Biometrics. 2024 Jul 1;80(3):ujae082. doi: 10.1093/biomtc/ujae082.
ABSTRACT
Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer’s disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.
PMID:39222026 | DOI:10.1093/biomtc/ujae082
