Berlin Research Initiative for Diagnostics, Genetics and Environmental Factors in Schizophrenia (BRIDGE-S)

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Over the past decade tremendous progress has been made in unraveling the genetic architecture of schizophrenia (SCZ) driven by modern high-throughput techniques and large-scale collaboration within the field of psychiatric genetics. Likewise, epidemiological research has yielded a range of environmental factors that are associated with SCZ risk. Yet the relationship between multiple acquired risk factors and genetic vulnerability remains largely elusive. Adding to the complexity, accumulating evidence suggests that certain protective factors can promote resilience towards adverse mental health outcomes, particularly in “high-risk” individuals. Studies examining more than a few genetic or environmental determinants are sparse, mostly due to a lack of suitable data sources as SCZ patients are widely underrepresented in biobanks and population-based cohorts. Here, we aim to assemble a novel cohort of at least 1,300 SCZ patients and 1,300 unaffected controls following an established multi-stage recruitment and modular phenotyping strategy. Data collection is comprised of genetic information (genome-wide SNP data), a comprehensive cognitive and clinical evaluation, a questionnaire capturing health data and robustly implicated environmental exposures that confer risk and resilience to SCZ.The specific research objectives encompass: i) contributions to the discovery of common and rare genetic variation underlying SCZ ii) exploring gene-environment interactions using univariate and poly-risk prediction models iii) developing an integrative data analysis framework that combines risk and protective factors via generalized regression, Structural Equation Modelling and Mendelian Randomization iv) application of a recently developed pathway-based polygenic risk scoring technique to infer putative G-E constellations underlying etiologically distinct subgroups. As such, a joint analysis of genetic, epidemiological and clinical data in a representative sample has the potential to offer valuable insights relevant to risk-prediction and early intervention strategies.