Selective mutation accumulation: a computational model of the paternal age effect

Area of Science:

• Genetics
• Computational Biology
• Reproductive Medicine

Background:

• Increasing parental age is linked to a higher incidence of genetic disorders in offspring.
• Autosomal dominant disorders often exhibit a paternal age effect driven by selfish mutations.
• These mutations confer a selective advantage to spermatogonial stem cells (SSCs) in fathers but harm offspring.

Purpose of the Study:

• To develop a computational model of the SSC niche.
• To investigate the phenomenon of selfish mutations and their impact on genetic disease.
• To analyze the paternal age effect across various genes and disorders.

Main Methods:

• Utilized a Markov chain model to simulate mutation and positive selection during cell division.
• Fitted the model to existing data on disease incidence and sperm donor mutation assays.
• Quantified the strength of selective advantage for specific genetic disorders.

Main Results:

• The model accurately predicted disease incidence for numerous disorders.
• Disease prediction was sensitive to site-specific mutation rates and the number of mutable alleles.
• Demonstrated that stronger gain-of-function mutations within a gene exhibit greater selective advantage.
• Confirmed the significant contributions of both positive selection and copy-error mutation rates to the paternal age effect.

Conclusions:

• Computational modeling provides insights into the paternal age effect on genetic disease.
• Selfish mutations and their selective advantage in SSCs are key drivers of age-related genetic risks.
• Understanding these mechanisms is crucial for assessing and mitigating genetic disease risks associated with advanced paternal age.