Handbook of Human Genetic Linkage.

Edwards, A. W. F.; Terwilliger, Joseph D.; Ott, Jürg

doi:10.2307/2533082

Cited by 185 publications

(3 citation statements)

References 9 publications

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“…In genetics, the stochastic nature of chromosomal segregation and crossing over provides a form of randomization by which causation can be inferred. Linkage analysis has been used to identify thousands of Mendelian disease genes [1][2][3][4]51,52]. The premise for this analytical approach is to first establish the mode of inheritance for a particular trait (e.g.…”

Section: Comparing Experimental Approaches To Threshold Determination...mentioning

confidence: 99%

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

Jenkins

2024

Phil. Trans. R. Soc. B

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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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Section: Comparing Experimental Approaches To Threshold Determination...mentioning

confidence: 99%

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

Jenkins

2024

Phil. Trans. R. Soc. B

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show abstract

“…In genetics, the stochastic nature of chromosomal segregation and crossing over provides a form of randomisation by which causation can be inferred. Linkage analysis has been used to identify thousands of Mendelian disease genes [1][2][3][4]45,46]. The premise for this analytical approach is to first establish the mode of inheritance for a particular trait (e.g.…”

Section: Comparing Experimental Approaches To Threshold Determination...mentioning

confidence: 99%

“…to identify thousands of Mendelian disease genes [1][2][3][4]45,46]. The premise for this analytical approach is to first establish the mode of inheritance for a particular trait (e.g.…”

Section: Comparing Experimental Approaches To Threshold Determination...mentioning

confidence: 99%

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

Jenkins

2023

Preprint

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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 we 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 prioritise gene therapy targets across modes and scales of gene function.

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Novel Mutation in the SPG3A Gene in an African American Family With an Early Onset of Hereditary Spastic Paraplegia

et al. 2004

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Handbook of Human Genetic Linkage.

Cited by 185 publications

References 9 publications

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

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

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

Novel Mutation in the SPG3A Gene in an African American Family With an Early Onset of Hereditary Spastic Paraplegia

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