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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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使用机器学习模型评估多基因风险来预测阿尔茨海默病.

Tiffany Ngai1,2, Julian Willett1, Mohammad Waqas1

  • 1Department of Neurology, Genetics and Aging Research Unit and the McCance Center for Brain Health, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.

Alzheimer's & dementia : the journal of the Alzheimer's Association
|November 8, 2024
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概括

早期发现阿尔茨海默氏病 (AD) 是至关重要的. 使用基因组学和蛋白质组学的多体模型确定了GFAP和CXCL17蛋白质作为强烈的预测因子,使得早期的症状前诊断成为可能.

关键词:
阿尔茨海默氏症的疾病是阿尔茨海默氏症.机器学习是机器学习.俄米克斯 (omicsics) 是一个电子产品.一个多层次的模型,多层次的模型.预测 预测 预测 预测

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科学领域:

  • 生物标志物和诊断仪器
  • 计算生物学和生物信息学
  • 神经退行性疾病研究

背景情况:

  • 阿尔茨海默病 (AD) 是老年人痴呆的主要原因.
  • 阿尔茨海默病的神经病理学在症状表现之前几十年就开始了,这凸显了早期检测工具的必要性.
  • 对AD的早期干预策略可以通过及时和准确的查方法来促进.

研究的目的:

  • 开发和评估阿尔茨海默病 (AD) 情绪状态和发病时的年龄的多组预测模型.
  • 确定用于早期AD检测的关键预测特征和信息性数据模式.
  • 评估"AD-by-proxy"案例在增强预测模型中的实用性.

主要方法:

  • 利用基于树的和深度学习算法来训练多组预测模型.
  • 来自英国生物银行的综合基因组,蛋白质组,代谢组和药物使用数据.
  • 采用SHAP分析来确定特征的重要性,并确定关键预测因素.

主要成果:

  • 最好的多原子模型在预测AD时获得了0.87的AUROC.
  • 状纤维酸性蛋白 (GFAP) 和CXCL17蛋白被确定为最强的预测因子,与阿波利波蛋白E (APOE) 的等位基因一起.
  • 纳入"AD-by-proxy"案例并没有显著改善模型性能.

结论:

  • 基因组学和蛋白质组学成为基于AUROC的AD预测最有信息的数据模式.
  • 基于血液的生物标志物GFAP和CXCL17显示出早期,症状前的AD预测的潜力.
  • 开发的多组模型有效地使用omics和EHR数据预测AD和发病时的年龄.