Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment

作者：Lanlan Li, Yeying Yang, Qi Zhang, Jiao Wang, Jiehui Jiang, Alzheimer’s Disease Neuroimaging Initiative · 发表于：Behavioural Neurology · 年份：2021 · DOI：10.1155/2021/3359103 · 被引用次数：20 · 研究领域：Genetic Associations and Epidemiology、Genomics and Rare Diseases、Alzheimer's disease research and treatments

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 fro...