Skip to main content

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



Large-scale collaborative efforts in the field of psychiatric genetics have made substantial progress in unraveling the biological architecture of schizophrenia (SCZ). Although both genetic and environmental factors are known to play a role in schizophrenia etiology our mechanistic understanding of how they shape risk, resilience and disease trajectories remains limited.


Here, we present the study protocol of the Berlin Research Initiative for Diagnostics, Genetic and Environmental Factors of Schizophrenia (BRIDGE-S), which aims to collect a densely phenotyped genetic cohort of 1,000 schizophrenia cases and 1,000 controls. The study’s main objectives are to build a resource for i) promoting genetic discoveries and ii) genotype–phenotype associations to infer specific disease subtypes, and iii) exploring gene-environment interactions using polyrisk models. All subjects provide a biological sample for genotyping and complete a core questionnaire capturing a variety of environmental exposures, demographic, psychological and health data. Approximately 50% of individuals in the sample will further undergo a comprehensive clinical and neurocognitive assessment.


With BRIDGE-S we created a valuable database to study genomic and environmental contributions to schizophrenia risk, onset, and outcomes. Results of the BRIDGE-S study could yield insights into the etiological mechanisms of schizophrenia that could ultimately inform risk prediction, and early intervention and treatment strategies.

Peer Review reports


Schizophrenia is a clinically heterogeneous psychiatric disorder with a substantial underlying genetic component. Genome-wide association studies identified over 200 common variants, each conferring a small risk [1] and few but high-impact rare variants [2, 3] reflecting a complex molecular architecture and a high degree of polygenicity. The fraction of variance in disease risk attributable to common genetic variation, known as SNP-based heritability, is estimated at 24%, well below the benchmark of 80% derived from a collection of twin and family studies [4, 5]. This discrepancy might partially be explained by the insufficient statistical power to detect all genetic signals and the inflation of twin-based heritability measures that do not account for shared environment within families [6]. Indeed, epidemiological findings further point to multiple environmental exposures with moderate effect sizes that affect schizophrenia onset. These include minority status, urban upbringing, cannabis use, perinatal complications, and childhood adversity, which likely depend on an individual’s genetic vulnerability [7,8,9].

Despite progress in establishing environmental and genetic factors, our understanding of their individual and combined effects on the disease risk, onset, and outcome is still limited [10]. Theoretical considerations on etiological models postulating an interaction of biological predisposition and external stressors that increase susceptibility to schizophrenia can be traced back to the 1950s [11]. Empirical investigations of gene-environment interactions have proven to be challenging primarily due to a lack of data sources, poor reproducibility of earlier candidate gene studies, and the use of proxy measures like family history [12, 13].

As the focus of research has shifted from single-gene-environmental analyses to polygenic models [14], new approaches and methods are increasingly incorporated, such as Polygenic Risk Scores (PRS) that aggregate the weighted effects of many genetic variants to obtain an overall measure of propensity towards a given trait. A landmark study published in 2019 by EU-GEI investigators provides direct evidence for the additive effects of gene-environment interaction for SCZ-PRS with cannabis use and exposure to childhood adversities [15]. Efforts to substantiate an interplay between polygenic risk and obstetric complications have thus yielded inconsistent results [16, 17]. Similar to PRS, the concept of composite scores of environmental exposures evolved as a tool to capture the cumulative effects of the exposome [18, 19]. Recent findings indicate a higher burden of environmental exposures in first episode patients [20] and individuals with schizophrenia [21]. This might hold particularly true for affected individuals with an earlier onset [22]. Moreover, there is evidence that the effects of environmental exposures and genetic liability on the outcome are not independent [20, 23]. Adding further to the complexity, internal and external protective factors like social support mitigate the influence of genetic vulnerability and adversities to promote positive mental health outcomes and recovery [24]. Determinants contributing to resilience could potentially elucidate differences in disease courses and severity; however, the biological mechanisms underlying such associations remain largely elusive.

Although international consortia like the Psychiatric Genomics Consortium (PGC) have collated many genetic samples, they often lack dense, homogeneous phenotype data needed to assess the functional impact of genetic variation alone and in conjunction with environmental factors. To our knowledge, only a few studies exist that allow for such investigations [25,26,27], and further research and replication of previous findings are urgently warranted. With the introduction of the BRIDGE-S, we attempt to assemble a large, well-characterized cohort of affected and unaffected individuals to study relationships and interactions between genetics, environment, and phenotypic variance. The purpose of this protocol is to outline scientific objectives and study procedures which will also serve as a foundation for future collaboration.


