Lee Seunggeun, Kim Sehee, Fuchsberger Christian
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Genet Epidemiol. 2017 Nov;41(7):610-619. doi: 10.1002/gepi.22057. Epub 2017 Jun 28.
Due to the drop in sequencing cost, the number of sequenced genomes is increasing rapidly. To improve power of rare-variant tests, these sequenced samples could be used as external control samples in addition to control samples from the study itself. However, when using external controls, possible batch effects due to the use of different sequencing platforms or genotype calling pipelines can dramatically increase type I error rates. To address this, we propose novel summary statistics based single and gene- or region-based rare-variant tests that allow the integration of external controls while controlling for type I error. Our approach is based on the insight that batch effects on a given variant can be assessed by comparing odds ratio estimates using internal controls only vs. using combined control samples of internal and external controls. From simulation experiments and the analysis of data from age-related macular degeneration and type 2 diabetes studies, we demonstrate that our method can substantially improve power while controlling for type I error rate.
由于测序成本的下降，已测序基因组的数量正在迅速增加。为了提高罕见变异检测的效能，除了来自研究本身的对照样本外，这些已测序样本还可作为外部对照样本。然而，使用外部对照时，由于使用不同的测序平台或基因分型流程可能产生的批次效应会显著增加I型错误率。为了解决这个问题，我们提出了基于新颖汇总统计量的单基因或基于区域的罕见变异检测方法，该方法允许在控制I型错误的同时整合外部对照。我们的方法基于这样一种见解，即通过比较仅使用内部对照与使用内部和外部对照的组合对照样本的比值比估计值，可以评估给定变异上的批次效应。通过模拟实验以及对年龄相关性黄斑变性和2型糖尿病研究数据的分析，我们证明我们的方法在控制I型错误率的同时可以显著提高效能。
