Lee Eunjee, Ibrahim Joseph G, Zhu Hongtu
Department of Information and Statistics, Chungnam National University, Daejeon 34134, Korea.
Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.
Genomics Inform. 2023 Sep;21(3):e28. doi: 10.5808/gi.23047. Epub 2023 Jun 28.
Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.
轻度认知障碍（MCI）是一种以认知障碍的发生和发展为特征的临床综合征，通常被认为是向阿尔茨海默病（AD）过渡的阶段。快速进展为AD的MCI患者的遗传特征可以提高早期诊断能力，并促进AD药物的研发。虽然全基因组关联研究（GWAS）是识别与疾病相关的单核苷酸多态性（SNP）的标准工具，但由于对多重检验的严格控制，它无法检测到效应大小较小的SNP。此外，该方法没有考虑SNP的组结构，如基因或连锁不平衡块，而这些结构可以为遗传结构提供有价值的见解。为了解决这些局限性，我们提出了一种贝叶斯双层变量选择方法，该方法可以检测与从MCI转化为AD的时间相关的SNP。我们的方法将组包含指标整合到加速失效时间模型中，以识别重要的SNP组。此外，我们采用数据增强技术，使用预测后验来插补删失的时间值。我们采用狄利克雷-拉普拉斯收缩先验来纳入SNP水平变量选择的组结构。在模拟研究中，我们的方法在变量选择方面优于其他竞争方法。对阿尔茨海默病神经影像倡议（ADNI）数据的分析揭示了几个与AD直接或间接相关的基因，而经典的GWAS没有识别出任何显著的SNP。
