基于基因组数据的深度学习发现了新的基因,预测了注意力缺陷多动症障碍的药理疗法反应
Yilu Zhao1, Zhao Fu1, Eric J Barnett2
1Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, (Peking University S+ixth Hospital), NHC Key Laboratory of Mental Health (Peking University), Beijing, China.
Translational psychiatry
|February 7, 2025
概括
这项研究结合了全基因组关联分析和深度学习,以确定注意力缺陷/多动症 (ADHD) 治疗反应的遗传预测因素. 这些发现揭示了新的基因,并突出了神经和代谢系统之间的联系,用于个性化ADHD治疗.
科学领域:
- 遗传学 是一个遗传学.
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
背景情况:
- 药物治疗对ADHD有效,但预测个体反应仍然具有挑战性.
- 针对ADHD的个性化治疗策略受到治疗有效性的可靠预测指标缺乏的阻碍.
研究的目的:
- 确定影响ADHD药物治疗反应的遗传因素.
- 通过使用遗传数据和深度学习,开发ADHD治疗结果的预测模型.
主要方法:
- 全基因组关联分析 (GWAS) 在以前没有服用过药物的ADHD患者身上进行.
- 深度学习 (DL) 模型,包括卷积神经网络 (CNN),用于根据遗传变异预测症状得分的变化.
- 用各种全基因组P值值来选择遗传变异.
主要成果:
- GWAS确定了两个显著的位点 (rs10880574,rs2000900),涉及TMEM117和MYO5B基因.
- 使用P < E-02的变体的CNN模型在验证和独立测试数据集中表现强.
- 与ADHD和代谢过程相关的NKAIN2基因是CNN模型中的关键预测因素.
结论:
- 整合GWAS和DL成功识别了与ADHD药理反应相关的新基因.
- 这些发现表明ADHD治疗反应中神经和代谢系统之间的相互作用.
- 开发的CNN模型显示了个性化ADHD治疗中临床应用的潜力.
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