Abstract：
Schizophrenia is a polygenic complex disease with a heritability as high as 80 %, yet the mechanism of polygenic interaction in its pathogenesis remains unclear. Studying the interaction and regulation of schizophrenia susceptibility genes is crucial for unraveling the pathogenesis of schizophrenia and developing antipsychotic drugs. Therefore, we developed a bioinformatics method named GRACI (Gene Regulation Analysis based on Causal Inference) based on the principles of information theory, a causal inference model, and high order chromatin 3D conformation. GRACI captures the interaction and regulatory relationships between schizophrenia susceptibility genes by analyzing genotyping data. Two datasets, comprising 1459 and 2065 samples respectively, were analyzed, and the gene networks from both datasets were constructed. GRACI showcased superior accuracy when compared to widely adopted methods for detecting gene-gene interactions and intergenic regulation. This alignment was further substantiated by its correlation with chromatin high-order conformation patterns. Using GRACI, we identified three potential genes-KCNN3, KCNH1, and KCND3-that are directly associated with schizophrenia pathogenesis. Furthermore, the results of GRACI on the standalone dataset illustrated the method\'s applicability to other complex diseases. GRACI download: https://github.com/liuliangjie19/GRACI.
摘要：
精神分裂症是一种多基因复杂疾病，遗传率高达80%，然而，多基因相互作用在其发病机制中的作用机制仍不清楚。研究精神分裂症易感基因的相互作用和调节对于揭示精神分裂症的发病机制和开发抗精神病药物至关重要。因此,我们开发了一种基于信息论原理的生物信息学方法，称为GRACI（基于因果关系的基因调控分析），因果推理模型，和高阶染色质3D构象。GRACI通过分析基因分型数据捕获精神分裂症易感基因之间的相互作用和调控关系。两个数据集,分别包含1459和2065个样本，被分析,并构建了来自两个数据集的基因网络。与广泛采用的检测基因-基因相互作用和基因间调控的方法相比，GRACI显示出更高的准确性。这种排列通过其与染色质高阶构象模式的相关性得到进一步证实。使用GRACI，我们确定了与精神分裂症发病机制直接相关的三个潜在基因-KCNN3，KCNH1和KCND3。此外，GRACI在独立数据集上的结果说明了该方法对其他复杂疾病的适用性。GRACI下载：https://github.com/liuliangjie19/GRACI。
