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Refining genetic discoveries of group knockoffs via a feature-level filter

Jiaqi Gu et al.

Journal of the Royal Statistical Society. Series C: Applied Statistics2026https://doi.org/10.1093/jrsssc/qlag006article
AJG 3
Weight
0.50

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.

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https://doi.org/https://doi.org/10.1093/jrsssc/qlag006

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@article{jiaqi2026,
  title        = {{Refining genetic discoveries of group knockoffs via a feature-level filter}},
  author       = {Jiaqi Gu et al.},
  journal      = {Journal of the Royal Statistical Society. Series C: Applied Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/jrsssc/qlag006},
}

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Refining genetic discoveries of group knockoffs via a feature-level filter

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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