the first dimension computed by FAMD were observed for quality of life items, a global depression rating and level of functioning. Three clusters of longitudinal trajectories were identified on this dimension: A) patients who scored highly on the dimension across all time points (58.1%); B) patients with consistently low scores (26.3%); C) patients who improved from baseline to the last follow up (15.7%). There were no significant between-group differences regarding sex, age, diagnoses, center, age at onset, and duration of illness. Cluster membership was significantly associated with the SZ-PRS with highest polygenic burden in cluster B. Discussion Although the reported results are preliminary and therefore have to be interpreted with caution, the approach of longitudinal clustering in order to identify cross-diagnostic, homogeneous subgroups of patients for genetic studies is promising. The next steps will be refinement of clusters by taking more than one dimension from the FAMD into account, verification of cluster solutions in an external dataset, and exploration of associations with other biological markers.   AU - Budde, M.* AU - Kondofersky, I. AU - Adorjan, K.* AU - Aldinger, F.* AU - Anderson-Schmidt, H.* AU - Andlauer, T.F.* AU - Flatau, L.* AU - Gade, K.* AU - Heilbronner, U.* AU - Kalman, J.* AU - Papiol, S.* AU - Theis, F.J. AU - Falkai, P.* AU - Müller, N.S. AU - Schulze, T.G.* C1 - 51819 C2 - 43363 CY - Amsterdam SP - S406 TI - Integrating polygenic allele burden information and phenomic data to characterize complex disease trajectories in severe mental illness. JO - Eur. Neuropsychopharmacol. VL - 27 PB - Elsevier Science Bv PY - 2017 SN - 0924-977X ER - TY - JOUR AB - Background Psychiatric illnesses such as bipolar disorder, schizophrenia and schizoaffective disorder are severe, disabling disorders associated with decreased quality of life (QOL) and functioning (Bobes, Garcia-Portilla, Bascaran, Saiz, & Bousoño, 2007; Latalova, Prasko, Diveky, Kamaradova, & Velartova, 2010; Merikangas et al., 2012). Stigmatization, co-morbidities, adverse effects of medications, care models with deficits in personal and social recovery needs and chronic symptoms due to treatment resistance are factors that can lead to severe reductions in quality of life and functioning (Kahn et al., 2015; Sum, Ho, & Sim, 2015). In this study we aim to characterize patients with good and poor outcomes according to QOL and functioning scores. Using cluster analysis, we sought to identify longitudinal trajectories and investigate whether levels of QOL and functioning are associated with polygenic risk scores. Determining clusters of patients at higher risk of poorer outcomes is critical to provide early and effective interventions. Methods Longitudinal data was used from the Clinical Research Group 241 and PsyCourse studies in Germany. Participants were phenotyped using a comprehensive battery which included data on socio-demographics, history of illness, symptomatology, QOL and functioning. Data was collected at four equidistant time points over an 18-month period. The Infinium Psycharray from Illumina was used to genotype patients. Relevant questionnaire items (i.e. QOL, functioning scores, and socio-demographic data) were pre-selected and factor analysis for mixed data was applied to identify trends in the data. This allowed for the computation of abstract data dimensions which were used for calculation of longitudinal trajectories. These trajectories can be seen as a representation of the overall status of patients and both the overall level as well as the longitudinal change of this status were used as inputs for a k-mean clustering for longitudinal data (Genolini et al., 2013). This, in turn, resulted in the identification of three distinct subpopulations of patients. In a linear regression model we used clusters as predictive variables for polygenic risk scores at 11 thresholds. Results The dimension which explained the most variance was used for cluster analysis. This dimension was mainly driven by scores for self-satisfaction, life enjoyment, ability to cope with daily tasks, energy, and quality of life. In a sample