跨多祖先的老年人认知表现的遗传关联分析:托比特模型的应用
Xian Wu1,2, Inori Tsuchiya1,2, Khine Zin Aung1,2
1Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA.
Journal of Alzheimer's disease : JAD
|October 23, 2025
概括
这项研究表明,与线性模型相比,托比特回归模型在不同人群中更好地识别了与晚期阿尔茨海默病 (LOAD) 认知功能相关的遗传变异.
科学领域:
- 神经遗传学 神经遗传学
- 认知神经科学 认知神经科学
- 统计遗传学 统计遗传学
背景情况:
- 之前关于晚期阿尔茨海默病 (LOAD) 遗传变异和认知功能的研究主要集中在欧洲祖先和使用线性模型上,由于测试成绩的上限效应,可能缺少关联.
- 与认知表现的遗传关联在不同的祖先群体中可能有所不同.
研究的目的:
- 调查与LOAD相关的单核酸多态 (SNPs) 和跨多个遗传祖先群体的认知表现之间的关联.
- 将托比特回归模型与传统线性模型在识别遗传关联方面的有效性进行比较,特别是当认知测试得分表现出天花板效应时.
主要方法:
- 利用了来自国家阿尔茨海默氏症协调中心 (NACC) 统一数据集 (UDS) 和阿尔茨海默氏症遗传学联盟 (ADGC) 的表型和基因型数据.
- 从ADGC基因型数据中确定了遗传祖先群 (非西班牙裔白人,非裔美国人,西班牙裔,亚洲人).
- 应用了托比特和线性回归模型来分析84个LOAD相关SNP与使用NACC UDS数据的认知表现之间的关联.
主要成果:
- 认知功能的遗传架构在不同的遗传祖先群体中存在显著差异.
- 托比特模型发现TMEM106B-rs13237518A和认知功能之间存在显著的关联,而线性模型却忽略了这一点.
- APOE-rs429358 ((C) 在所有祖先群体中显示出与全球认知功能的负相关性. 特定的SNP与痴呆症子组的认知状态有关 (例如,亚洲人的ADAMTS1,西班牙人的SHARPIN).
结论:
- 托比特回归模型为分析与认知表现的遗传关联提供了优越的适合性,特别是对于诸如全球认知和语言测试等通常表现出天花板效应的措施.
- 这些发现强调了考虑多样化的遗传祖先群体和使用先进的统计模型的重要性,以充分理解与LOAD相关的认知衰退的遗传基础.
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