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MBE Advance Access published online on November 1, 2006
Molecular Biology and Evolution, doi:10.1093/molbev/msl165
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© The Author 2006. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Accepted October 20, 2006

Research Article

Time Squared: Repeated Measures on Phylogenies

Hua Guo 1, Robert E. Weiss 1, Xun Gu 2, and Marc A. Suchard 3 *

1 Department of Biostatistics, School of Public Health, UCLA, Los Angeles, CA 90095
2 Department of Genetics, Development & Cell Biology-Genetics, Iowa State University, Ames, IA 50011; Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011
3 Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095; Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095

* To whom correspondence should be addressed.
Marc A. Suchard, E-mail: msuchard{at}ucla.edu


  Abstract

Studies of gene expression profiles in response to external perturbation generate repeated measures data that generally follow non-linear curves. To explore the evolution of such profiles across a gene family, we introduce phylogenetic repeated measures (PR) models. These models draw strength from two forms of correlation in the data. Through gene duplication, the family's evolutionary relatedness induces the first form. The second is the correlation across time-points within taxonic units, individual genes in this example. We borrow a Brownian diffusion process along a given phylogenetic tree to account for the relatedness and co-opt a repeated measures framework to model the latter. Through simulation studies, we demonstrate that repeated measures models outperform the previously available approaches that consider the longitudinal observations or their differences as independent and identically distributed by using deviance information criteria as Bayesian model selection tools; PR models that borrow phylogenetic information also perform better than non-phylogenetic repeated measures models when appropriate. We then analyze the evolution of gene expression in the yeast kinase family using splines to estimate non-linear behavior across three perturbation experiments. Again, the PR models outperform previous approaches and afford the prediction of ancestral expression profiles. To demonstrate PR model applicability more generally, we conclude with a short examination of variation in brain development across four primates species.

Keywords: Bayesian; Evolution; Development; Gene Expression; MCMC.
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[Abstract] [Full Text] [PDF]


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