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When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
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此摘要是机器生成的。

在阿尔茨海默病 (AD) 队列中确定了异质性,包括年龄和APOE概况. 优化这些多样化的队列改善了AD基因的发现和恢复,提供了新的研究机会.

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科学领域:

  • 遗传学 是一个遗传学.
  • 神经科学是一个神经科学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 阿尔茨海默氏病 (AD) 影响着数百万人,需要在基因发现和治疗方面取得进展.
  • 阿尔茨海默氏病测序项目 (ADSP) 通过阿尔茨海默氏病数据存储网站 (NIAGADS) 的全国老龄化遗传研究所 (NIAGADS) 收集了大量的测序数据.
  • 在NIAGADS子队列中观察到的表型异质性 (性别,APOE,年龄) 促使了这一调查.

研究的目的:

  • 为了解决阻碍强大的AD风险发现的队列异质性.
  • 从不同的NIAGADS WES (R2) 和WGS (R4) 数据中生成一个统一的数据集.
  • 为了识别和验证新的AD候选基因.

主要方法:

  • 利用来自NIAGADS R2和R4数据集的56000个AD和健康受试者样本.
  • 进行质量控制,包括遗传祖先预测.
  • 根据性别,年龄,发病年龄和APOE基因型分析了子队列,识别和删除异常子队列.
  • 应用管道用于识别和计算/实验验证AD候选基因.

主要成果:

  • 在AD和健康受试者之间,APOE和年龄分布的确诊显著差异.
  • 识别并删除了具有不同年龄和APOE配置文件的异常子队列.
  • 删除异常值增强了AD基因的发现,改善了已知的AD基因的复制 (R2的p < 1.2e-3,R4的p < 8.6e-5) 和STRING网络连接 (z > 5.6).
  • 在剩余队伍中分析极端发病年龄的病例进一步促进了基因发现.

结论:

  • NIAGADS R2-R4队列在APOE和年龄分布中表现出显著的异质性.
  • 根据这些分布优化队列可以明显改善AD基因恢复.
  • 队列异质性为研究特定子组 (例如,发病年龄,APOE资料) 提供了机会.
  • 这些发现可以为AD病例/对照选择的标准标准提供信息,推动未来的研究.