of 198 patients, three clusters were observed; cluster A (39,4%) consisted of participants with the highest average scores for functioning and QOL, cluster B (33,8%) including participants with the lowest average scores for functioning and QOL, and cluster C (26,8%) consisting of participants who had great improvement in functioning and QOL scores over the course of the longitudinal study. Male patients were substantially overrepresented in cluster A and the inverse effect was observed in cluster B. No significant differences were seen for age of onset, age at interview, or duration of illness within the clusters. Polygenic risk scores at certain thresholds can be predicted by the clusters. In cluster B there was a trend for higher polygenic risk scores. Discussion Phenotypic data provide insight to target sufferers of severe mental illness with worse outcomes. Levels of functioning and QOL seem to be associated with polygenic risk scores. Further investigations are needed.   AU - Comes, A.* AU - Aldinger, F.* AU - Kondofersky, I. AU - Adorjan, K.* AU - Anderson-Schmidt, H.* AU - Andlauer, T.F.* AU - Budde, M.* AU - Gade, K.* AU - Heilbronner, U.* AU - Kalman, J.* AU - Papiol, S.* AU - Theis, F.J. AU - Falkai, P.* AU - Müller, N.S. AU - Schulze, T.G.* C1 - 51821 C2 - 43365 CY - Amsterdam SP - S408-S409 TI - Polygenic burden analysis of longitudinal clusters of quality of life and functioning in patients with severe mental illness. JO - Eur. Neuropsychopharmacol. VL - 27 PB - Elsevier Science Bv PY - 2017 SN - 0924-977X ER - TY - JOUR AB - Background Illnesses from the schizophrenia-to-bipolar spectrum have a highly variable course. Determinants of these different individual trajectories have been of particular interest to scholars during the past century. Beyond rudimentary understanding, however, different course types have been difficult to delineate in categorical disease phenotypes. We have therefore embarked upon a project in which we seek to delineate different course types in a large longitudinal sample of deeply phenotyped patients suffering from disorders of the schizophrenia-to-bipolar continuum. With respect to biology, a dysregulation of microRNAs, small non-coding RNA molecules that flexibly influence transcription, in mental disorders is increasingly recognized. To combine both of these novel approaches, we plan investigate the role of microRNAs in different course types identified using longitudinal cluster analysis. Methods Longitudinal clustering Participants were selected from an ongoing longitudinal, multi-site study (www.kfo241.de, www.PsyCourse.de). Patients with a DSM-IV diagnosis of the schizophrenia-to-bipolar spectrum were comprehensively phenotyped at four time-points over a period of 18 months. A set of longitudinally measured variables on current psychopathology, medication adherence, substance use, cognitive performance, level of psychosocial functioning and various questionnaires was analyzed using factor analysis for mixed data followed by longitudinal cluster analyses. This resulted in the identification of distinct subpopulations of patients, each being heterogeneous in terms of diagnostic composition. MicroRNA sequencing So far, we have compared four different methods to isolate blood borne small non-coding RNAs for RNA-sequencing. By this we were able to establish SOPs for the reliable analysis of circulating small non-coding RNAs in longitudinal cohorts. Results We will present results of our research project at the meeting. Discussion We will discuss our research project at the meeting.   AU - Heilbronner, U.* AU - Jain, G.* AU - Kaurani, L.* AU - Kondofersky, I. AU - Budde, M.* AU - Gade, K.* AU - Kalman, J.* AU - Adorjan, K.* AU - Aldinger, F.* AU - Anderson-Schmidt, H.* AU - Müller, N.S. AU - Theis, F.J. AU - Falkai, P.* AU - Fischer, A.* AU - Schulze, T.G.