心脏代谢性疾病进展的遗传学
Johanne M Justesen1,2, Guhan Venkataraman1, Yosuke Tanigawa1
1Department of Biomedical Data Science, Stanford University, CA, United States.
medRxiv : the preprint server for health sciences
|February 20, 2025
概括
我们开发了snpnet-Cox来分析影响心脏代谢疾病进展的遗传因素. 该方法识别了新的多基因危险评分 (PHS),可以预测疾病的进展,有助于个性化风险分层和干预策略.
科学领域:
- 遗传学 遗传学 是一个
- 心血管疾病 心血管疾病
- 代谢疾病 代谢疾病
背景情况:
- 全基因组关联研究 (GWAS) 揭示了心脏代谢疾病的遗传学.
- 疾病进展的遗传学仍未得到充分研究,尽管有临床影响.
- 疾病的进展是由诊断后的次要事件的时间来定义的.
研究的目的:
- 为了研究心脏代谢疾病进展的遗传结构.
- 应用一种新的计算方法进行大规模的时间到事件遗传分析.
主要方法:
- 采用了snpnet-Cox,这是一种在高维数据中同时进行变量选择和估计的方法.
- 在时间到事件框架中应用考克斯回归来计算英国生物库数据中的多基因危险分数 (PHS).
- 从初始诊断到随后的并发症或手术的分析时间.
主要成果:
- 确定了十种新的PHS,可以显著预测心脏代谢疾病的进展.
- 表明PHS预测了从高脂血症到冠状动脉旁路移植 (CABG) 手术的时间 (HR=1.3,p=4.5×10−9).
- 发现了一种与预防疾病进展相关的变种 (rs11041816).
结论:
- snpnet-Cox提供了一个计算效率高的工具,用于对时间到事件数据的基因分析.
- 计算机PHS可以根据疾病进展轨迹对个体进行分层,从而实现有针对性的干预.
- 十个已识别的PHS为心脏代谢疾病进展提供了宝贵的见解.
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