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DeepGenePrior:一种深度学习模型,用于优先考虑受拷贝数变异影响的基因
Zahra Rahaie1, Hamid R Rabiee1, Hamid Alinejad-Rokny2
1BCB Group, DML, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
PLoS computational biology
|July 24, 2023
概括
DeepGenePrior是一种新的深度学习模型,通过使用副本数变体 (CNVs) 来提高脑疾病的基因优先级. 这种方法改善了与疾病相关的基因的识别,揭示了自闭症,精神分裂症和发育迟缓之间潜在的共同遗传联系.
科学领域:
- 神经遗传学 神经遗传学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 遗传性脑疾病表现出高度的异质性,使基因发现复杂化.
- 目前的基因优先级方法依赖于有限的证据,可以产生错误的阳性/负性结果.
- 识别致病基因对于理解和治疗中枢神经系统异常至关重要.
研究的目的:
- 介绍DeepGenePrior,这是一个深度神经网络模型,用于在遗传性脑疾病中优先考虑候选基因.
- 开发一种使用变异自编码器 (VAE) 进行基因影响评估的新型评分系统.
- 专门利用副本数变异 (CNV) 来确定基因优先级,克服现有方法的局限性.
主要方法:
- 开发了DeepGenePrior,这是一个利用变量自动编码器 (VAE) 的深度学习模型.
- 分析了来自自闭症,精神分裂症和发育迟缓队列的74,811名个体的CNV数据.
- 优先考虑的候选基因仅基于CNV数据,而不依赖于先前的关联或辅助数据.
主要成果:
- 与之前的研究相比,大脑表达基因的折叠丰富度增加了12%.
- 观察到与小鼠神经系统表型相关的基因增加了15%.
- 在所有三种疾病中确定了ZDHHC8,DGCR5和CATG00000022283的共享删除,这表明了共同的遗传基础.
结论:
- DeepGenePrior 通过使用 CNVs 有效地优先考虑使用 CNVs 治疗大脑疾病的候选基因.
- 这些发现表明自闭症,精神分裂症和发育迟缓的潜在共同遗传病因.
- DeepGenePrior模型是公开可用的,用于在复杂的神经疾病中推进基因发现.
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