  Genome-wide association studies (GWASs) have been extensively adopted to
depict the underlying genetic architecture of complex diseases. Motivated by
GWASs' limitations in identifying small effect loci to understand complex
traits' polygenicity and fine-mapping putative causal variants from proxy ones,
we propose a knockoff-based method which only requires summary statistics from
GWASs and demonstrate its validity in the presence of relatedness. We show that
GhostKnockoffs inference is robust to its input Z-scores as long as they are
from valid marginal association tests and their correlations are consistent
with the correlations among the corresponding genetic variants. The property
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 GhostKnockoffs'
performance using empirical simulation and a meta-analysis of nine European
ancestral genome-wide association studies and whole exome/genome sequencing
studies. Both results demonstrate that GhostKnockoffs identify more putative
causal variants with weak genotype-phenotype associations that are missed by
conventional GWASs.
