Adaptive Protein Evolution in Animals and the Effective Population Size Hypothesis

Area of Science:

• Evolutionary Genetics
• Molecular Evolution
• Genomics

Background:

• The rate of genome adaptation and prevalence of adaptive processes in molecular evolution are debated.
• Previous studies on adaptive rates focused on model organisms, yielding diverse patterns and lacking a comprehensive overview.
• Discrepancies in adaptive rates between species like Drosophila and humans were hypothesized to relate to effective population size.

Purpose of the Study:

• To quantify the genome-wide rate of adaptation through amino-acid substitution across a broader range of species.
• To investigate the influence of effective population size on adaptive and nearly-neutral evolutionary processes.
• To provide a homogeneous dataset and methodology for comparing adaptive protein evolution across diverse animal taxa.

Main Methods:

• Developed extended estimators for amino-acid adaptive rates, modeling the impact of favorable mutations on non-synonymous polymorphism.
• Compiled a new, homogeneous dataset of 44 non-model animal species pairs for analysis.
• Applied the developed methods to analyze adaptive substitution patterns and their correlation with effective population size.

Main Results:

• Adaptive evolution significantly contributes to amino-acid substitution across most metazoan phyla, with humans and primates as notable exceptions.
• A positive correlation was observed between the proportion of adaptive amino-acid substitution and species' effective population size.
• This correlation is mainly driven by a reduced rate of nearly-neutral amino-acid substitution in larger populations due to more efficient purifying selection.

Conclusions:

• Adaptive processes are dominant in protein evolution for most animal species studied.
• The hypothesis that adaptive substitutions accumulate faster in larger populations is not supported by these findings.
• Results highlight the importance of purifying selection in large populations and offer insights into factors affecting adaptive evolution rates and positive selection detection across species.