整合功能注释与双层连续收缩,用于多基因风险预测
Yongwen Zhuang1, Na Yeon Kim2, Lars G Fritsche1
1University of Michigan, Ann Arbor, USA.
BMC bioinformatics
|February 9, 2024
概括
我们开发了PRSbils,这是一种多基因风险评分 (PRS) 的新方法,通过结合功能遗传注释和双层收缩来提高预测准确性. 这种方法通过在变体和注释层面考虑稀疏效应来提高遗传风险预测.
科学领域:
- 遗传学 遗传学 是一个
- 统计遗传学 统计遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 功能性注释可以提高多基因风险评分 (PRS) 预测性能.
- 贝叶斯缩小方法通过考虑稀疏的因果变异来增强PRS.
- 有限方法整合了功能注释和效果尺寸缩小.
研究的目的:
- 提出PRSbils,一种使用双层连续收缩先验的新型PRS方法.
- 在变体和功能注释层面上适应不同的遗传架构.
- 通过整合功能注释信息来改善遗传风险预测.
主要方法:
- 开发了PRSbils,一种具有双层连续收缩的PRS方法.
- 使用的功能注释信息 (ANNOVAR,KEGG通路).
- 将该方法应用于英国生物银行,MGI和KoGES数据集中的二进制和定量特征.
主要成果:
- 在具有可变群体遗传性的模拟中,PRSbils比PRS-CS实现了更高的预测性能 (例如,比AUC高8.0%).
- 经过重叠的注释组,证明了AUC的改善 (平均高于6.4%).
- 展示了PRSbils在现实数据中提高预测性能的潜力.
结论:
- PRSbils有效地使用双层收缩结合重叠和非重叠的注释.
- 该方法提高了遗传风险预测性能.
- 软件可以在 https://github.com/styvon/PRSbils.上公开使用.
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