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 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 achieves 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.

翻译：识别携带与目标性状相关重要信息的变异体仍然是遗传学研究中的核心课题。然而，在分析EADB-UKBB数据集以识别与阿尔茨海默病（AD）相关的遗传变异时，我们认识到现有的边际关联检验和使用现有敲除过滤器的条件独立性检验均存在功效损失或信息量不足的问题，尤其是在变异体间存在强相关性时。为应对这些局限性，我们提出了一种新的特征对组（FVG）过滤器，该过滤器利用组敲除技术，在从一组强相关特征中识别重要特征时实现了功效与精确度之间的平衡。在广泛的模拟研究中，FVG过滤器控制了错误发现的期望比例，并在较小的捕获集中识别重要特征，且未造成大的功效损失。将所提方法应用于EADB-UKBB数据集，我们从89个基因座（类似于最强大的组敲除过滤器）中发现了重要变异体，其捕获集规模显著更小、纯度更高，并验证了我们发现的生物学信息量。