2019-09-30

022_ Investigation of the genetic correlates of chronic disease course in the PsyCourse sample

Research Question and Aims

Mental health disorders are associated with impaired functioning, quality of life and are among the leading causes of disability. Although these conditions are often described as chronic and recurrent, there is a great individual variability in their course and prognosis. Stratifying patients based on their most likely disease course is necessary for the development of individualized and need-driven treatment strategies and for improving clinical care. However, in the absence of reliable and established prognostic markers, treatment is currently organized following standardized guidelines.
Evidence suggests that the genetic risk load for psychiatric disorders and the accumulation of familial cases might be greater in chronic and recurrent forms of major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BD). For example, a significant correlation with chronicity and clinical outcome has been observed in siblings and monozygotic (Allan et al., 2009; Meier et al., 2016). However, associations between chronicity and episodicity (i.e., the number of illness episodes) and the cumulative genetic risk load (genetic risk score (GRS)) for major mental health conditions have so far only been investigated in few studies. Using the iPSYCH cohort, Meier et al. found that SCZ GRS increase with the number of hospitalizations (Meier et al., 2016). Furthermore, Wray et al. (2018) reported that patients with recurrent or more severe MDD (endorsed MDD criteria) have higher MDD GRS than patients with a single episode of MDD (Wray et al., 2018). These studies provide evidence for the role of genetic factors in chronicity and episodicity and call for further, both diagnosis-specific and cross-disorder studies.
On the short-term, we, therefore, plan to select markers of chronicity, such as the number of hospitalizations, socioeconomic status, occupational status, and the number of missed workdays during the follow-up period from the PsyCourse data to investigate whether these measures are correlated with the GRS for mental health disorders (e.g., SCZ, MDD, and BD). We plan to conduct both cross-disorder and disorder-specific analyses.
On the long-term, we aim to use other samples (in-house and, e.g., FOR2107) for extension and validation of our findings. Moreover, we plan to submit analysis proposals to international consortia (e.g., the PGC) and research groups with access to patient cohorts with rich phenotypic information, such as medical records and the prescribing information system (e.g., iPSYCH, Partners Healthcare, Vanderbilt Biobank) to replicate these analyses using "real-life" data.

Analytic Plan

Short-term:
Application of regression models using "chronicity markers" as dependent and GRS as independent variables, while controlling for sex, age, duration of illness, diagnosis, and ancestry components. Our hypothesis is that GRS are positively associated with chronicity. Results from the PsyCourse cohort will be validated in additional samples.

Long-term ;
(PsyCourse/FOR2107): Clustering algorithms and GWAS. GWAS will require the inclusion of further cohorts, e.g. from the PGC.

Resources needed

Phenotypes:
Information on in- and outpatient treatment (continuous, ordinal)
the number of illness episodes (depression, mania, hypomania)
the number of missed workdays during follow-up
SES markers (e.g., work and family status)

Genotypes:
First to calculate GRS and ancestry components; on the long-term for GWAS
The validation of findings and the long-term aims require the inclusion of further cohorts, which have, so far, not been contacted regarding the present proposal.