Genetic distance for a general non-stationary markov substitution process

Area of Science:

• Molecular Evolution
• Computational Biology
• Phylogenetics

Background:

• Genetic distance is crucial for understanding molecular evolution, rates of evolution, molecular clocks, and phylogenetic inference.
• Current continuous-time substitution models often assume stationarity and time-reversibility, which may not always hold true.
• Existing methods can lead to inaccuracies in estimating evolutionary parameters.

Purpose of the Study:

• To develop a more accurate measure of genetic distance by relaxing assumptions of stationarity and time-reversibility.
• To introduce a general nonstationary Markov model for calculating genetic distance.
• To compare the performance of the new model against existing methods.

Main Methods:

• Developed a general nonstationary Markov model for estimating genetic distance.
• Applied the model to biological sequence data from across the tree of life.
• Compared results with the general time-reversible model (with and without rate heterogeneity) and paralinear distance.

Main Results:

• Existing methods (general time-reversible, paralinear distance) systematically overestimate genetic distance and departure from the molecular clock.
• The overestimation bias is proportional to the departure from stationarity, linked to longer evolutionary timescales (edge lengths).
• The new nonstationary Markov model shows improved consistency with sequence alignments.

Conclusions:

• The developed general nonstationary Markov model provides a more accurate measure of genetic distance.
• Relaxing stationarity and time-reversibility assumptions is critical for accurate evolutionary analyses.
• This model is expected to significantly improve analyses of evolutionary rates and phylogenies.