Aim and objectives

Building on prior research, the primary goal of BRIDGE-S is to recruit a large sample of schizophrenia patients (N = 1,000) and unaffected individuals (N = 1,000) with comprehensive phenotypic and environmental information alongside genomic data. We aim to build a resource to i) facilitate genetic discoveries ii) study genotype–phenotype relationships within schizophrenia as well as unaffected individuals iii) explore joint and independent effects of environmental and genetic factors that confer risk and resilience on schizophrenia onset and outcomes iv) enable prospective recall studies informed by genotypic and environmental constellations.

Study design

The BRIDGE-S is an ongoing case–control study with a strong focus on accessibility and feasibility. Thus, we established a modular multistage data collection strategy encompassing core and optional modules (see section Modular Phenotyping), while participants may choose between in-house assessment and remote participation (see section In-house assessment and remote participation). Procedures for cases and controls are very closely aligned, as illustrated in the BRIDGE-S’s study design and workflow in Fig. 1.

Fig. 1
figure 1

Study design and workflow. Abbreviations: SCZ—Schizophrenia, SZA—Schizoaffective Disorder, BD—Bipolar Disorder

During a pilot phase conducted between July 2018 and December 2019, participants were enrolled at two sites to test the feasibility of recruitment, evaluate intended data collection procedures in different settings, and identify solutions to potential issues. We established Standard Operating Procedures (SOPs) to ensure high comparability of collected data across and within study sites.

Inclusion criteria

All participants must a) be at least 18 years old, b) have sufficient German language skills required to understand the scope of the study and to complete the questionnaires, and c) provide written informed consent. Individuals are allocated to the case sample if they ever met diagnostic criteria for Schizophrenia (ICD-10: F20) or Schizoaffective Disorder (ICD-10: F25) at any point during their life. Formal diagnosis is ascertained upon referral by clinicians via access to medical records or hospital discharge letters. Control subjects are eligible to participate if they have never been diagnosed with Schizophrenia, Schizoaffective-, or Bipolar Disorder (ICD-10: F31); the latter due to its high genetic correlation with Schizophrenia [28]. Non-European ancestry is not an exclusion criterion to promote the recruitment of individuals from all populations including those underrepresented in genetic studies.


Case sample

Study proceedings, especially recruitment strategies, were developed to engage patients from different social backgrounds and with various outcomes. Research team members recruit patients from three core study sites at Charité Universitätsmedizin—Campus Mitte, Charité Universitätsmedizin—Campus Benjamin Franklin, and the Alexianer St. Hedwig Hospital in Berlin, Germany. Several other outpatient clinics and medical practices in the Berlin metropolitan area refer patients by handing out study material. Schizophrenia cases from the broader population are recruited via online advertising on search engines and our institution’s website. Additionally, we launched an advertising campaign in public transportation in November 2021. Patients who participated in an earlier study (the “Berlin Psychosis Study”—BePS) and consented to be contacted for future studies were invited to participate in the present study. These patients were originally recruited through a network of collaborating hospitals in Berlin. BePS focused on the genetic underpinnings of Schizophrenia, most patients already provided a saliva sample for genetic analyses (see section Genetic data & genotyping).

Control sample

Healthy controls are recruited from local universities and the broad population via media outlets, including television, newspapers, and radio broadcasts, along with ads on social media platforms and search engines.

Data collection

Registration, informed consent and contact data

Participants are either recruited directly via the clinic or sign up for the study via E-Mail, telephone, or through the registration form on the study website. Upon inclusion, all subjects are asked whether they permit to be contacted for follow-up studies. If participants agree to be informed about future opportunities to take part in research studies, contact information is recorded and stored separately from any biological and health data. Identifying data is processed in compliance with existing data protection laws and access to personal data is restricted to designated staff members.

Modular phenotyping

As part of the core modules, all participants provide a biosample for genetic analyses and complete a comprehensive questionnaire. It takes between 45 and 75 min to complete the mandatory core modules. The questionnaire was carefully composed to assess a range of self-report measures required for large-scale investigations while keeping the overall length short. This approach also enables patients with a higher disease burden to engage in the study.

The deep phenotyping module was designed to facilitate secondary analyses on genetic contributions to specific symptom dimensions and clusters. Subjects are assessed regarding symptom severity, cognitive- and overall level of functioning. This module includes a clinical interview for patients, additional questionnaires, and a neurocognitive battery for both cases and control subjects. The optional deep phenotyping modules take ~ 100 min for control subjects and 150–180 min for cases to complete. Based on previous experience we anticipate that ~ 50% of enrolled subjects will complete the deep phenotyping modules.

Digital phenotyping & database

Phenotypic data is collected and managed using Research Electronic Data Capture [29, 30] hosted at servers of Charité – Universitätsmedizin Berlin. REDCap is a secure, self-hosted, and web-based software platform developed to support data capture for research studies and clinical trials. In most instances, subjects directly enter questionnaire data into REDCap using the online survey mode. Alternatively, data from printed questionnaires are entered manually into the REDCap database, each data entry is then carefully double-checked by another research team member. If available, interview-based ratings are recorded in corresponding REDCap data entry forms. Real-time data validation and quality rules were defined to ensure that data is entered accurately and as completely as possible.

