Episode 86

July 25, 2025

00:19:27

️ 86: Variable Penetrance in Monogenic Traits — How Genetic Background Modulates Disease Severity

Hosted by

Gustavo B Barra
️ 86: Variable Penetrance in Monogenic Traits — How Genetic Background Modulates Disease Severity
Base by Base
️ 86: Variable Penetrance in Monogenic Traits — How Genetic Background Modulates Disease Severity

Jul 25 2025 | 00:19:27

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Show Notes

️ Episode 86: Variable Penetrance in Monogenic Traits — How Genetic Background Modulates Disease Severity

In this episode of PaperCast Base by Base, we explore a landmark study that unravels why carriers of the same pathogenic variant often present with very different clinical outcomes, providing new insights into gene–disease association, variant interpretation, and the interplay between rare and common genetic effects.

Study Highlights:

The researchers analyzed exome and clinical data from large biobank cohorts to investigate incomplete penetrance and disease variability in monogenic metabolic traits. They developed a framework combining variant-level effect size estimation through a protein language model with polygenic risk scoring and a novel method to quantify marginal epistasis. Their findings show that missense variants in the same gene have different phenotypic impacts, that polygenic background significantly modulates carrier traits, and that genetic interactions can amplify or buffer the effect of pathogenic variants, sometimes exceeding the primary variant effect itself. These insights reveal a multidimensional genetic architecture underlying rare metabolic disorders.

Conclusion:

This work paves the way for more precise prediction of disease risk and progression in clinical genomics by integrating rare and common variant effects with functional modeling.

Reference:

Wei, A., Border, R., Fu, B., et al. (2025). Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions. Nature Communications, 16, 5223. https://doi.org/10.1038/s41467-025-60339-7

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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