ML-MAGES:用于多变量遗传关联分析的机器学习框架,与基因和效果缩小大小的分析
Xiran Liu1, Lorin Crawford1,2, Sohini Ramachandran1
1Brown University, Providence, RI 02906, USA.
bioRxiv : the preprint server for biology
|February 24, 2025
概括
我们开发了ML-MAGES,一种机器学习方法,通过减少效果大小膨胀和同时分析多个特征来改进全基因组关联 (GWA) 研究. ML-MAGES准确地识别了与特征相关的遗传变异,包括跨多个特征共享的遗传架构.
科学领域:
- 遗传学和生物信息学 遗传学和生物信息学
- 统计基因组学 统计基因组学
- 机器学习在生物学中的应用
背景情况:
- 全基因组关联 (GWA) 研究旨在将遗传变异与特征联系起来.
- 关键的挑战包括膨胀效应估计和同时分析多个特征.
研究的目的:
- 为GWA研究引入ML-MAGES,一种计算效率高的机器学习方法.
- 为了解决效果大小通胀问题,并使同时进行多特征分析.
主要方法:
- 利用神经网络缩小GWA效应大小,减轻变量非独立的通胀.
- 采用变异推理来对多个特征的变异关联进行聚类.
- 将神经网络收缩与正规化回归和精细映射进行比较.
主要成果:
- 神经网络收缩在模拟中超越现有的方法来近似真实效应大小.
- 无限混合集群方法有效地区分特征特定的,共享的和虚假的关联.
- ML-MAGES在识别基因水平关联方面表现出高精度和回忆.
结论:
- ML-MAGES提供了一种灵活的,数据驱动的方法来进行多特征遗传关联分析.
- 在英国生物库数据中的应用确定了特征特异和共享的遗传变异.
- 该方法表明潜在的共同遗传架构是复杂特征的基础.
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