风险因素多基因风险评分与疾病多基因风险评分的整合用于疾病预测
Hyein Jung1, Hae-Un Jung1, Eun Ju Baek2
1Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea.
Communications biology
|February 13, 2024
概括
将风险因子多原风险评分 (RFPRS) 与疾病多原风险评分 (PRS) 整合起来可以改善疾病预测. 新的RFPRS疾病PRS模型RFDiseasemetaPRS提高了许多疾病的预测准确性和风险分类.
科学领域:
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 多基因风险评分 (PRS) 对于评估对疾病的遗传易感性是有价值的.
- 之前的研究还没有充分利用风险因子PRS (RFPRS) 来优化疾病预测模型.
- 整合多样化的遗传风险信息可能会提高预测能力.
研究的目的:
- 研究将与疾病相关的RFPRS与已确定的疾病PRS结合起来,以提高疾病预测的潜力.
- 开发和评估一种新的联合PRS,称为RFDiseasemetaPRS,以提高预测性能.
主要方法:
- 构建了112个RFPRS并分析了它们与英国生物库数据集中的700种疾病的关联.
- 在247种疾病和109种RFPRS之间确定了6157个显著的关联.
- 通过将显著的RFPRS与70种遗传性疾病的疾病PRS集成,开发了RFDiseasemetaPRS,并将其性能与单独的疾病PRS进行了比较.
主要成果:
- 对于70种疾病中的31种,RFDiseasemetaPRS在Nagelkerke的伪R2,每1个SD的几率比 (OR),净重新分类改进 (NRI) 和R2的变异方面表现出卓越的表现.
- 风险分类分析显示,RFDiseasemetaPRS在R2,NRI和OR中优于疾病PRS,在31种疾病中排名前10%的风险组与剩余90%的风险组相比.
结论:
- 在RFDiseasemetaPRS中将RFPRS与疾病PRS集成到RFDiseasemetaPRS中,显著提高了疾病预测的准确性和风险分层.
- RFDiseasemetaPRS为个性化医疗保健提供了一个有前途的工具,可以根据综合遗传风险评估制定定制的预防策略.
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