In-house assessment and remote participation

Core modules may be completed from home to lower the barrier to participation for patients that would otherwise not opt for or be able to join an on-site assessment. After registration and contact with the research team, detailed study information, a consent form, and a DNA saliva kit are sent via mail. Patients may choose to fill out the questionnaire as a paper and pencil version from home or as an online survey, in which the eCRF can be accessed through a user-friendly interface. Additional instructions are displayed on the questionnaire landing page and the cover letter that is forwarded together with the saliva kit and consent form. The research team offers support and assists during at-home participation whenever needed.

Participation in the deep phenotyping modules requires an in-house assessment at one of the study sites. For that purpose, participants are reimbursed for any travel expenses in addition to the monetary compensation they receive for their study participation. Participants are eligible to take part in the optional phenotyping modules once the core modules are completed.


The core questionnaire is composed of two parts. The first part may be conducted as an interview and contains questions about socio-demographics, complications during pregnancy and childbirth, parental age at birth, birth month, migration, urbanicity, drug use, basic clinical data, physical health, including traumatic brain injury, as well as family history of certain medical conditions. Basic clinical data that is collected from patients and by chart review contains hospitalizations, age of onset, duration of illness, principal diagnosis, and psychiatric comorbidity, current and past treatments, particularly medication, treatment with clozapine, and electroconvulsive therapy. The second questionnaire part encompasses a selection of self-report instruments. These capture traumatic or adverse events during childhood [31] and across the lifespan [32], resilience [33, 34], social support [35], suicidality [36] subjective well-being [37] and current anxiety and depressive symptoms [38]. Control subjects complete a series of additional questionnaires assessing psychotic-like experiences [39, 40] and schizotypal personality traits in the general population [41] as well as previous or current manic episodes [42].

Besides their appropriateness to assess previously reported risk- and resilience factors and important outcomes in schizophrenia, instruments for phenotyping were selected based on the following aspects: i) length and duration of administration ii) validity and reliability in German-speaking samples iii) applicability across cultures and countries iv) validity and appropriateness for both clinical and population-based samples. A complete list of all instruments administered to the case and control study arms is presented in Table 1.

Table 1 Overview of collected data and phenotyping instruments

Genetic data & genotyping

All study subjects provide a biological sample for genome-wide genotyping. Most subjects donate a 1.0 ml saliva sample using OraGene-510 or OraGene-610 DNA-Self-Collection Kits (Genotek, Ottawa, Ontario, Canada), which is an easy, safe, and user-friendly collection system that can also be used for self-administration at home. Alternatively, a blood sample (EDTA whole-blood, commercially available brands) is collected during routine care laboratory blood sampling. Saliva and blood samples are stored on-site until further processing.

DNA is extracted from saliva and blood samples following established standard protocols in Berlin, Germany. DNA stock solutions are transferred to the central Charité biobank (ZeBanC) for long-term storage at -60 degrees. Normalized DNA aliquots are sent to the ERASMUS Medical Center’s Human Genotyping Facility (HuGe-F) in the Netherlands for genotyping. All samples are assayed on the Illumina Infinium Global Screening Array (GSA) MD BeadChip (Illumina, San Diego, CA), which covers > 650,000 genetic variants. Genome-wide SNP data is processed on an High Performance Computing cluster.

Neuropsychological assessment

Participants undergo a neurocognitive assessment to measure performance in different domains. The battery encompasses tests measuring memory capacity, executive functioning, decision making, social cognition, attention, and psychomotor speed. To assure consistency and standardization across multiple sites, we adopted a fully computerized neurocognitive battery using the Cambridge Neuropsychological Test Automated Battery (CANTAB; [43]) system.

The CANTAB schizophrenia battery is composed of eight tasks covering key domains recommended by the MATRICS initiative (Marder, 2006): Reaction Time (RTI), Rapid Visual Information Processing (RVP), Paired Associates Learning (PAL), Verbal Recognition Memory (VRM), Spatial Working Memory (SWM), Multitasking Test (MTT), One Touch Stockings of Cambridge (OTS), and Emotion Recognition Task (ERT). The battery was preceded by a short Motor Screening Task (MOT) to familiarize participants with the setting & usage and screen for potential motor and comprehension issues. An overview of the test battery and domains assessed is shown in Table 1. Respective test versions and sequences applied in the current study can be found in Supplementary Table 1.

Instructions are presented via voice-over in German for each test. Subjects interact with a touchscreen system on a 10.5–11 inch display (Apple iPad, iOS version 12.1 or later). The entire battery takes ~ 75 min to complete. Key outcome measures are automatically recorded and stored on secure servers hosted by Cambridge Cognition. In addition to CANTAB, the short version of the Edinburgh Handedness Inventory (EHI-SF) [44] is administered as part of the module.

