Graph-structured populations elucidate the role of deleterious mutations in long-term evolution

Area of Science:

• Evolutionary biology
• Population genetics
• Mathematical modeling

Background:

• Birth-death models are standard for studying genetic drift and natural selection.
• In well-mixed populations, evolutionary outcomes are independent of birth/death order.
• Spatially structured populations introduce complexities influenced by these orders.

Purpose of the Study:

• To investigate how the movement rules of individuals (parent vs. offspring) in spatially structured populations affect evolutionary dynamics.
• To identify conditions under which fixation probabilities differ from well-mixed populations.
• To explore the long-term evolutionary consequences of these spatial effects.

Main Methods:

• Utilizing birth-death models on graph structures to simulate spatial populations.
• Analyzing mutant fixation probabilities under different update rules (parent moving vs. offspring moving).
• Comparing evolutionary outcomes on specific graph types (star graphs, Erdős-Rényi graphs) against well-mixed populations.

Main Results:

• The choice of moving parent or offspring significantly alters mutant placement and fixation probability.
• A new class of graphs, termed "amplifiers of fixation," were identified, showing higher fixation probabilities than well-mixed populations.
• The star graph and most size-8 Erdős-Rényi graphs act as amplifiers under specific (death-Birth parent moving) rules.
• Surprisingly, amplifiers of fixation lead to lower long-term population fitness despite favoring beneficial mutants.

Conclusions:

• Individual movement rules in spatially structured populations critically influence evolutionary trajectories.
• Deleterious mutations play a surprisingly important role in adaptive evolution within these structured systems.
• The findings challenge traditional assumptions and highlight the importance of spatial structure and update rules in evolutionary modeling.