Estimating mutation rates in low-replication experiments.

Area of Science:

• Genetics
• Evolutionary Biology
• Microbiology

Background:

• Accurate estimation of mutation rate is vital in biological research.
• Standard mutation rate estimation methods often require high-replication experiments, which are frequently impractical.
• Mutant frequency is often reported instead, but it is less informative and poorly reproducible, especially with arithmetic means.

Purpose of the Study:

• To compare the performance of different methods for estimating mutation rate in low-replication settings (≤4 cultures).
• To evaluate whether fluctuation methods or mutant frequency averages are more suitable under resource constraints.
• To identify the optimal method for reproducible and accurate mutation rate estimation in low-replication experiments.

Main Methods:

• Computer simulations were employed to assess various statistical methods.
• The performance of mutant frequency averages (arithmetic mean, median, geometric mean) was compared.
• Two established fluctuation methods, including MSS maximum likelihood, were evaluated against mutant frequency averages.

Main Results:

• Contrary to common practice, fluctuation methods performed as well as or better than mutant frequency averages in low-replication experiments.
• MSS maximum likelihood demonstrated superior reproducibility compared to mutant frequency averages.
• Fluctuation methods allow for direct estimation of the mutation rate and facilitate conventional statistical analysis.

Conclusions:

• Fluctuation methods, particularly MSS maximum likelihood, are recommended for mutation rate estimation in low-replication experiments.
• These methods offer improved reproducibility and direct estimation of mutation rates, overcoming limitations of mutant frequency averages.
• Adopting fluctuation methods enhances the reliability and statistical validity of mutation rate studies under experimental constraints.