Clinical assessment

Assessment of clinical symptoms differs between cases and controls (see Table 1). The Positive and Negative Syndrome Scale (PANSS) [45] is used as the primary measure to examine the severity of specific symptoms associated with schizophrenia during the past seven days. The scale is rated by trained clinical staff based on a semi-structured interview (SCI-PANSS) that typically takes between 45–90 min. Wherever possible, information from family members, caregivers, or hospital staff is gathered to rate items requiring a third-person perspective adequately. At least 10% of all PANSS interviews are rated independently by two members of the research team to ensure overall consistency between raters and calculate the inter-rater reliability. In addition to PANSS scores, the Clinical Global Impression (CGI-S) [46] and global assessment of functioning (GAF) [47] were used to measure global symptom burden and level of functioning, respectively. Finally, both cases and controls complete the WHO disability schedule (WHODAS 2.0) [48], and the Symptom Checklist 90 Revised (SCL-90-R) [49, 50].

Statistical analysis plan

Facilitating genetic discoveries (Aim 1)

Statistical analysis of genome-wide SNP data will be conducted using standard software like PLINK [51] and RICOPILI [52], a pipeline that allows for standardized and efficient common variant analyses at all steps: quality control (QC), relatedness testing & principal component analysis (PCA), genotype imputation, and association analysis. Stringent QC filters incorporated in RICOPILI will be applied to obtain high-quality genetic data for analyses. Imputed genotype data will be meta-analyzed with other samples aggregated by PGC investigators to i) identify genomic loci associated with schizophrenia risk ii) to dissect disorder-specific and shared SNP associations across multiple or pairs of psychiatric disorders and other relevant phenotypes in cross-trait approaches and iii) to uncover genetic variation underlying specific dimensional phenotypes within and beyond diagnostic categories, e.g., exploring symptom profiles, treatment outcomes. A range of post-GWAS analyses will be performed on summary-level data to increase interpretability, e.g., gene-set analysis and genetic correlations to quantify the molecular overlap between traits.

Genotype–phenotype analyses (Aim 2)

Complementing Aim 1, we will calculate PRS that index liability to various disorders and traits in our target sample, following a leave-one-out approach whenever appropriate to avoid overlap with discovery samples. Polygenic associations will be tested for multiple subjective and objective outcome measures. We will particularly focus on the level of functioning, cognitive markers, and clinical dimensions in patients. Complimentarily, we will investigate schizotypy and psychotic-like experiences like hallucinations and delusions and their relationship with genetic risk in the control sample.

Gene-environment analyses (Aim 3)

We further aim to explore genomic and environmental influences on schizophrenia risk, the occurrence of psychotic symptoms, and other secondary phenotypes by examining main and additive interaction effects between i) polygenic risk and specific exposures as well as environmental scores (ES) that combine individual effects of exposures ii) pathway-based PRS, that aggregate genetic risk across distinct biological pathways, ES and single exposures iii) single and multiple determinants that confer risk and resilience in an integrative model. Besides gene-by-environment interaction, we will also assess gene-environment correlations. Furthermore, we will incorporate state-of-the-art methods to compute ES [53] and to estimate the causal effects of environmental factors by leveraging genetic risk variants, for instance, via Mendelian Randomization [54].

Sample size and power calculation

Sample size for the main effects was calculated according to Cohen [55]. We expect small to modest effect sizes in analyses with dichotomous and continuous outcomes and PRS and ES environmental exposures as predictors. By including N = 2,000 (1,000 cases and 1,000 controls) into the core module, we will be able to detect potentially very small main effects (f2 ≥ 0.005, OR ≥ 1.29 at p ≤ 0.05) with sufficient power of ≥ 80% as visualized in Supplementary Figure S1. Further, the planned sample size is sufficient to test case-only (N = 1000) hypotheses and conduct analyses limited to the deep phenotyping module (N = 500/500) while still detecting small effects of (f2 ≥ 0.01, OR ≥ 1.44, p ≤ 0.05) with a power of ≥ 80%. According to VanderWeele’s method [56], the proposed sample size is suitable to detect a positive additive interaction in the case–control sample, assuming a rare outcome (main effects OR = 1.3; IOR = 1.5; p ≤ 0.05; power = 80%).


