Zhang Hanxin, Khan Atif, Kushner Steven A, Rzhetsky Andrey
Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, IL, 60637, USA.
Department of Medicine, and Institute of Genomics and Systems Biology, The University of Chicago, Chicago, IL, 60637, USA.
Schizophrenia (Heidelb). 2022 May 10;8(1):51. doi: 10.1038/s41537-022-00257-5.
Schizophrenia is among the leading causes of disability worldwide. Prior studies have conclusively demonstrated that the etiology of schizophrenia contains a strong genetic component. However, the understanding of environmental contributions and gene-environment interactions have remained less well understood. Here, we estimated the genetic and environmental contributions to schizophrenia risk using a unique combination of data sources and mathematical models. We used the administrative health records of 481,657 U.S. individuals organized into 128,989 families. In addition, we employed rich geographically specific measures of air, water, and land quality across the United States. Using models of progressively increasing complexity, we examined both linear and non-linear contributions of genetic variation and environmental exposures to schizophrenia risk. Our results demonstrate that heritability estimates differ significantly when gene-environment interactions are included in the models, dropping from 79% for the simplest model, to 46% in the best-fit model which included the full set of linear and non-linear parameters. Taken together, these findings suggest that environmental factors are an important source of explanatory variance underlying schizophrenia risk. Future studies are warranted to further explore linear and non-linear environmental contributions to schizophrenia risk and investigate the causality of these associations.
精神分裂症是全球致残的主要原因之一。先前的研究已经确凿地证明,精神分裂症的病因包含很强的遗传成分。然而,对于环境因素的作用以及基因 - 环境相互作用的理解仍不够充分。在此,我们使用独特的数据来源和数学模型组合,估计了遗传因素和环境因素对精神分裂症风险的影响。我们使用了481,657名美国个体的行政健康记录,这些个体被组织成128,989个家庭。此外,我们采用了美国各地丰富的特定地理区域的空气、水和土地质量测量数据。通过使用复杂度逐渐增加的模型,我们研究了遗传变异和环境暴露对精神分裂症风险的线性和非线性影响。我们的结果表明,当模型中纳入基因 - 环境相互作用时,遗传度估计值有显著差异,从最简单模型的79%降至包含全套线性和非线性参数的最佳拟合模型中的46%。综上所述,这些发现表明环境因素是精神分裂症风险潜在解释变异的重要来源。未来的研究有必要进一步探索环境因素对精神分裂症风险的线性和非线性影响,并研究这些关联的因果关系。