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Alzheimer Disease · Journal article
Genetic Epidemiology · September 1, 2026
Encouraging direction, but not yet definitive.
This methodological study extends GhostKnockoffs, a knockoff-based variant selection method, to genome-wide association studies with related individuals by demonstrating robustness when input Z-scores derive from valid generalized linear mixed models. Validation via simulation and meta-analysis of nine European-ancestry studies on Alzheimer's disease suggests practical utility, but does not establish improved clinical outcomes or definitive causal inference compared to standard methods.
Simulation-based methodological validation with observational meta-analysis. Simulation studies and meta-analysis participants from nine European-ancestry genome-wide association studies and whole exome/genome sequencing studies investigating Alzheimer's disease; studies with sample relatedness (e.g., UK Biobank).. Intervention: GhostKnockoffs method integrated with state-of-the-art marginal association tests for variant selection. n = 9. European ancestry cohorts (specific number of centres and countries not stated).
GhostKnockoffs approach is robust to arbitrary relatedness structure when Z-scores derived from valid generalized linear mixed models Method extends to meta-analysis of studies with sample overlap when score test Z-scores are properly calibrated Validity and practical utility demonstrated using simulation studies and meta-analysis of nine European ancestry GWAS and sequencing studies for Alzheimer's disease
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This work provides statisticians and genetic epidemiologists with a validated computational method for identifying potentially causal variants in large, related-sample cohorts. Clinicians and researchers should note this enables more robust variant discovery in studies with family structure or population stratification, though the method itself does not change clinical management or directly validate causal mechanisms.
A methodological study demonstrating that GhostKnockoffs with proper Z-score derivation can identify variants with potentially causal effects in related-sample GWASs, validated by simulation and a nine-study meta-analysis, but without direct clinical outcome comparison.
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This work provides statisticians and genetic epidemiologists with a validated computational method for identifying potentially causal variants in large, related-sample cohorts. Clinicians and researchers should note this enables more robust variant discovery in studies with family structure or population stratification, though the method itself does not change clinical management or directly validate causal mechanisms.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Genome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex traits. Recent studies show that knockoff-based methods can identify variants with unique, potentially causal effects on phenotypes. However, their statistical validity and effectiveness in studies with related individuals, such as the UK Biobank, remain unexplored. In this paper, we extensively evaluate a simple and effective analytical strategy that integrates GhostKnockoffs and state-of-the-art marginal association tests. We show that this approach is robust to arbitrary relatedness structure as long as the input Z-scores are derived from valid generalized linear mixed models. This robustness also extends GhostKnockoffs to other GWASs settings, including meta-analysis of studies with sample overlap when the input score test Z-scores are properly calibrated, and association test statistics beyond score tests in independent sample settings. We demonstrate the method's validity and practical utility 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.
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