Objectives. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, , , and using the DLG model, while , , and using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

中文翻译：

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使用深度学习基因组学区分健康个体与阿尔茨海默病或轻度认知障碍患者

目标。阿尔茨海默病（AD）是最常见的神经退行性疾病，也是老年人最常见的痴呆症。某些基因已被确定为 AD 的重要临床危险因素，基因组研究的技术进步，例如全基因组关联研究 (GWAS)，允许进行多态性分析，并已广泛应用于 AD 研究。然而，GWAS 的缺点包括对样本量和遗传性缺失的敏感性，导致分类和预测准确性较低。因此，本文提出了一种新的深度学习基因组学方法，并将其应用于 AD 进展的多任务分类，目的是识别传统 GWAS 分析忽略的新遗传生物标志物。方法。在这项研究中，我们选择了参与阿尔茨海默病神经影像计划的 1461 名受试者的基因型数据，其中包括 622 名 AD 受试者、473 名轻度认知障碍 (MCI) 受试者和 366 名健康对照 (HC) 受试者。所提出的深度学习基因组学（DLG）方法包括三个步骤：质量控制、单核苷酸多态性编码和分类。DLG模型采用ResNet框架，并将结果与​​简单的卷积神经网络结构的分类结果进行比较。所有数据以9:1的比例随机分配到一组训练/验证组和一组测试组。并使用五倍交叉验证。结果。我们将 DLG 模型的分类结果与传统 GWAS 分析的三组分类结果进行了比较。对于 AD 和 HC 组，分类的准确性、敏感性和特异性分别为：, ，和使用DLG模型，同时, ，和使用传统的 GWAS。其他两个组间分类也得到了类似的结果。结论。DLG模型应用于AD进展时可以获得更高的准确性和灵敏度。更重要的是，我们发现了AD进展的几个新的遗传生物标志物，包括HTR2A中的rs6311和rs6313、NAV2中的rs1354269和RFC3中的rs690705。在未来的研究中应该探讨这些新基因座在 AD 中的作用。