New Methods for Detecting Lineage-Specific Selection
So far, most methods for identifying sequences under selection based on comparative sequence data have either assumed selectional pressures are the same across all branches of a phylogeny, or have focused on changes in specific lineages of interest. Here, we introduce a more general method that detects sequences that have either come under selection, or begun to drift, on any lineage. The method is based on a phylogenetic hidden Markov model (phylo-HMM), and does not require element boundaries to be determined a priori, making it particularly useful for identifying noncoding sequences. Insertions and deletions (indels) are incorporated into the phylo-HMM by a simple strategy that uses a separately reconstructed “indel history.” To evaluate the statistical significance of predictions, we introduce a novel method for computing P-values based on prior and posterior distributions of the number of substitutions that have occurred in the evolution of predicted elements. We derive efficient dynamic-programming algorithms for obtaining these distributions, given a model of neutral evolution. Our methods have been implemented as computer programs called DLESS (Detection of LinEage-Specific Selection) and phyloP (phylogenetic P-values). We discuss results obtained with these programs on both real and simulated data sets.
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- 2.Woolfe, A., Goodson, M., Goode, D., Snell, P., McEwen, G., Vavouri, T., Smith, S., North, P., Callaway, H., Kelly, K., et al.: Highly conserved non-coding sequences are associated with vertebrate development. PLoS Biol. 3, e7 (2005)Google Scholar
- 7.Nielsen, R., Yang, Z.: Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics 148, 929–936 (1998)Google Scholar
- 8.Yang, Z., Nielsen, R.: Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages. Mol. Biol. Evol. 19, 908–917 (2002)Google Scholar
- 12.Nielsen, R., Bustamante, C., Clark, A.G., Glanowski, S., Sackton, T.B., Hubisz, M.J., Fledel-Alon, A., Tanenbaum, D.M., Civello, D., White, T.J., et al.: A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol. 3, e170 (2005)Google Scholar
- 14.Felsenstein, J., Churchill, G.A.: A hidden Markov model approach to variation among sites in rate of evolution. Mol. Biol. Evol. 13, 93–104 (1996)Google Scholar
- 15.Yang, Z.: A space-time process model for the evolution of DNA sequences. Genetics 139, 993–1005 (1995)Google Scholar
- 19.Siepel, A., Haussler, D.: Computational identification of evolutionarily conserved exons. In: Proc. 8th Int’l Conf. on Research in Computational Molecular Biology, pp. 177–186 (2004)Google Scholar
- 24.Jukes, T.H., Cantor, C.R.: Evolution of protein molecules. In: Munro, H. (ed.) Mammalian Protein Metabolism, pp. 21–132. Academic Press, New York (1969)Google Scholar
- 25.Gillespie, J.: Lineage effects and the index of dispersion of molecular evolution. Mol. Biol. Evol. 6, 636–647 (1989)Google Scholar
- 28.Felsenstein, J.: Inferring Phylogenies. Sinauer Associates, Inc., Sunderland, Massachusetts (2004)Google Scholar
- 29.Nielsen, R., Huelsenbeck, J.P.: Detecting positively selected amino acid sites using posterior predictive P-values. Pac. Symp. Biocomput., 576–588 (2002)Google Scholar