Current evidence highlights the complex and multifactorial etiology of schizophrenia [13]. In addition to expanding our genetic knowledge of schizophrenia and related disorders, our study may provide valuable insights into molecular pathways that underlie variability in psychopathology, disease course, and other important outcomes. Importantly, our database enables us to test etiological hypotheses in the context of schizophrenia and emerging subclinical psychotic symptoms involving environmental and genetic factors. A better understanding of etiopathogenic processes is likely to inform precision-medicine approaches, particularly personalized prevention, and therapy strategies. While patient-tailored prediction models integrating biological, environmental, and lifestyle factors are common practice in the diagnosis and management of other disorders, for example, cardiovascular disease [57], such measures still have to be established within psychiatry.

Within BRIDGE-S, we designed a flexible study framework to efficiently recruit a large number of patients while collecting a broad selection of phenotypes by implementing a modular workflow, remote participation, and digital tools. However, compromises between maximizing sample size and depth of phenotyping were made on several levels, including the omission of structured diagnostic interviews in favor of shorter self-reported instruments and chart reviews for case ascertainment. By employing a condensed questionnaire for the initial assessment, we also sought to improve study enrollment and counteract a putative selection bias.

At present, this study does not generate longitudinal data, but follow-up visits are possible through the recall of selected participants. This represents an excellent opportunity to acquire additional biological samples for further omics analyses or information to map disease trajectories, but also to address more specific research questions by targeting individuals with distinct genetic or environmental configurations. Such approaches can be advantageous to efficiently and causally interrogate biological mechanisms behind genetic associations and trial personalized treatment concepts [58].

Availability of data and materials

To promote reproducibility and collaborations, additional study materials and resources such as variable lists, data codebook, metadata, and analysis scripts will be shared on a public GitHub repository after completion of the study and initial dissemination: Summary-level genetic data will be shared publicly via established repositories such as GWAS Catalog, meta-analyses summary statistics will be available for download via the Psychiatric Genomics Consortium (PGC). Genotype data at the individual level can be obtained upon reasonable request either through the authors or the Data Access Committee (DAC) of PGC.

Please reach out to the corresponding author and principal investigator of the study if you are interested in collaborating or working with primary data from BRIDGE-S.



Bipolar Disorder


Berlin Research Initiative for Diagnostics, Genetics and Environmental Factors in Schizophrenia


Brief Resilience Scale


Cambridge Neuropsychological Test Automated Battery


Clinical Global Impression Scale (severity)


Global Assessment of Functioning


Global Screening Array


Childhood Trauma Questionnaire


Genome-wide Association Study


Emotion Recognition Task


Essener Trauma Inventar


Environmental scores


EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions


Fragebogen zur Sozialen Unterstützung 14-item version (Engl.: Perceived Social Support Questionnaire 14-item version)


International Statistical Classification of Diseases and Related Health Problems


Odds ratio multiplicative interaction


Launay-Slade Hallucination Scale


Measurement and Treatment Research to Improve Cognition in Schizophrenia (initiative)


Mood Disorder Questionnaire


Motor Screening Task


Multitasking Test


Odds Ratio


One Touch Stockings of Cambridge


Paired Associates Learning


Positive and Negative Syndrome Scale


Principal Component Analysis


Paranoia Checklist 5


Peters et al. Delusion Inventory 21-item version


Patient Health Questionnaire 4-item version


Polygenic Risk Scores


Psychiatric Genomics Consortium


Personal-Wellbeing-Index for Adults


Quality Control


Rapid Imputation for COnsortias PIpeLIne for GWAS


Resilience Scale 13-item version


Reaction Time


Rapid Visual Information Processing


Suicidal Behavior Questionnaire


Symptom Checklist 90 item revised




Single Nucleotide Polymorphism


Standard Operation Procedure


Schizotypal Personality Questionnaire Brief Revised


Spatial Working Memory


Schizoaffective Disorder


Verbal Recognition Memory


WHO Disability Assessment Schedule 2.0


  1. Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604(7906):502–8.

    Article  CAS  Google Scholar 

  2. Marshall CR, Howrigan DP, Merico D, Thiruvahindrapuram B, Wu W, Greer DS, et al. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat Genet. 2017;49(1):27–35.

    Article  CAS  Google Scholar 

  3. Singh T, Poterba T, Curtis D, Akil H, Al Eissa M, Barchas JD, et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature. 2022;604(7906):509–16.

    Article  CAS  Google Scholar 

  4. Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a Complex Trait: Evidence From a Meta-analysis of Twin Studies. Arch Gen Psychiatry. 2003;60(12):1187–92.

    Article  Google Scholar 

  5. Hilker R, Helenius D, Fagerlund B, Skytthe A, Christensen K, Werge TM, et al. Heritability of Schizophrenia and Schizophrenia Spectrum Based on the Nationwide Danish Twin Register. Biol Psychiatry. 2018;83(6):492–8.

    Article  Google Scholar 

  6. Yang J, Zeng J, Goddard ME, Wray NR, Visscher PM. Concepts, estimation and interpretation of SNP-based heritability. Nat Genet. 2017;49(9):1304–10.

