精神分裂症,双相情感障碍和主要抑郁障碍与并发性特征和深度学习算法的分类
Xiangning Chen1,2, Yimei Lu3, Joan Manuel Cue3
1Center for Precision Medicine, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA. xiangning.chen@uth.tmc.edu.
Schizophrenia (Heidelberg, Germany)
|February 5, 2025
概括
精神疾病如精神分裂症,双相情感障碍和严重抑郁症之间的遗传联系可以改善其分类. 单靠相关特征的多基因风险得分可以有效地将患者与对照区分开来.
科学领域:
- 精神病学遗传学 精神病学遗传学
- 计算精神病学是一种计算精神病学.
- 生物信息学是一种生物信息学.
背景情况:
- 精神疾病往往具有共同的基因因素,这使得精确的诊断和区分变得复杂.
- 了解这些共同的遗传责任对于开发有针对性的分类策略至关重要.
研究的目的:
- 调查多基因风险评分 (PRSs) 从并发性特征在分类精神分裂症 (SCZ),双相情感障碍 (BIP) 和严重抑郁障碍 (MDD) 中的有用性.
- 为了确定单独来自共病特征的PRS是否可以将这些疾病与未受影响的对照区分开来.
主要方法:
- 用PRS对42种与SCZ,BIP和MDD共存的特征进行了利用.
- 采用机器学习模型来分类疾病,使用特定目标PRS和并发性特征PRS的组合.
- 评估了各种PRS组合的分类准确性和曲线下的面积 (AUC).
主要成果:
- 将特定目标PRS与共患性特征PRS结合起来,显著改善了疾病分类.
- 仅从共患性特征的PRS就获得了SCZ (0.710 ± 0.008),BIP (0.782 ± 0.006) 和MDD (0.753 ± 0.019) 的显著分类准确性.
- 伴并发性特征PRS单独有效地将SCZ,BIP和MDD患者与对照患者区分开来 (精度为0.861 ± 0.003,AUC为0.961 ± 0.041).
结论:
- 共同的遗传责任,如被共患性特征的PRS所捕获的那样,对于分类和区分主要精神疾病是有效的.
- 同病性特征PRS的强表现表明,这些疾病中的风险变异存在显著的重叠.
- 利用遗传信息的数据驱动方法对精神疾病分类有希望.
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