* C1 - 51820 C2 - 43364 CY - Amsterdam SP - S456-S457 TI - The Role of micrornas in the course of servere mental disorders. JO - Eur. Neuropsychopharmacol. VL - 27 PB - Elsevier Science Bv PY - 2017 SN - 0924-977X ER - TY - JOUR AB - Background Bipolar disorder (BD), schizophrenia (SZ) and schizoaffective disorder (SZA) can be disabling disorders associated with severe psychiatric symptomatology. Individual psychopathological features often overlap between these diagnostic groups and their severity can vary widely. More severe psychopathological features are generally associated with a less favorable outcome. Further, all three diseases are common complex genetic disorders with a polygenic genetic architecture in the majority of cases. The inherent heterogeneity with regard to disease severity has posed a significant challenge to both the study of the underlying disease mechanism and the clinical management. Therefore, stratification of cases into more homogeneous subgroups across diagnoses using both longitudinal clusters derived from psychometric data and genetic information could provide a means to identify individuals with higher risk for severe illness, mandating earlier and intensified clinical intervention. Methods Individuals included herein partake in an ongoing multisite cohort study across Germany and Austria (www.kfo241.de; www.PsyCourse.de). Participants were characterized at 4 time points over an 18-months period using a comprehensive phenotyping battery. The subsample used here totals 198 participants (46.9±12.4 yrs; 46% female) with DSM-IV diagnoses of SZ, SZA or BD. Blood DNA samples were genotyped using Illumina’s Infinium PsychArray and imputed using the 1000 genomes. SZ-PRS were calculated using PLINK 1.07. Effect sizes and p-values were determined with the PGC2 SZ summary results as discovery sample. A set of 67 longitudinally measured variables derived from the Positive and Negative Syndrome Scale (PANSS), the Inventory of Depressive Symptoms (IDS) and the Young Mania Rating Scale (YMRS) entered the cluster analyses. Factor analysis for mixed data (FAMD) was applied to compute abstract data dimensions, subsequently used to derive the longitudinal trajectories which then served as inputs for a k-mean clustering for longitudinal data. Identified clusters were employed in a linear regression model as predictive variables for SZ-PRS at 11 thresholds. Results Computed by FAMD, the strongest loadings were observed for PANSS and IDS on the first dimension and for IDS on the second dimension. Two clusters of longitudinal trajectories were identified in these dimensions: (A) individuals with continuously low scores on both PANSS and IDS (70.7%) and (B) individuals with consistently high scores on both PANSS and IDS (29.3%). Clusters differed significantly with regard to Global Assessment of Functioning (GAF; higher in (A); FDR-adjusted p-value=2.23x10-10), while there were no significant differences regarding sex, age, diagnoses, center, age at onset, family history or duration of illness. Cluster membership was not significantly associated with the SZ-PRS in either cluster. Discussion Although the results are preliminary and have to be interpreted with caution, the approach of longitudinal clustering to identify cross-diagnostic homogeneous subgroups of individuals appears to be feasible. The fact that more severe psychopathological features were not associated with increased genetic risk burden will also be interesting to explore further.   AU - Schulte, E.* AU - Kondofersky, I. AU - Budde, M.* AU - Adorjan, K.* AU - Aldinger, F.* AU - Anderson-Schmidt, H.* AU - Andlauer, T.F.* AU - Gade, K.* AU - Heilbronner, U.* AU - Kalman, J.* AU - Papiol, S.* AU - Theis, F.J. AU - Falkai, P.* AU - Müller, N.S. AU - Schulze, T.G.* C1 - 51822 C2 - 43366 CY - Amsterdam SP - S401-S402 TI - Polygenic burden analysis of longitudinal clusters of psychopathological features in a cross-diagnostic group of individuals with severe mental illness. JO - Eur. Neuropsychopharmacol. VL - 27 PB - Elsevier Science Bv PY - 2017 SN - 0924-977X ER - TY - JOUR AU - Bender, J.* AU - Ederer, M.S.* AU - Breu, J.* AU - Engeholm, M.* AU - Michalakis, S.* AU - Wurst, W. AU - Deussing, J.M.* C1 - 46709 C2 - 37745 CY - Amsterdam SP - S8-S9 TI - Clustering of corticotropin-releasing hormone receptor type 1 with membrane-associated guanylate kinases. JO - Eur. Neuropsychopharmacol. VL - 25 PB - Elsevier Science Bv PY - 2015 SN - 0924-977X ER -