Nonlinear kernel-based high-dimensional inference for set-based genetic association studies

doi:10.1093/bib/bbag275

2026-05-27

DOI：10.1093/bib/bbag275

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非线性遗传架构，包括上位性和阈值效应，日益被认为是复杂疾病风险的贡献因素，但现有的大部分SNP集关联检验依赖线性建模假设，导致在遗传效应非线性或变异间异质分布时功效降低和推断不稳定。为解决此局限，我们提出了一种非线性高维推断框架，用于集基遗传关联分析，该框架整合了可扩展的核表示与有效的统计推断。该框架结合基于距离相关性的确信独立性筛选以减少超维预测变量，利用Nystrom近似进行核主成分分析以提取非线性特征，以及采用去稀疏化LASSO实现在高维下的渐近有效假设检验，并辅以两阶段综合检验策略，自适应地聚合互补信号模型的证据。广泛模拟研究显示，所提方法能保持良好的第一类错误校准，并持续在包括Sequence Kernel Association Test和自适应加权幂和检验在内的传统集基方法中取得更高的功效，尤其在非线性及异质遗传效应场景下，在线性设置中仍具竞争力。对阿尔茨海默病神经影像倡议数据的分析识别出与脑区体积相关的基因水平关联，这些关联涉及神经元兴奋性、钙信号和细胞骨架调控，这些生物学过程在神经退行性变中起核心作用。综上，本研究为全基因组研究中的非线性集基推断提供了一种稳健且可扩展的框架，扩展了剖析复杂疾病遗传贡献的分析工具箱。

Keyword:

nonlinear genetic architecture

kernel-based inference

set-based association testing

high-dimensional statistics

epistasis

期刊

Briefings in Bioinformatics

IF:

7.7

论文数:

5.8K

被引数:

2.7W

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Hebei Medical University

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michigan state university

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3.6W

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被引数: 44

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