Pinpointing Important Genetic Variants via A Feature-level Filter and Group Knockoffs

Identifying variants that carry substantial information on the trait of interest remains a core topic in genetic studies. In analyzing 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 knockoffs suffer either power loss or lack of informativeness, especially when strong correlations exist among variants. To address the limitations of existing knockoff filters, we propose a new feature-versus-group (FVG) filter that is more powerful and precise 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 with enhanced power and greater precision. Applying the proposed method to the EADB-UKBB dataset, we discover important variants from 84 loci (same as the most powerful group knockoff filter) with catching sets of substantially smaller size and higher purity.