Yang Zhiying, Yao Shun, Xu Yichong, Zhang Xiaoqing, Shi Yuan, Wang Lijun, Cui Donghong
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Neuropsychiatr Dis Treat. 2024 Aug 9;20:1553-1561. doi: 10.2147/NDT.S466554. eCollection 2024.
Schizophrenia is a devastating mental disease with high heritability. A growing number of susceptibility genes associated with schizophrenia, as well as their corresponding SNPs loci, have been revealed by genome-wide association studies. However, using SNPs as predictors of disease and diagnosis remains difficult. Here, we aimed to uncover susceptibility SNPs in a Chinese population and to construct a prediction model for schizophrenia.
A total of 210 participants, including 70 patients with schizophrenia, 70 patients with bipolar disorder, and 70 healthy controls, were enrolled in this study. We estimated 14 SNPs using published risk loci of schizophrenia, and used these SNPs to build a model for predicting schizophrenia via comparison of genotype frequencies and regression. We evaluated the efficacy of the diagnostic model in schizophrenia and control patients using ROC curves and then used the 70 patients with bipolar disorder to evaluate the model's differential diagnostic efficacy.
5 SNPs were selected to construct the model: rs148415900, rs71428218, rs4666990, rs112222723 and rs1716180. Correlation analysis results suggested that, compared with the risk SNP of 0, the risk SNP of 3 was associated with an increased risk of schizophrenia (OR = 13.00, 95% CI: 2.35-71.84, = 0.003). The ROC-AUC of this prediction model for schizophrenia was 0.719 (95% CI: 0.634-0.804), with the greatest sensitivity and specificity being 60% and 80%, respectively. The ROC-AUC of the model in distinguishing between schizophrenia and bipolar disorder was 0.591 (95% CI: 0.497-0.686), with the greatest sensitivity and specificity being 60% and 55.7%, respectively.
The SNP risk score prediction model had good performance in predicting schizophrenia. To the best of our knowledge, previous studies have not applied SNP-based models to differentiate between cases of schizophrenia and other mental illnesses. It could have several potential clinical applications, including shaping disease diagnosis, treatment, and outcomes.
精神分裂症是一种具有高遗传性的毁灭性精神疾病。全基因组关联研究已经揭示了越来越多与精神分裂症相关的易感基因及其相应的单核苷酸多态性（SNP）位点。然而，使用SNP作为疾病预测和诊断指标仍然具有挑战性。在此，我们旨在揭示中国人群中的易感SNP，并构建一个精神分裂症预测模型。
本研究共纳入210名参与者，包括70名精神分裂症患者、70名双相情感障碍患者和70名健康对照。我们利用已发表的精神分裂症风险位点估计了14个SNP，并通过比较基因型频率和回归分析，使用这些SNP构建了一个预测精神分裂症的模型。我们使用ROC曲线评估了该诊断模型在精神分裂症患者和对照患者中的效能，然后使用70名双相情感障碍患者评估了该模型的鉴别诊断效能。
选择了5个SNP构建模型：rs148415900、rs71428218、rs4666990、rs112222723和rs1716180。相关性分析结果表明，与风险SNP为0的情况相比，风险SNP为3与精神分裂症风险增加相关（OR = 13.00，95% CI：2.35 - 71.84，P = 0.003）。该精神分裂症预测模型的ROC-AUC为0.719（95% CI：0.634 - 0.804），最大灵敏度和特异度分别为60%和80%。该模型在区分精神分裂症和双相情感障碍方面的ROC-AUC为0.591（95% CI：0.497 - 0.686），最大灵敏度和特异度分别为60%和55.7%。
SNP风险评分预测模型在预测精神分裂症方面具有良好性能。据我们所知，以往研究尚未应用基于SNP的模型来区分精神分裂症病例与其他精神疾病。它可能具有多种潜在临床应用，包括影响疾病诊断、治疗和预后。
