平衡图表审查的努力和PRS预测准确性的收益:一个经验研究
Yuqing Lei1, Adam Christian Naj2, Hua Xu3
1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.
偏差减小方法只在表型错误分类高时,才能提高多基因风险评分 (PRS) 预测准确度. 最优的验证集大小平衡了图表审查的努力与PRS准确度的增长.
科学领域:
- 遗传学 遗传学 是一个
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 表型错误分类可以降低多基因风险评分 (PRS) 预测模型的准确性.
- 使用图表审查的偏差减少方法已被提出,但其对PRS预测准确性的影响尚不清楚.
研究的目的:
- 使用偏差减少方法评估模拟PRS模型的性能.
- 确定用于表型验证的图表审查的最佳数量.
主要方法:
- 使用阿尔茨海默氏病遗传学联盟 (ADGC) 的基因型和表型数据模拟的表型.
- 在各种相关性结构下引入了差异性和非差异性表型错误.
- 在不同的错误分类率和验证集大小中评估PRS模型.
主要成果:
- 减少偏差并没有普遍提高PRS预测准确度.
- 在具有较小验证集的低错误分类场景中,不依据模型的表现比易出错的模型更差.
- 偏差减少的有效性取决于错误分类率和验证集大小;更高的错误分类需要更小的验证集进行改进.
结论:
- 选择合适的验证集大小对于平衡图表审查工作和PRS预测准确性收益至关重要.
- 为各种敏感度和特异性组合的验证规划提供指导.
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