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A longitudinal approach to biological psychiatric research: The PsyCourse study.

ABSTRACT: In current diagnostic systems, schizophrenia and bipolar disorder are still conceptualized as distinct categorical entities. Recently, both clinical and genomic evidence have challenged this Kraepelinian dichotomy. There are only few longitudinal studies addressing potential overlaps between these conditions. Here, we present design and first results of the PsyCourse study (N = 891 individuals at baseline), an ongoing transdiagnostic study of the affective-to-psychotic continuum that combines longitudinal deep phenotyping and dimensional assessment of psychopathology with an extensive collection of biomaterial. To provide an initial characterization of the PsyCourse study sample, we compare two broad diagnostic groups defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) classification system, that is, predominantly affective (n = 367 individuals) versus predominantly psychotic disorders (n = 524 individuals). Depressive, manic, and psychotic symptoms as well as global functioning over time were contrasted using linear mixed models. Furthermore, we explored the effects of polygenic risk scores for schizophrenia on diagnostic group membership and addressed their effects on nonparticipation in follow-up visits. While phenotypic results confirmed expected differences in current psychotic symptoms and global functioning, both manic and depressive symptoms did not vary between both groups after correction for multiple testing. Polygenic risk scores for schizophrenia significantly explained part of the variability of diagnostic group. The PsyCourse study presents a unique resource to research the complex relationships of psychopathology and biology in severe mental disorders not confined to traditional diagnostic boundaries and is open for collaborations.

SUBMITTER: Budde M

PROVIDER: S-EPMC6585634 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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A longitudinal approach to biological psychiatric research: The PsyCourse study.

Budde Monika M Anderson-Schmidt Heike H Gade Katrin K Reich-Erkelenz Daniela D Adorjan Kristina K Kalman Janos L JL Senner Fanny F Papiol Sergi S Andlauer Till F M TFM Comes Ashley L AL Schulte Eva C EC Klöhn-Saghatolislam Farah F Gryaznova Anna A Hake Maria M Bartholdi Kim K Flatau Laura L Reitt Markus M Quast Silke S Stegmaier Sophia S Meyers Milena M Emons Barbara B Haußleiter Ida Sybille IS Juckel Georg G Nieratschker Vanessa V Dannlowski Udo U Schaupp Sabrina K SK Schmauß Max M Zimmermann Jörg J Reimer Jens J Schulz Sybille S Wiltfang Jens J Reininghaus Eva E Anghelescu Ion-George IG Arolt Volker V Baune Bernhard T BT Konrad Carsten C Thiel Andreas A Fallgatter Andreas J AJ Figge Christian C von Hagen Martin M Koller Manfred M Lang Fabian U FU Wigand Moritz E ME Becker Thomas T Jäger Markus M Dietrich Detlef E DE Stierl Sebastian S Scherk Harald H Spitzer Carsten C Folkerts Here H Witt Stephanie H SH Degenhardt Franziska F Forstner Andreas J AJ Rietschel Marcella M Nöthen Markus M MM Falkai Peter P Schulze Thomas G TG Heilbronner Urs U

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics 20180802 2

In current diagnostic systems, schizophrenia and bipolar disorder are still conceptualized as distinct categorical entities. Recently, both clinical and genomic evidence have challenged this Kraepelinian dichotomy. There are only few longitudinal studies addressing potential overlaps between these conditions. Here, we present design and first results of the PsyCourse study (N = 891 individuals at baseline), an ongoing transdiagnostic study of the affective-to-psychotic continuum that combines lo ...[more]

PMID: 30070057

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Project description:A popular approach to reduce the high dimensionality resulting from genome-wide association studies is to analyze a whole pathway in a single test for association with a phenotype. Kernel machine regression (KMR) is a highly flexible pathway analysis approach. Initially, KMR was developed to analyze a simple phenotype with just one measurement per individual. Recently, however, the investigation into the influence of genomic factors in the development of disease-related phenotypes across time (trajectories) has gained in importance. Thus, novel statistical approaches for KMR analyzing longitudinal data, i.e. several measurements at specific time points per individual are required. For longitudinal pathway analysis, we extend KMR to long-KMR using the estimation equivalence of KMR and linear mixed models. We include additional random effects to correct for the dependence structure. Moreover, within long-KMR we created a topology-based pathway analysis by combining this approach with a kernel including network information of the pathway. Most importantly, long-KMR not only allows for the investigation of the main genetic effect adjusting for time dependencies within an individual, but it also allows to test for the association of the pathway with the longitudinal course of the phenotype in the form of testing the genetic time-interaction effect. The approach is implemented as an R package, kalpra. Our simulation study demonstrates that the power of long-KMR exceeded that of another KMR method previously developed to analyze longitudinal data, while maintaining (slightly conservatively) the type I error. The network kernel improved the performance of long-KMR compared to the linear kernel. Considering different pathway densities, the power of the network kernel decreased with increasing pathway density. We applied long-KMR to cognitive data on executive function (Trail Making Test, part B) from the PsyCourse Study and 17 candidate pathways selected from Reactome. We identified seven nominally significant pathways.

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A longitudinal approach to biological psychiatric research: The PsyCourse study.

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A longitudinal approach to biological psychiatric research: The PsyCourse study.

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