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Time squared: repeated measures on phylogenies.

Hua Guo1, Robert E Weiss, Xun Gu

  • 1Department of Biostatistics, School of Public Health, University of California, Los Angeles, USA.

Molecular Biology and Evolution
|November 3, 2006
PubMed
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Phylogenetic repeated measures (PR) models analyze gene expression evolution by integrating evolutionary relationships and time-series data. These models accurately capture nonlinear gene expression changes and outperform existing methods for evolutionary analysis.

Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Gene expression studies generate nonlinear, repeated measures data.
  • Understanding gene family evolution requires accounting for evolutionary relatedness and temporal correlations.

Purpose of the Study:

  • Introduce phylogenetic repeated measures (PR) models to analyze gene expression evolution.
  • Improve accuracy in modeling nonlinear gene expression profiles across gene families.

Main Methods:

  • Utilize a Brownian diffusion process along a phylogenetic tree to model evolutionary relatedness.
  • Employ a repeated measures framework to capture temporal correlations within genes.
  • Apply Bayesian model selection using deviance information criteria for model comparison.

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Main Results:

  • PR models significantly outperform existing methods lacking phylogenetic or repeated measures considerations.
  • Phylogenetic PR models show superior performance compared to nonphylogenetic repeated measures models.
  • Analysis of the yeast kinase family revealed nonlinear expression evolution and enabled ancestral profile prediction.

Conclusions:

  • PR models provide a robust framework for analyzing complex gene expression evolution.
  • The models are applicable to diverse biological systems, including primate brain development.
  • PR models enhance our ability to predict ancestral gene expression patterns.