稀有多基因风险得分推断使用尖和 LASSO
Junyi Song1, Shadi Zabad1, Archer Yang2
1School of Computer Science, McGill University, Montréal, QC H3A 0G4, Canada.
Bioinformatics (Oxford, England)
|October 17, 2025
概括
我们开发了SSLPRS,一种使用遗传数据预测疾病风险的新方法. 它提高了准确性和变量选择,特别是在稀疏的遗传结构中,在多基因风险评分方法中取得了重大进展.
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.
- 统计遗传学 统计遗传学
背景情况:
- 大规模的生物库为复杂特征的遗传研究提供了丰富的数据.
- 高维基因组数据为疾病风险预测带来了挑战,特别是有限的样本大小.
- 现有的多基因风险评分 (PRS) 方法在可扩展性或系数收缩方面存在局限性.
研究的目的:
- 为了引入SSLPRS,一种使用Spike-and-Slab LASSO (SSL) 的新型PRS方法.
- 使用全基因组协会研究 (GWAS) 总结统计数据开发一个可扩展的PRS推断算法.
- 与现有方法相比,评估SSLPRS的性能.
主要方法:
- 为SSLPRS开发了一个坐标上升推断算法,该算法运行在GWAS总结统计上.
- 在PRS推断之前使用了尖和板 LASSO (SSL).
- 通过对英国生物银行定量表型的模拟和分析验证了该方法.
主要成果:
- SSLPRS 显示出具有竞争力的预测准确性和优越的变量选择性能,特别是在稀疏的遗传架构中.
- 在模拟中实现了超过50%的积极预测值的改善.
- 在真实表型分析中选择的变异被丰富为显著的基因组注释,并显示改进的复制率.
结论:
- 从基因型数据来预测疾病风险,SSLPRS提供了一种强大而可扩展的方法.
- 该方法弥合了稀疏贝叶斯先验和处罚回归的理论框架.
- SSLPRS提供了增强的变量选择和预测准确性,推进了多基因风险评分方法.
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