    Article  CAS  Google Scholar 

  7. Uher R, Zwicker A. Etiology in psychiatry: embracing the reality of poly-gene-environmental causation of mental illness. World Psychiatry Off J World Psychiatr Assoc WPA. 2017;16(2):121–9.

    Google Scholar 

  8. Stilo SA, Murray RM. Non-Genetic Factors in Schizophrenia. Curr Psychiatry Rep. 2019;21(10):100–100.

    Article  Google Scholar 

  9. Radua J, Ramella-Cravaro V, Ioannidis JPA, Reichenberg A, Phiphopthatsanee N, Amir T, et al. What causes psychosis? An umbrella review of risk and protective factors. World Psychiatry. 2018;17(1):49–66.

    Article  Google Scholar 

  10. Arango C, Díaz-Caneja CM, McGorry PD, Rapoport J, Sommer IE, Vorstman JA, et al. Preventive strategies for mental health. Lancet Psychiatry. 2018;5(7):591–604.

    Article  Google Scholar 

  11. Kendler KS. A Prehistory of the Diathesis-Stress Model: Predisposing and Exciting Causes of Insanity in the 19th Century. Am J Psychiatry. 2020;177(7):576–88.

    Article  Google Scholar 

  12. Modinos G, Iyegbe C, Prata D, Rivera M, Kempton MJ, Valmaggia LR, et al. Molecular genetic gene–environment studies using candidate genes in schizophrenia: A systematic review. Schizophr Res. 2013;150(2):356–65.

    Article  Google Scholar 

  13. Zwicker A, Denovan-Wright EM, Uher R. Gene-environment interplay in the etiology of psychosis. Psychol Med. 2018;48(12):1925–36.

    Article  Google Scholar 

  14. Dick DM, Agrawal A, Keller MC, Adkins A, Aliev F, Monroe S, et al. Candidate Gene-Environment Interaction Research: Reflections and Recommendations. Perspect Psychol Sci J Assoc Psychol Sci. 2015;10(1):37–59.

    Article  Google Scholar 

  15. Guloksuz S, Pries LK, Delespaul P, Kenis G, Luykx JJ, Lin BD, et al. Examining the independent and joint effects of molecular genetic liability and environmental exposures in schizophrenia: results from the EUGEI study. World Psychiatry Off J World Psychiatr Assoc WPA. 2019;18(2):173–82.

    Google Scholar 

  16. Vassos E, Kou J, Tosato S, Maxwell J, Dennison CA, Legge SE, et al. Lack of Support for the Genes by Early Environment Interaction Hypothesis in the Pathogenesis of Schizophrenia. Schizophr Bull. 2022;48(1):20–6.

    Article  Google Scholar 

  17. Ursini G, Punzi G, Chen Q, Marenco S, Robinson JF, Porcelli A, et al. Convergence of placenta biology and genetic risk for schizophrenia. Nat Med. 2018;24(6):792–801.

    Article  CAS  Google Scholar 

  18. Vassos E, Sham P, Kempton M, Trotta A, Stilo SA, Gayer-Anderson C, et al. The Maudsley environmental risk score for psychosis. Psychol Med. 2020;50(13):2213–20.

    Article  Google Scholar 

  19. Pries LK, Lage-Castellanos A, Delespaul P, Kenis G, Luykx JJ, Lin BD, et al. Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study. Schizophr Bull. 2019;45(5):960–5.

    Article  Google Scholar 

  20. Mas S, Boloc D, Rodríguez N, Mezquida G, Amoretti S, Cuesta MJ, et al. Examining Gene-Environment Interactions Using Aggregate Scores in a First-Episode Psychosis Cohort. Schizophr Bull. 2020;46(4):1019–25.

    Article  Google Scholar 

  21. Pries LK, Erzin G, van Os J, ten Have M, de Graaf R, van Dorsselaer S, et al. Predictive Performance of Exposome Score for Schizophrenia in the General Population. Schizophr Bull. 2021;47(2):277–83.

    Article  Google Scholar 

  22. Stepniak B, Papiol S, Hammer C, Ramin A, Everts S, Hennig L, et al. Accumulated environmental risk determining age at schizophrenia onset: a deep phenotyping-based study. Lancet Psychiatry. 2014;1(6):444–53.

    Article  Google Scholar 

  23. Pries LK, Ferro GAD, van Os J, Delespaul P, Kenis G, Lin BD, et al. Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum. Epidemiol Psychiatr Sci. 2020;29:e182.

    Article  Google Scholar 

  24. Wambua GN, Kilian S, Ntlantsana V, Chiliza B. The association between resilience and psychosocial functioning in schizophrenia: A systematic review and meta-analysis. Psychiatry Res. 2020;1(293):113374.

