Disentangling variant effect size from incomplete penetrance in liability

Ascertain whether, for a given genetic variant or gene implicated in human disease, the observed impact on individual liability reflects a genuinely modest effect size or a stronger effect that is not consistently expressed due to incomplete penetrance.

Background

Incomplete penetrance confounds interpretation of variant effects across genetic classes by masking whether observed associations arise from intrinsically modest effects or stronger effects expressed inconsistently due to modifiers such as genetic background, environment, or developmental timing.

The paper highlights a reframing of penetrance as a continuous trait using machine-learning predictions from EHR-linked biobank data to identify modifiers that buffer risk, but this approach has not yet been applied to risk gene discovery. Clarifying the distinction between effect size and penetrance is crucial for accurate risk gene mapping and for prioritizing variants whose impact is modulated by context.