基因风险评估基于关联和预测研究
Nicole Cathlene N Astrologo1,2, Joverlyn D Gaudillo3,4,5, Jason R Albia6,7,8
1Data Analytics Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines.
个性化遗传风险模型改善了对乙型肝炎表面抗原 (HBsAg) 血清清除的预测. 将全基因组关联研究 (GWAS) 和机器学习 (ML) 生物标记物结合起来,实现了90%的准确性,有助于理解表型出现和肝细胞癌风险.
科学领域:
- 遗传学 是一个遗传学.
- 生物标志物发现发现
- 个性化医疗是个性化的医疗.
背景情况:
- 遗传因素影响疾病风险和进展.
- 全基因组关联研究 (GWAS) 确定了人口层面的遗传关联.
- 个体级风险预测需要像机器学习 (ML) 这样的补充方法.
研究的目的:
- 开发和评估个性化的风险评估模型,以预测乙型肝炎表面抗原 (HBsAg) 血清清除.
- 为了比较GWAS识别和ML识别生物标志物的预测能力.
- 评估结合GWAS和ML生物标志物的协同效应.
主要方法:
- 利用来自200名韩国患者的单核酸多态性 (SNP) 数据 (100 HBsAg血清,100 高HBsAg).
- 使用GWAS识别的候选生物标志物和ML (随机森林) 识别的生物标志物开发风险模型.
- 用单个和组合的生物标志物集评估模型准确性.
主要成果:
- 使用相关生物标志物模型的准确性高于使用所有特征的模型 (64%).
- GWAS生物标记器实现了82% (52个标记) 和71% (3个标记) 的准确性.
- ML生物标志物实现了80%的准确性 (150个标志物).
- 结合GWAS和ML生物标志物,预测准确度提高到90%.
结论:
- 相关的生物标志物显著影响表型的出现.
- 机器学习作为GWAS的有价值的辅助分析,用于增强的预测建模.
- 通过ML识别的生物标志物与肝细胞癌 (HCC) 有关,即使在HBsAg阴性病例中也是如此.
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