Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA.
Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA.
Nat Commun. 2021 May 25;12(1):3152. doi: 10.1038/s41467-021-22889-4.
The analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.
由于非编码区域中存在大量罕见变异，以及缺乏用于检测的自然单位，全基因组测序研究的分析具有挑战性。我们提出了一种统计方法，基于最近开发的 knockoff 框架，用于检测和定位全基因组测序研究中的罕见和常见风险变异。它可以（1）优先考虑由于连锁不平衡而导致的因果变异，从而提高可解释性；（2）有助于区分罕见变异引起的信号和附近显著常见变异的阴影效应；（3）整合多个 knockoffs 以提高功效、稳定性和可重复性；以及（4）灵活地结合最先进和未来的关联测试，以实现这里提出的好处。在应用于阿尔茨海默病测序项目（ADSP）的全基因组测序数据和 NHLBI Trans-Omics for Precision Medicine（TOPMed）计划的 COPDGene 样本时，我们表明，与传统关联测试相比，我们的方法可以导致更多的发现。
