title: Ascertainment correction for a population tree via a pruning algorithm for likelihood computation.
abstract: We present a method for correcting ascertainment-bias in a coalescent-based likelihood for population trees. Our method is computationally simple and fast. To correct for the bias we compute the probability of allele-counts conditioned on the locus being included. This conditional probability is simply the uncorrected likelihood divided by the inclusion probability. A modification of a pruning algorithm is introduced so that the inclusion probability can be computed with a single run of the algorithm. Our computation is exact and avoids Monte-Carlo based methods.
authors.affiliation: Department of Biostatistics, Columbia University, New York, NY 10032, USA. ar2946@columbia.edu
authors.forename: Arindam
authors.initials: A
authors.lastname: RoyChoudhury
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Thompson
mesh_terms: D000465:Algorithms; D005828:Genetics, Population; D016013:Likelihood Functions; D020641:Polymorphism, Single Nucleotide