TWAS-GKF:一种新的方法用于因果基因鉴定,用于转录全基因组相关性研究,并使用淘汰推断推断
Anqi Wang1, Peixin Tian1, Yan Dora Zhang1
1Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, 999077, China.
Bioinformatics (Oxford, England)
|August 27, 2024
概括
我们开发了TWAS-GKF,这是一种全转录组关联研究 (TWAS) 的新方法,仅使用总结统计数据识别特征关联基因,确保在没有个人级数据的情况下控制错误发现率 (FDR).
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:
- 全转录组关联研究 (TWAS) 识别与特征及其生物机制相关的基因.
- 目前的TWAS方法难以控制有限样本错误发现率 (FDR),并且需要个体级遗传数据,但通常无法获得.
研究的目的:
- 开发一种新的TWAS方法,保证有限样本的FDR控制.
- 仅使用总结统计数据来实现基因特征关联发现,克服数据可访问性的限制.
主要方法:
- 提出了TWAS-GKF,一种利用Ghostknockoff原则的淘汰推断方法.
- 仅从总结统计数据中生成淘汰变量,消除了对个体级数据的需求.
- 在模拟和现实应用中验证了该方法的FDR控制和性能.
主要成果:
- 在所有测试的场景中,TWAS-GKF展示了强大的有限样本FDR控制.
- 在大脑小脑 (精神分裂症) 和肝脏 (LDL-C) 组织中确定了重要的特征相关基因.
- 大多数已识别的基因使用开放目标验证平台进行了验证.
结论:
- TWAS-GKF提供了一种强大且易于使用的方法来识别特征相关基因.
- 该方法有效地控制了FDR,并利用总结统计数据在遗传关联研究中得到更广泛的应用.
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