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How do stochastic processes and genetic threshold effects explain incomplete penetrance and inform causal disease mechanisms?

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Jenkins, Dagan·2024·Philosophical Transactions of the Royal Society B: Biological Sciences
DOI 10.1098/rstb.2023.0045PMC PMC10909503
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Abstract

Incomplete penetrance is the rule rather than the exception in Mendelian disease. In syndromic monogenic disorders, phenotypic variability can be viewed as the combination of incomplete penetrance for each of multiple independent clinical features. Within genetically identical individuals, such as isogenic model organisms, stochastic variation at molecular and cellular levels is the primary cause of incomplete penetrance according to a genetic threshold model. By defining specific probability distributions of causal biological readouts and genetic liability values, stochasticity and incomplete penetrance provide information about threshold values in biological systems. Ascertainment of threshold values has been achieved by simultaneous scoring of relatively simple phenotypes and quantitation of molecular readouts at the level of single cells. However, this is much more challenging for complex morphological phenotypes using experimental and reductionist approaches alone, where cause and effect are separated temporally and across multiple biological modes and scales. Here I consider how causal inference, which integrates observational data with high confidence causal models, might be used to quantify the relative contribution of different sources of stochastic variation to phenotypic diversity. Collectively, these approaches could inform disease mechanisms, improve predictions of clinical outcomes and prioritize gene therapy targets across modes and scales of gene function. This article is part of a discussion meeting issue ‘Causes and consequences of stochastic processes in development and disease’.

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  • Stochasticity rules all living cells through fluctuations in gene expression, molecular interactions, and metabolic fluxes, even systems governed by deterministic laws.

    “Actually, stochasticity rules all living cells through fluctuations in gene expression, molecular interactions, and metabolic fluxes, even systems governed by deterministic laws, such as monogenic genetics [131,132,133,134].”

    From Revisiting the Pathogenesis of X-Linked Adrenoleukodystrophy (2025) · cites this paper as [131]

    The Introduction (page unavailable) describes biological systems as inherently noisy at microscopic scales and identifies transcriptional and translational bursts as sources of single-cell variation. The paper also discusses variation in protein–protein interactions, but the cited passages do not establish fluctuations in metabolic fluxes or the claim that stochasticity rules all living cells, including systems governed by deterministic laws. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • A large part of phenotype variability across patients with the same ABCD1 genotype may be due to stochasticity.

    “Beyond identifiable causes, a large part of phenotype variability across patients with the same ABCD1 genotype may “simply” be due to stochasticity, a factor hitherto considered “noise” by Cartesian researchers.”

    From Revisiting the Pathogenesis of X-Linked Adrenoleukodystrophy (2025) · cites this paper as [131]

    The ‘What is phenotypic variability?’ section says stochastic processes contribute to clinical variability in humans with genetic diseases. It calls them the major source of variation only in genetically identical experimental organisms under controlled conditions; it does not establish that they account for a large part of variability among patients with the same ABCD1 genotype. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • The study could not correctly assess the influence of environmental factors and modifier genes on penetrance of APOE mutations.

    “Meanwhile, it also prevents the correct and comprehensive assessment of the influence of environmental factors and modifier genes on the penetrance of APOE mutations9,18, 19, 20, 21”

    From The First Case of Lipoprotein Glomerulopathy in a Patient With Male Infertility (2026) · cites this paper as [18]

    In ‘Incomplete penetrance and genetic threshold effects’ (page unavailable), the paper says that not all genetic modifiers can be identified and that environmental factors are too complicated to fully ascertain. This supports the general difficulty of comprehensive assessment, but the paper does not assess APOE mutations or report that a particular study could not assess their penetrance. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Authors
Jenkins, D.
Affiliations
University College London (GB)
Corresponding author
Jenkins, Dagan
ORCID
Jenkins, Dagan
Venue
Philosophical Transactions of the Royal Society B: Biological Sciences
Publisher
The Royal Society
Published
2024-03-04
Volume
379
Issue
1900
Pages
20230045-20230045
ISSN
  • 0962-8436 (ISSN-L)
  • 1471-2970
DOI
10.1098/rstb.2023.0045
PMC
PMC10909503
OpenAlex
W4392343498
Cited by
6
References
64
Citations by year
2026: 2 · 2025: 2 · 2024: 2
FWCI
0.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Philosophical Transactions of the Royal Society B Biological Sciences (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
Funders
Wellcome Trust (210585/Z/18/Z)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
Keywords
incomplete penetrance, randomness, monogenic disorders, phenotypic variability, genetic susceptibility, causal inference, causal models, disease mechanisms, gene therapy target, morphological phenotypes
MeSH terms
Biological Variation, Population, Humans, Phenotype, Stochastic Processes, Causality, Penetrance

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