    Article  Google Scholar 

  25. Korver N, Quee PJ, Boos HBM, Simons CJP, de Haan L, GROUP investigators. Genetic Risk and Outcome of Psychosis (GROUP), a multi-site longitudinal cohort study focused on gene-environment interaction: objectives, sample characteristics, recruitment and assessment methods. Int J Methods Psychiatr Res. 2012;21(3):205–21.

    Article  Google Scholar 

  26. European Network of National Networks studying Gene-Environment Interactions in Schizophrenia (EU-GEI). Identifying Gene-Environment Interactions in Schizophrenia: Contemporary Challenges for Integrated Large-scale Investigations. Schizophr Bull. 2014;40(4):729–36.

    Article  Google Scholar 

  27. Ribbe K, Friedrichs H, Begemann M, Grube S, Papiol S, Kästner A, et al. The cross-sectional GRAS sample: A comprehensive phenotypical data collection of schizophrenic patients. BMC Psychiatry. 2010;10(1):91.

    Article  Google Scholar 

  28. Mullins N, Forstner AJ, O’Connell KS, Coombes B, Coleman JRI, Qiao Z, et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat Genet. 2021;53(6):817–29.

    Article  CAS  Google Scholar 

  29. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.

    Article  Google Scholar 

  30. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. 2019;1(95):103208.

    Article  Google Scholar 

  31. Wingenfeld K, Spitzer C, Mensebach C, Grabe HJ, Hill A, Gast U, et al. The German version of the Childhood Trauma Questionnaire (CTQ): preliminary psychometric properties. Psychother Psychosom Med Psychol. 2010;60(11):442–50.

    Article  Google Scholar 

  32. Tagay S. Das Essener Trauma-Inventar (ETI) – Ein Screeninginstrument zur Identifikation traumatischer Ereignisse und posttraumatischer Störungen. Z Für Psychotraumatologie Psychother Psychol Med. 2007;5(1):75.

    Google Scholar 

  33. Leppert K, Koch B, Brähler E, Strauß B. Die Resilienzskala (RS) – Überprüfung der Langform RS-25 und einer Kurzform RS-13. 2008;22.

  34. Chmitorz A, Wenzel M, Stieglitz RD, Kunzler A, Bagusat C, Helmreich I, et al. Population-based validation of a German version of the Brief Resilience Scale. PLoS ONE. 2018;13(2):e0192761.

    Article  Google Scholar 

  35. Fydrich T, Sommer G, Tydecks S, Brähler E. Fragebogen zur sozialen Unterstützung (F-SozU): Normierung der Kurzform (K-14). Z Für Med Psychol. 2009;18(1):43–8.

    Google Scholar 

  36. Glaesmer H, Kapusta ND, Teismann T, Wagner B, Hallensleben N, Spangenberg L, et al. Psychometrische Eigenschaften der deutschen Version des Suicide Behaviors Questionnaire Revised (SBQ-R). PPmP - Psychother Psychosom Med Psychol. 2018;68(8):346–52.

    Article  Google Scholar 

  37. Renn D, Pfaffenberger N, Platter M, Mitmansgruber H, Cummins RA, Höfer S. International Well-being Index: The Austrian Version. Soc Indic Res. 2009;90(2):243–56.

    Article  Google Scholar 

  38. Löwe B, Wahl I, Rose M, Spitzer C, Glaesmer H, Wingenfeld K, et al. A 4-item measure of depression and anxiety: Validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. J Affect Disord. 2010;122(1):86–95.

    Article  Google Scholar 

  39. Lincoln TM, Keller E, Rief W. Die Erfassung von Wahn und Halluzinationen in der Normalbevölkerung. Diagnostica. 2009;55(1):29–40.

    Article  Google Scholar 

  40. Schlier B, Moritz S, Lincoln TM. Measuring fluctuations in paranoia: Validity and psychometric properties of brief state versions of the Paranoia Checklist. Psychiatry Res. 2016;30(241):323–32.

    Article  Google Scholar 

  41. Cohen AS, Matthews RA, Najolia GM, Brown LA. Toward a more psychometrically sound brief measure of schizotypal traits: introducing the SPQ-Brief Revised. J Personal Disord. 2010;24(4):516–37.

    Article  CAS  Google Scholar 

  42. Meyer TD, Bernhard B, Born C, Fuhr K, Gerber S, Schaerer L, et al. The Hypomania Checklist-32 and the Mood Disorder Questionnaire as screening tools — going beyond samples of purely mood-disordered patients. J Affect Disord. 2011;128(3):291–8.

    Article  Google Scholar 

  43. Cambridge Cognition. CANTAB® [Cognitive assessment software]. [Internet]. 2019. Available from: All rights reserved.

  44. Veale JF. Edinburgh Handedness Inventory - Short Form: a revised version based on confirmatory factor analysis. Laterality. 2014;19(2):164–77.

