通过使用异质知识图,提高未经研究的复杂疾病中的变异类型关联发现
Ananya Rajagopalan1, Tram Anh Nguyen1, Lindsay A Guare1
1Genomics and Computational Biology Graduate Program.
medRxiv : the preprint server for health sciences
|September 2, 2025
概括
我们开发了DRIVE-KG, 一个综合多种病理学数据的知识图表, 以揭示新型SNP与子宫内膜异位症的联系, 并改善对这种研究不足的女性健康状况的患者分类.
科学领域:
- 基因组学和生物信息学
- 计算生物学
- 妇女健康研究
背景情况:
- 子宫内膜异位症是一种普遍的女性健康状况,影响10%的生育年龄女性.
- 有限的子宫内膜异位症遗传特征存在,目前GWAS仅解释了其遗传率的11%.
- 由于稀缺性和高维度,多种经济学数据的整合具有挑战性.
研究的目的:
- 介绍DRIVE-KG,一个用于整合多种经济学数据的新型异质知识图.
- 确定新的单核酸多态 (SNP) - 子宫内膜异位症关联.
- 通过基于图表的机器学习来改善子宫内膜异位症的临床预测.
主要方法:
- 构建了一个异质图 (DRIVE-KG),整合了dbSNP,NCBI人类基因,Omics Pred,GTEx和开放目标的数据.
- 使用链接预测模型来识别SNP-表型关联.
- 使用图形卷积网络 (GCN) 来对患者进行子宫内膜异位症分类,使用来自1441名参与者的数据.
主要成果:
- 发现了66个高可信度,以前未报告的SNP- 子宫内膜异位症关联.
- 鉴定了新型变体与肥胖,脂质代谢和抑郁症之间的联系,与新兴的子宫内膜异位症假说保持一致.
- 使用GCN获得了0.738的AUPRC,超过了遗传风险得分 (0.679).
结论:
- 通过DRIVE-KG进行多样化的多组数据集成对于未经研究的疾病的发现和临床预测具有价值.
- 通过DRIVE-KG,便于识别子宫内膜异位症的新型遗传关联.
- 与传统方法相比,基于图的方法可以更好地预测子宫内膜异位症的临床情况.
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