Predicting evolutionary change at the DNA level in a natural Mimulus population

Area of Science:

• Evolutionary biology
• Population genetics
• Genomics

Background:

• Natural selection drives evolution through differential fitness, but predicting allele frequency changes from fitness data remains challenging.
• Understanding the quantitative link between fitness and genetic variation is key to predicting evolutionary trajectories.

Purpose of the Study:

• To quantitatively assess natural selection on millions of Single Nucleotide Polymorphisms (SNPs) in Mimulus guttatus.
• To calibrate population genetic models for predicting allele frequency shifts based on fitness estimates.
• To investigate trade-offs between different fitness components and their impact on allele maintenance.

Main Methods:

• Genomic analysis of millions of Single Nucleotide Polymorphisms (SNPs) in Mimulus guttatus.
• Estimation of fitness components (survival, reproductive success) in a natural population.
• Calibration of population genetic models using fitness data to predict allele frequency changes.
• Longitudinal study over two generations to track allele frequency dynamics.

Main Results:

• Hundreds of SNPs showed significant "male selection" in one generation, with allele frequencies shifting in the predicted direction in the next generation.
• SNPs influenced both viability and reproductive success, often exhibiting antagonistic pleiotropy (benefits in one component, costs in another).
• Trade-offs between fitness components and temporal fluctuations in selection were observed, potentially maintaining genetic variation.

Conclusions:

• Population genetic models can effectively predict evolutionary change by incorporating fitness measurements.
• Antagonistic pleiotropy and fluctuating selection may be critical mechanisms for the long-term maintenance of alleles.
• Despite challenges in field studies, strong correlations validate the predictive power of population genetic models for evolutionary change.