解决因多基因风险评分中的样本重叠导致的过度拟合偏差
Seokho Jeong1,2, Manu Shivakumar2, Sang-Hyuk Jung2,3
1Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
概括
在阿尔茨海默病 (AD) 研究中的样本重叠使多基因风险得分 (PRS) 膨胀. 我们开发了重叠调整的PRS (OA-PRS) 来纠正这种偏差,确保更准确的PRS估计,并防止在AD遗传研究中过度匹配.
科学领域:
- 遗传学 是一个遗传学.
- 神经科学是一个神经科学.
- 生物统计学 生物统计学
背景情况:
- 对阿尔茨海默病 (AD) 的多基因风险评分 (PRS) 被广泛使用,但经常忽视像国际阿尔茨海默病基因组学项目 (IGAP) 和阿尔茨海默病神经成像计划 (ADNI) 等主要队列之间的样本重叠.
- 这种样本重叠可以引入显著的过拟合偏差,膨胀AD PRS的性能指标.
- 准确的PRS对于理解AD的遗传倾向至关重要.
研究的目的:
- 开发和验证一种用于调整阿尔茨海默病多基因风险评分 (PRS) 样本重叠的方法.
- 为了减轻来自大型重叠遗传数据集的PRS中的过度匹配偏差.
- 提高PRS在阿尔茨海默病研究中的准确性和可靠性.
主要方法:
- 开发了一种重叠调整的PRS (OA-PRS) 方法,以纠正遗传数据集中的样本重叠.
- 在模拟数据上测试OA-PRS,培训,测试和重叠样本的比例各不相同.
- 将OA-PRS应用于IGAP和ADNI数据集,并使用视觉诊断验证结果.
主要成果:
- 在模拟和现实数据集 (IGAP和ADNI) 中有效调整OA-PRS以考虑样本重叠.
- 最初的IGAP PRS在重叠样本上显示了膨胀的性能 (AUROC:0.915),OA-PRS将其纠正为0.726,与非重叠样本的性能 (0.712) 保持一致.
- 视觉诊断证实了OA-PRS.成功地缓解了过拟合偏差.
结论:
- 对于重叠的ADNI样本,OA-PRS成功地调整了基于IGAP的PRS,使得数据集的完整利用成为可能,而不存在过度匹配的风险.
- 开发的OA-PRS方法有效地减轻了阿尔茨海默病遗传研究中的样本重叠引起的过拟合偏差.
- 调整后的PRS证明了与临床特征相关的关联研究的功率有所提高,从而提高了它们在阿尔茨海默病研究中的有用性.
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