Using rare mutations to estimate population divergence times: a maximum likelihood approach

Area of Science:

• Population Genetics
• Human Evolutionary Biology
• Genomic Data Analysis

Background:

• Estimating divergence times between populations is crucial for understanding human history.
• Rare mutations, particularly those associated with diseases, offer valuable genetic markers.
• Existing methods may not be optimal for analyzing nonrecurrent rare mutations.

Purpose of the Study:

• To develop and validate a maximum likelihood method for estimating divergence times using nonrecurrent rare mutations.
• To apply the method to analyze divergence times among human populations using cystic fibrosis mutations.

Main Methods:

• Derivation of the RD and RDc estimators based on birth-death process approximation for rare alleles.
• Assumption that mutations predate population splits.
• Application of the RD estimator to cystic fibrosis mutations in Mediterranean and Danish populations.

Main Results:

• The RD estimator could not rule out very recent divergence among three Mediterranean populations.
• Estimated divergence times between Mediterranean and Danish populations ranged from 4,500 to 15,000 years, depending on assumptions about selective advantage.
• Confidence intervals for divergence times were large, indicating a need for more data.

Conclusions:

• The proposed method provides a novel approach for divergence time estimation using rare mutations.
• Analysis of cystic fibrosis mutations reveals insights into population history.
• Further research with more genetic data is needed to refine divergence time estimates.