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  4. Model for de novo mutation propagation depending on paternal age at conception and associated neurological disorders [version 1; peer review: 1 not approved]
 
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Model for de novo mutation propagation depending on paternal age at conception and associated neurological disorders [version 1; peer review: 1 not approved]

Publikationstyp
Journal Article
Date Issued
2018
Sprache
English
Author(s)
Lawen, Johannes  
Wang, Ena  
Institut
Systemverfahrenstechnik V-4  
TORE-URI
http://hdl.handle.net/11420/11010
Journal
F1000Research  
Volume
7
Article Number
358
Citation
F1000Research 7: 358 (2018)
Publisher DOI
10.12688/F1000RESEARCH.13619.1
Scopus ID
2-s2.0-85079040775
This paper presents a computational model for simulating the propagation of de novo mutations and paternal age effects in populations. The model uses data for paternal de novo mutation rates depending on age and demographic data such as age distributions, birth distributions versus age, varying life expectancy, and correlations with fertility. The number of paternal de novo mutations in children increases with the paternal age at conception. This might be of interest considering that the average paternal age has risen significantly in many societies throughout the last century. The model introduced below can superimpose and extrapolate different effects based on demographic dynamics. This includes the assessment of statistically associated neurological disorders in offspring, particularly IQ decay depending on the paternal age and other medical phenotypes which constitute paternal age effects. Yearly paternal mutation rates and correlations with paternal age were used to simulate both, de novo mutation propagation and probabilities for correlating conditions such as IQ decay. The extrapolated effect after several generations of persistently elevated paternal age appears to be drastic. To account for possibly mitigating factors, the paternal age effect has been super-positioned with the Flynn effect in simulated cases. The model automatically generates distributions for varying paternal ages, not just single cases, in convenient 3D distributions. The model simulates each person’s individual reproductive incidents through a particle type approach which is more rigorous than insufficiently adaptive, continuum models based on partial differential equations. The model is not only applicable to humans and yields many valuable conclusions for a wide array of topics including the paternal age effect, correlations with intelligence, evolution, bottlenecks in evolution, as well as the role of de novo mutation.
Subjects
De novo mutations
Demographic models
IQ
Paternal age
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