A two-stage pruning algorithm for likelihood computation for a population tree. Academic Article uri icon

Overview

abstract

  • We have developed a pruning algorithm for likelihood estimation of a tree of populations. This algorithm enables us to compute the likelihood for large trees. Thus, it gives an efficient way of obtaining the maximum-likelihood estimate (MLE) for a given tree topology. Our method utilizes the differences accumulated by random genetic drift in allele count data from single-nucleotide polymorphisms (SNPs), ignoring the effect of mutation after divergence from the common ancestral population. The computation of the maximum-likelihood tree involves both maximizing likelihood over branch lengths of a given topology and comparing the maximum-likelihood across topologies. Here our focus is the maximization of likelihood over branch lengths of a given topology. The pruning algorithm computes arrays of probabilities at the root of the tree from the data at the tips of the tree; at the root, the arrays determine the likelihood. The arrays consist of probabilities related to the number of coalescences and allele counts for the partially coalesced lineages. Computing these probabilities requires an unusual two-stage algorithm. Our computation is exact and avoids time-consuming Monte Carlo methods. We can also correct for ascertainment bias.

publication date

  • September 9, 2008

Research

keywords

  • Algorithms
  • Evolution, Molecular

Identity

PubMed Central ID

  • PMC2567359

Scopus Document Identifier

  • 58149357704

Digital Object Identifier (DOI)

  • 10.1534/genetics.107.085753

PubMed ID

  • 18780754

Additional Document Info

volume

  • 180

issue

  • 2