New Methods for Detecting Lineage-Specific Selection

  • Adam Siepel
  • Katherine S. Pollard
  • David Haussler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3909)


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.


Posterior Distribution False Positive Rate Neutral Model Full Paper Neutral Evolution 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Adam Siepel
    • 1
  • Katherine S. Pollard
    • 1
  • David Haussler
    • 1
    • 2
  1. 1.Center for Biomolecular Science and Engineering, U.C. Santa CruzSanta CruzUSA
  2. 2.Howard Hughes Medical Institute, U.C. Santa CruzSanta CruzUSA

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