用于预测阿尔茨海默病风险和疾病进展速度的 SNP 相互作用的模拟深度学习模型在 19 号染色体上。
A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression.
机构信息
Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.
出版信息
Alzheimers Dement. 2023 Dec;19(12):5690-5699. doi: 10.1002/alz.13319. Epub 2023 Jul 6.
BACKGROUND
Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies.
METHODS
We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed.
RESULTS
Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression.
DISCUSSION
The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.
背景
识别导致阿尔茨海默病（AD）的遗传模式不仅对症状前的风险评估很重要，而且对建立个性化的治疗策略也很重要。
方法
我们使用一种新颖的模拟深度学习模型，对来自阿尔茨海默病神经影像学倡议（Alzheimer's Disease Neuroimaging Initiative）和成像与阿尔茨海默病遗传生物标志物（Imaging and Genetic Biomarkers of Alzheimer's Disease）数据集的 19 号染色体遗传数据进行了分析。该模型使用遮挡法来量化每个单核苷酸多态性（SNP）对 AD 发生概率的贡献及其上位性影响。确定了 19 号染色体上与 AD 风险相关的前 35 个 SNP，并分析了它们预测 AD 进展速度的能力。
结果
发现 rs561311966（APOC1）和 rs2229918（ERCC1/CD3EAP）是影响 AD 风险的最有力因素。前 35 个与 19 号染色体 AD 风险相关的 SNP 是 AD 进展的显著预测因子。
讨论
该模型成功地估计了导致个体 AD 进展的 AD 风险 SNP 的贡献。这有助于建立预防性精准医学。
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