Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Evaluating the likelihood function of parameters in highly-structured population genetic models from extant deoxyribonucleic acid (DNA) sequences is computationally prohibitive. In such cases, one may approximately infer the parameters from summary statistics of the data such as the site-frequency-spectrum (SFS) or its linear combinations. Such methods are known as approximate likelihood or Bayesian computations. Using a controlled lumped Markov chain and computational commutative algebraic methods, we compute the exact likelihood of the SFS and many classical linear combinations of it at a non-recombining locus that is neutrally evolving under the infinitely-many-sites mutation model. Using a partially ordered graph of coalescent experiments around the SFS, we provide a decision-theoretic framework for approximate sufficiency. We also extend a family of classical hypothesis tests of standard neutrality at a non-recombining locus based on the SFS to a more powerful version that conditions on the topological information provided by the SFS.

Original publication

DOI

10.1007/s11538-010-9605-5

Type

Journal article

Journal

Bulletin of mathematical biology

Publication Date

04/2011

Volume

73

Pages

829 - 872

Addresses

Biomathematics Research Centre, Christchurch, New Zealand. r.sainudiin@math.canterbury.ac.nz

Keywords

Likelihood Functions, Monte Carlo Method, Probability, Bayes Theorem, Markov Chains, Stochastic Processes, Pedigree, Sequence Alignment, Genetics, Population, Population Density, Population Growth, Base Sequence, Heterozygote, Mutation, Algorithms, Models, Genetic, Computer Simulation