    Article  Google Scholar 

  45. Kay SR, Fiszbein A, Opler LA. The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia. Schizophr Bull. 1987;13(2):261–76.

    Article  CAS  Google Scholar 

  46. Guy W, National Institute of Mental Health (U.S.). Psychopharmacology Research Branch. Division of Extramural Research Programs. ECDEU assessment manual for psychopharmacology [Internet]. Rockville, Md. : U.S. Dept. of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute of Mental Health, Psychopharmacology Research Branch, Division of Extramural Research Programs; 1976 [cited 2022 Nov 8]. 616 p. Available from:

  47. Henning Sass. Diagnostische Kriterien und Differentialdiagnosen des Diagnostischen und statistischen Manuals psychischer Störungen DSM-III-R. Weinheim ; Basel: Beltz; 1989. 351 S.

  48. Üstün TB, Chatterji S, Kostanjsek N, Rehm J, Kennedy C, Epping-Jordan J, et al. Developing the World Health Organization Disability Assessment Schedule 2.0. Bull World Health Organ. 2010;88(11):815–23.

    Article  Google Scholar 

  49. Schmitz N, Hartkamp N, Kiuse J, Franke GH, Reister G, Tress W. The Symptom Check-List-90-R (SCL-90-R): A German validation study. Qual Life Res. 2000;9(2):185–93.

    Article  CAS  Google Scholar 

  50. Franke G. Die Symptom-Checkliste von Derogatis (SCL-90-R) - Deutsche Version - Manual. 2002.

    Google Scholar 

  51. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am J Hum Genet. 2007;81(3):559–75.

    Article  CAS  Google Scholar 

  52. Lam M, Awasthi S, Watson HJ, Goldstein J, Panagiotaropoulou G, Trubetskoy V, et al. RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics. 2020;36(3):930–3.

    Article  CAS  Google Scholar 

  53. Pries LK, Erzin G, Rutten BPF, van Os J, Guloksuz S. Estimating Aggregate Environmental Risk Score in Psychiatry: The Exposome Score for Schizophrenia. Front Psychiatry. 2021;12:671334.

    Article  Google Scholar 

  54. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89-98.

    Article  CAS  Google Scholar 

  55. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Routledge; 1988. p. 567.

    Google Scholar 

  56. VanderWeele TJ. Sample Size and Power Calculations for Additive Interactions. Epidemiol Methods. 2012;1(1):159–88.

    CAS  Google Scholar 

  57. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Brindle P. Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart. 2008;94(1):34.

    Article  CAS  Google Scholar 

  58. Corbin LJ, Tan VY, Hughes DA, Wade KH, Paul DS, Tansey KE, et al. Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference. Nat Commun. 2018;9(1):711.

    Article  Google Scholar 

Download references


Data collection further relies on research infrastructure implemented by the Clinical Research Unit (CRU) of the Berlin Institute for Health (BIH), which provides technical support for the REDCap database and CANTAB licenses. Biological samples are stored at the central biobank (ZeBanC), a core facility of Charité Universitätsmedizin—Berlin and BIH.


Open Access funding enabled and organized by Projekt DEAL. Between 2018 and 2020 the data collection was financially supported by the Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard. Since 2020 the study is funded by an Individual Research Grant of the German Research Foundation (DFG); for a summary, see None of the third-party funding bodies had any influence over the design and execution of the study.

Author information

Authors and Affiliations



A.B. and J.K. wrote the first draft of the manuscript. A.B., S.R., and J.K. contributed substantially to the planning and development of the study protocol and standard procedures. S.R. functions as the principal investigator and supervises data processing and statistical analyses. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Stephan Ripke.

Ethics declarations

Ethics approval and consent to participate

Full ethical approval for the outlined study was granted in June 2018 by the local ethics committee at Charité Universitätsmedizin, Berlin (EA1/119/18). The collection of cognitive data was approved in March 2019 (EA1/067/19) by the same committee. Written informed consent will be obtained from all the study participants prior to investigation. All research is carried out in accordance with the Declaration of Helsinki, data protection laws and good clinical practice (GCP) as well as other relevant guidelines and regulations.

Consent for publication

The manuscript does not contain sensitive information on any individual that participated in BRIDGE-S.

Competing interests

None of the authors involved in this study report a conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

CANTAB battery and test variants (completed in the exact same order). Figure S1. A priori sample size calculation based on expected small effect sizes.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Braun, A., Kraft, J. & Ripke, S. Study protocol of the Berlin Research Initiative for Diagnostics, Genetics and Environmental Factors in Schizophrenia (BRIDGE-S). BMC Psychiatry 23, 31 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • GWAS
  • Gene-environment interaction
  • Schizophrenia
  • Psychosis
  • Psychiatric genetics
  • Risk-prediction