Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, 420 University Blvd, Indianapolis, IN 46204, USA.
Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, 420 University Blvd, Indianapolis, IN 46204, USA.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab121.
A large number of genetic variations have been identified to be associated with Alzheimer's disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community including multi-omic markers and their functional connections. Because of this, the immense value of multi-omics data on AD has attracted much attention. Leveraging genomic, transcriptomic and proteomic data, and their backbone network through functional relations, we proposed a modularity-constrained logistic regression model to mine the association between disease status and a group of functionally connected multi-omic features, i.e. single-nucleotide polymorphisms (SNPs), genes and proteins. This new model was applied to the real data collected from the frontal cortex tissue in the Religious Orders Study and Memory and Aging Project cohort. Compared with other state-of-art methods, it provided overall the best prediction performance during cross-validation. This new method helped identify a group of densely connected SNPs, genes and proteins predictive of AD status. These SNPs are mostly expression quantitative trait loci in the frontal region. Brain-wide gene expression profile of these genes and proteins were highly correlated with the brain activation map of 'vision', a brain function partly controlled by frontal cortex. These genes and proteins were also found to be associated with the amyloid deposition, cortical volume and average thickness of frontal regions. Taken together, these results suggested a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes.
大量的遗传变异已被鉴定与阿尔茨海默病（AD）和相关的定量特征有关。然而，大多数现有的研究都集中在单一类型的组学数据上，缺乏生成包括多组学标记物及其功能联系的社区的能力。正因为如此，多组学数据在 AD 上的巨大价值引起了广泛关注。利用基因组、转录组和蛋白质组数据及其通过功能关系的骨干网络，我们提出了一种模块化约束逻辑回归模型，以挖掘疾病状态与一组功能相关的多组学特征（即单核苷酸多态性（SNP）、基因和蛋白质）之间的关联。该新模型应用于宗教秩序研究和记忆与衰老项目队列的额皮质组织中收集的真实数据。与其他最先进的方法相比，它在交叉验证期间提供了整体最佳的预测性能。该新方法有助于确定一组密集连接的 SNP、基因和蛋白质，这些 SNP、基因和蛋白质可预测 AD 状态。这些 SNP 大多是额区的表达数量性状基因座。这些基因和蛋白质的脑广泛表达谱与“视觉”的脑激活图高度相关，“视觉”是部分由额皮质控制的脑功能。这些基因和蛋白质也与淀粉样蛋白沉积、皮质体积和额区的平均厚度有关。综上所述，这些结果表明了从 SNP 到基因表达、蛋白质表达，最终到脑功能和结构变化的 AD 发展的潜在途径。
