在中国人口中基于SNP的精神分裂症预测模型的识别
Zhiying Yang1, Shun Yao1,2, Yichong Xu1
1Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
这项研究开发了一种使用5个特定SNP来识别中国人口中精神分裂症的预测模型. 该模型在预测精神分裂症和区分其与双相情感障碍方面表现良好.
科学领域:
- 遗传学 是一个遗传学.
- 精神病学是一个精神病学.
- 生物信息学是一种生物信息学.
背景情况:
- 精神分裂症是一种高度遗传的精神障碍.
- 全基因组关联研究已经确定了许多精神分裂症易感基因和SNP.
- 使用SNP预测精神分裂症和诊断仍然具有挑战性.
研究的目的:
- 为了在中国人群中识别精神分裂症的易感性SNP.
- 使用已识别的SNP构建精神分裂症的预测模型.
- 评估模型的诊断和差异诊断疗效.
主要方法:
- 在210名参与者中分析了14个SNP的基因型频率 (70例精神分裂症,70例双相情感障碍,70例对照).
- 使用选择的SNP和回归分析构建了一个预测模型.
- 用接收器操作特征 (ROC) 曲线来评估模型性能.
主要成果:
- 五个SNP (rs148415900, rs71428218, rs4666990, rs112222723, rs1716180) 已被选择用于该模型.
- 该模型实现了0.719的曲线下面面积 (AUC) 来预测精神分裂症.
- 该模型显示AUC为0.591,用于区分精神分裂症和双相情感障碍.
结论:
- SNP风险评分预测模型显示了对精神分裂症预测的前景.
- 这种基于SNP的模型是区分精神分裂症与其他精神疾病的新型.
- 潜在的临床应用包括改善精神分裂症的诊断,治疗和预测结果.
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