Maximum likelihood estimation of spontaneous mutation rates from large initial populations

Area of Science:

• Genetics
• Evolutionary Biology
• Biostatistics

Background:

• Estimating spontaneous mutation rates is crucial for understanding genetic variation and disease.
• Traditional methods often rely on fluctuation experiments with specific assumptions about population size and mutant distribution.

Purpose of the Study:

• To develop and validate a more accurate maximum likelihood estimator for spontaneous mutation rates.
• To assess the performance of the new estimator compared to existing methods, particularly concerning initial population size.

Main Methods:

• Utilized a Gaussian approximation for the distribution of mutant counts in cultures undergoing exponential growth.
• Derived a new maximum likelihood estimator (p) for the mutation rate.
• Employed Monte Carlo simulations to compare the mean squared error of the new estimator against a previous method.

Main Results:

• The proposed maximum likelihood estimator (p) demonstrates improved accuracy and efficiency, especially with large initial population sizes (N0).
• The new method shows a smaller mean squared error compared to the estimator by Rossman et al. (1995).
• The advantage of the new estimator persists even for single cultures (C=1).

Conclusions:

• A large initial population size (N0) significantly enhances the accuracy of spontaneous mutation rate estimation.
• The developed Gaussian approximation-based maximum likelihood estimator offers a statistically robust and efficient approach for mutation rate determination.
• This method provides a valuable tool for genetic research, improving the reliability of mutation rate studies.