Multigeneration maximum-likelihood analysis applied to mutation-accumulation experiments in Caenorhabditis elegans.

Area of Science:

• Evolutionary genetics
• Quantitative genetics

Background:

• Mutation accumulation (MA) experiments are crucial for understanding mutation rates and effects.
• Traditional methods like the method of moments have limitations in estimating these parameters accurately.

Purpose of the Study:

• To develop and evaluate a maximum-likelihood (ML) approach for estimating genomic mutation rates (U) and average homozygous mutation effects (s) from MA experiments.
• To compare the performance of the ML method with the traditional method of moments.

Main Methods:

• Development of a maximum-likelihood statistical framework.
• Simulations to assess the precision of ML versus method of moments.
• Analysis of life-history trait data from two Caenorhabditis elegans MA experiments.

Main Results:

• The ML approach provides estimates with lower sampling variances than the method of moments when mutations with large effects have accumulated.
• Including data from intermediate generations can enhance estimation precision.
• Analysis of C. elegans data suggests that mutations with large effects dominate changes in genetic values, but small-effect mutations may also be present.

Conclusions:

• The ML method is a more precise tool for analyzing MA data, particularly for detecting large-effect mutations.
• The findings contribute to a better understanding of mutation dynamics and their impact on quantitative traits in populations.
• Further research may explore the contribution of small-effect mutations to adaptation and evolution.