Recurrent mutation in the ancestry of a rare variant

Area of Science:

• Population Genetics
• Evolutionary Biology
• Statistical Genetics

Background:

• Recurrent mutations can create multiple copies of the same allele, complicating population genetic analyses.
• Current methods often assume a single ancestral mutation for observed alleles, overlooking recurrent events.

Purpose of the Study:

• To develop a sampling theory for latent mutations in the ancestry of rare variants.
• To provide a more accurate framework for analyzing allele-frequency and site-frequency spectra.

Main Methods:

• Developed a statistical sampling theory for low-count mutations in large samples.
• Utilized coalescent theory and diffusion models, including the Ewens sampling formula.
• Created a Poisson sampling model for populations with varying sizes, applied to exponentially growing populations.

Main Results:

• Demonstrated statistical independence of low-count mutations under standard models.
• Showed that counts follow Ewens sampling formula distributions in constant-size populations.
• Applied the model to human SNP data, explaining variations in site-frequency spectra across mutation rates.

Conclusions:

• The new theory accurately models the number of latent mutations in rare variants.
• This framework enhances the interpretation of population genetic data, particularly human SNP spectra.
• Provides insights into the impact of recurrent mutations on genetic diversity estimates.