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

在过去的十年中，在现代高通量技术和精神病遗传学领域内的大规模合作的推动下，在解开精神分裂症（SCZ）的遗传结构方面取得了巨大进展。同样，流行病学研究也发现了一系列与SCZ风险相关的环境因素。然而，多种后天风险因素与遗传脆弱性之间的关系在很大程度上仍然难以捉摸。越来越多的证据表明，某些保护因素可以促进对不利心理健康结果的恢复力，特别是在“高风险”个体中。研究超过几个遗传或环境决定因素的研究很少，主要是由于缺乏合适的数据来源，因为SCZ患者在生物库和基于人群的队列中普遍代表性不足。在这里，我们的目标是在建立多阶段招募和模块化表型分析策略后，组建一个至少1,300名SCZ患者和1,300名未受影响的对照的新队列。数据收集包括遗传信息（全基因组SNP数据），全面的认知和临床评估，一份问卷，收集健康数据和强烈暗示的环境暴露，赋予SCZ风险和恢复力。具体的研究目标包括：i）对发现SCZ潜在的常见和罕见遗传变异的贡献ii）使用单变量和多变量探索基因-环境相互作用，风险预测模型iii）开发综合数据分析框架，其通过广义回归、结构方程模型和孟德尔随机化将风险和保护因素结合起来iv）应用最近开发的基于途径的多基因风险评分技术来推断病因学上不同亚组的假定G-E星座。因此，对代表性样本的遗传、流行病学和临床数据进行联合分析，有可能为风险预测和早期干预战略提供有价值的见解。

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.