Refining genetic discoveries of group knockoffs via a feature-level filter
针对遗传变异间强相关导致统计功效不足的问题，提出特征-分组（FVG）过滤器，在控制错误发现率的同时提高识别精度，并在阿尔茨海默病数据中验证了其有效性。
Abstract Identifying variants that carry substantial information on the trait of interest remains a core topic in genetic studies. In analysing the EADB-UKBB dataset to identify genetic variants associated with Alzheimer’s disease (AD), however, we recognize that both existing marginal association tests and conditional independence tests using existing knockoff filters suffer either power loss or lack of informativeness, especially when strong correlations exist among variants. To address these limitations, we propose a new feature-versus-group (FVG) filter that seeks balance between the power and precision in identifying important features from a set of strongly correlated features using group knockoffs. In extensive simulation studies, the FVG filter controls the expected proportion of false discoveries and identifies important features in smaller catching sets without large power loss. Applying the proposed method to the EADB-UKBB dataset, we discover important variants from 89 loci (similar to the most powerful group knockoff filter) with catching sets of substantially smaller size and higher purity and verify the biological informativeness of our discoveries.