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Updated: Feb 4, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

Identifying Drivers of Parallel Evolution: A Regression Model Approach.

Susan F Bailey^1,2, Qianyun Guo¹, Thomas Bataillon¹

¹Bioinformatics Research Centre, Aarhus University, Denmark.

Genome Biology and Evolution

|September 26, 2018

Summary

This summary is machine-generated.

Mutation and selection heterogeneity significantly drive parallel evolution in yeast populations. Gene-level variations in mutation rates and selection pressures, influenced by factors like gene length, explain evolutionary patterns.

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Molecular Evolution of the Tre Recombinase

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Published on: May 29, 2008

Area of Science:

Evolutionary biology
Genetics
Computational biology

Background:

Parallel evolution, identical genetic changes in independent populations, is often linked to similar selective pressures.
However, heterogeneous mutation rates across the genome can also contribute to parallel evolution.
The relative impact of mutation versus selection on parallel evolution patterns remains empirically underquantified.

Purpose of the Study:

To develop statistical models for quantifying the contributions of mutation and selection heterogeneity to gene-level parallel evolution.
To empirically assess the roles of mutation and selection heterogeneity in shaping evolutionary trajectories.
To investigate genomic factors influencing both mutation rates and selection pressures.

Main Methods:

Introduction of novel statistical models to dissect parallel evolution drivers.
Analysis of published experimental evolution data from Saccharomyces cerevisiae populations.
Partitioning genomic variable effects into mutation rate and selection (retention/loss) components.

Main Results:

Gene-to-gene heterogeneity in mutation and selection significantly drives parallel evolution at both synonymous and nonsynonymous sites.
Factors such as gene length, recombination rate, and protein domain count are associated with this heterogeneity.
The models provide improved predictions for the prevalence and extent of parallel evolution.

Conclusions:

Both heterogeneous mutation rates and selection pressures are critical drivers of parallel evolution.
Genomic features influence mutation and selection heterogeneity, impacting evolutionary outcomes.
Accounting for mutation and selection heterogeneity enhances our understanding and predictive power of parallel evolution.