Genetic Modulators of Disease Penetrance

In human genetics, penetrance describes the probability that a genotype manifests as a phenotype within a defined clinical window, yet its biological determinants remain poorly understood. Advances in sequencing technologies, statistical frameworks, and biobank-scale resources now enable systematic discovery of penetrance-modifying coding, regulatory, and structural variants spanning the allele frequency-effect size spectrum. Using metabolic dysfunction-associated steatotic liver disease, chronic kidney disease, and Alzheimer's disease as exemplars, we illustrate how rare, high-impact mutations, ancestry-enriched intermediate-frequency alleles, and common polygenic variation act through distinct molecular and cellular mechanisms to shape disease liability. We highlight gaps in integrating different variant types, modeling context-dependent effects, and building frameworks to translate genetic findings into individualized risk assessment. A unified understanding of penetrance promises to improve genotype-to-phenotype inference, refine patient risk stratification, and accelerate genetically informed therapeutic strategies.

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Publication Details

Journal
Annual Review of Biomedical Data Science
Published
2026-04-16
DOI
https://doi.org/10.1146/annurev-biodatasci-103123-094547
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
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article

Genetic Modulators of Disease Penetrance

Yunjun Kang, Theodore G. Drivas
Annual Review of Biomedical Data Science
Genetic Associations and Epidemiology
article

Genetic Modulators of Disease Penetrance

Yunjun Kang, Theodore G. Drivas
article en

Abstract

In human genetics, penetrance describes the probability that a genotype manifests as a phenotype within a defined clinical window, yet its biological determinants remain poorly understood. Advances in sequencing technologies, statistical frameworks, and biobank-scale resources now enable systematic discovery of penetrance-modifying coding, regulatory, and structural variants spanning the allele frequency-effect size spectrum. Using metabolic dysfunction-associated steatotic liver disease, chronic kidney disease, and Alzheimer's disease as exemplars, we illustrate how rare, high-impact mutations, ancestry-enriched intermediate-frequency alleles, and common polygenic variation act through distinct molecular and cellular mechanisms to shape disease liability. We highlight gaps in integrating different variant types, modeling context-dependent effects, and building frameworks to translate genetic findings into individualized risk assessment. A unified understanding of penetrance promises to improve genotype-to-phenotype inference, refine patient risk stratification, and accelerate genetically informed therapeutic strategies.

Annual Review of Biomedical Data Science
Translational Therapeutics (United States) (US), University of Pennsylvania (US)
Openalex Percentile: Top 20%
Genetic Associations and Epidemiology
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