CCAFE:从GWAS总结统计数据中估计病例和控制等位基因频率
Hayley R Stoneman1,2, Adelle Price1,3, Christopher R Gignoux1,2,4
1Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
bioRxiv : the preprint server for biology
|November 18, 2024
概括
研究人员开发了方法,从遗传总结统计数据中推导出病例和控制等位基因频率 (AF). 这些框架提高了全基因组关联研究 (GWAS) 数据对后期分析的有用性.
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:
- 来自遗传学研究的总结统计数据是有价值的,但往往缺乏病例和控制等位基因频率 (AF).
- 对全基因组关联研究 (GWAS) 的后期分析经常需要这些特定的AF,从而限制了数据的重用性.
研究的目的:
- 提出两种新的框架,用于从现有的GWAS总结统计数据中导出病例和对照AF.
- 提高公开可用的遗传数据的实用性和可重复使用性.
主要方法:
- 从GWAS总结统计数据中开发了两个框架,使用赔率比率,样本大小和总AF或标准误差 (SE).
- 通过模拟和现实世界遗传数据分析验证的方法.
- 纳入使用gnomAD AFs进行调整,以减轻从SE获得AFs时的偏差.
主要成果:
- 使用总AF的衍生方法在各种遗传环境中显示出高精度.
- 使用SE的导数显示,当共变量存在时,常见变异AFs (>0.3) 的低估值.
- 通过gnomAD AF调整,有效地减少了基于SE的衍生结果的偏差.
结论:
- 使用总AF估计案例和对照AF是非常准确和首选的.
- 从SE进行估计具有更广泛的适用性,因为SE可以从通常报告的p值和β估计中得出.
- 开发的方法和附带的R包CCAFE扩大了GWAS总结统计的实用性.
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