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

在过去的十年里，在现代高通量技术和精神病学遗传学领域的大规模合作的推动下，在揭开精神分裂症(SCZ)的遗传结构方面取得了巨大的进展。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，多个获得性风险因素和遗传脆弱性之间的关系在很大程度上仍然难以捉摸。更复杂的是，越来越多的证据表明，某些保护性因素可以提高对不利心理健康结果的复原力，特别是在“高风险”个人中。检查几个遗传或环境决定因素的研究很少，主要是因为缺乏合适的数据源，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，我们的目标是建立一个新的队列，至少包括1300名SCZ患者和1300名未受影响的对照，遵循已建立的多阶段招募和模块化表型策略。数据收集包括遗传信息(全基因组的SNP数据)、全面的认知和临床评估、获取健康数据的问卷调查以及与SCZ相关的环境暴露。具体研究目标包括：i)发现SCZ的常见和罕见遗传变异II)使用单变量和多风险预测模型探索基因-环境相互作用；iii)开发一个综合的数据分析框架，通过广义回归、结构方程建模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通路的多基因风险评分技术来推断病因不同的亚组潜在的G-E星座。因此，对代表性样本中的遗传、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.