MBE Advance Access originally published online on November 1, 2006
Molecular Biology and Evolution 2007 24(2):352-362; doi:10.1093/molbev/msl165
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Research Articles |
Time Squared: Repeated Measures on Phylogenies
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* Department of Biostatistics, School of Public Health, University of California, Los Angeles
Department of Genetics, Development & Cell Biology–Genetics, Iowa State University
Center for Bioinformatics and Biological Statistics, Iowa State University
Department of Biomathematics, David Geffen School of Medicine at University of California, Los Angeles
|| Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles
E-mail: msuchard{at}ucla.edu.
Accepted for publication October 20, 2006.
Studies of gene expression profiles in response to external perturbation generate repeated measures data that generally follow nonlinear curves. To explore the evolution of such profiles across a gene family, we introduce phylogenetic repeated measures (PR) models. These models draw strength from 2 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 nonphylogenetic repeated measures models when appropriate. We then analyze the evolution of gene expression in the yeast kinase family using splines to estimate nonlinear behavior across 3 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 4 primate species.
Key Words: Bayesian • evolution • development • gene expression • MCMC