Liang Y et al., The American Journal of Human Genetics. 113 ( - TWAS using genetically predicted expression exhibit polygenicity-driven inflation that increases with GWAS sample size and heritability; a gene-specific variance-control correction yields calibrated p values.

Study Highlights:
The authors evaluated TWAS and related xWAS using simulated polygenic null traits and UK Biobank genotypes with predicted mediators including gene expression, metabolites, and brain features. They combined large-scale simulations, theoretical derivations, and empirical regression of mean Z2 on N*h2δ to estimate a gene-specific inflation slope Φ and applied corrections with S‑PrediXcan/PrediXcan across 110 GWAS traits. They show analytically and empirically that Var(Z) ≈ 1 + N*h2δ*Φ, observe a cohort-level slope around 4.2×10^-5, and demonstrate that dividing Z by sqrt(1+N*h2δ*Φ) restores calibration. Applying the variance-control correction yields well-calibrated p values, reduces false positives for highly polygenic traits, and improves precision with minimal loss of power.

Conclusion:
A gene-specific variance-control correction based on an empirically estimated inflation slope Φ corrects polygenicity-driven inflation in TWAS/xWAS and yields calibrated false-positive rates in simulations and real GWAS analyses.

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Reference:
Liang Y, Nyasimi F, Im HK. A gene-specific variance-control approach corrects polygenicity-driven inflation observed in transcriptome-wide association studies. The American Journal of Human Genetics. 113 (2026) 276-290. https://doi.org/10.1016/j.ajhg.2025.12.014

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) - https://creativecommons.org/licenses/by/4.0/

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