Robust inference with GhostKnockoffs in genome-wide association studies

Genome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex traits. Recent studies have demonstrated that for feature selection in GWASs data, in addition to controlling the familywise error rate (FWER), the false discovery rate (FDR) serves as an appealing alternative for detecting small effect loci associated with polygenic traits. However, the presence of correlations among genetic variants makes direct application of usual FDR-controlling procedures to marginal association tests ineffective. The knockoffs-based methods have shown guarantee in FDR control in GWASs, but their statistical validity and effectiveness in studies with related individuals remain unexplored. In this paper, we propose a knockoff-based approach by integrating recently proposed GhostKnockoffs and state-of-the-art marginal association tests. We show that GhostKnockoffs, which only requires GWAS Z-scores as input, is robust to arbitrary relatedness structure as long as the input Z-scores are derived from valid generalized linear mixed models. Therefore, it can be flexibly applied on top of the standard GWASs pipeline that accounts for relatedness to enhance the discovery of small effect loci. This robustness also generalizes GhostKnockoffs to other GWASs settings, such as the meta-analysis of multiple overlapping studies and studies based on association test statistics deviated from score tests. We demonstrate the method's performance using simulation studies and a meta-analysis of nine European ancestral genome-wide association studies and whole exome/genome sequencing studies for the Alzheimer's disease.