Abstract
Coalescent likelihood is the probability of observing the given population sequences under the coalescent model. Computation of coalescent likelihood under the infinite sites model is a classic problem in coalescent theory. Existing methods are based on either importance sampling or Markov chain Monte Carlo. In this paper, we develop a simple method that can compute the exact coalescent likelihood for many datasets of moderate size, including a real biological data whose likelihood was previously thought to be difficult to compute exactly. Simulations demonstrate that the practical range of exact coalescent likelihood computation is significantly larger than what was previously believed.
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References
Bahlo, M., Griffiths, R.C.: Inference from Gene Trees in a Subdivided Population. Theoretical Population Biology 57, 79–95 (2000)
Ethier, S.N., Griffiths, R.C.: The Infinitely-Many-Sites Model as a Measure Valued Diffusion. Annals of Probability 15, 515–545 (1987)
Ewens, W.J.: The sampling theory of selectively neutral alleles. Theor. Popul. Biol. 3, 87–112 (1972)
Griffiths, R.C., Tavarè, S.: Simulatiing Probability Distributions in the Coalescent. Theor. Popul. Biol. 46, 131–159 (1994)
Griffiths, R.C., Tavarè, S.: Ancestral inference in population genetics Statistical Science 9, 307–319 (1994)
Griffiths, R.C., Jenkins, P.A., Song, Y.S.: Importance Sampling and Two-Locus Model with Subdivided Population Structure. Adv. Appl. Prob. 40, 473–500 (2008)
Gusfield, D.: Efficient algorithms for inferring evolutionary history. Networks 21, 19–28 (1991)
Hein, J., Schierup, M., Wiuf, C.: Gene Genealogies, Variation and Evolution: A primer in coalescent theory. Oxford University Press, Oxford (2005)
Hobolth, A., Uyenoyama, M.K., Wiuf, C.: Importance Sampling for the Infinite Sites Model. Stat. Appl. Genet. and Mol. Biol. 7 Article 32 (2008)
Hudson, R.: Generating Samples under the Wright-Fisher neutral model of genetic variation. Bioinformatics 18(2), 337–338 (2002)
Kingman, J.F.C.: The coalescent. Stochast. Process. Appl. 13, 235–248 (1982)
Kuhner, M.K., Yamato, J., Felsenstein, J.: Estimating effective population size and mutation rate from sequence data using Metropolis-Hastings sampling. Genetics 140, 1421–1430 (1995)
Lyngso, R., Song, Y.S., Hein, J.: Accurate Computation of Likelihoods in the Coalescent with Recombination Via Parsimony. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS (LNBI), vol. 4955, pp. 463–477. Springer, Heidelberg (2008)
Song, Y.S., Lyngsoe, R., Hein, J.: Counting all possible ancestral configurations of sample sequences in population genetics. IEEE/ACM Transactions on Computational Biology and Bioinformatics 3, 239–251 (2006)
Stephens, M., Donnelly, P.: Inference in molecular population genetics. J. R. Stat. Soc. 62, 605–655 (2000)
Tavarè, S.: Ancestral Inference in Population Genetics. In: Lectures on Probability Theory and Statistics, pages 1931. Springer, Heidelberg (2004)
Wakeley, J.: Coalescent Theory: An Introduction. Roberts and Company Publishers, Greenwood Village (2008)
Ward, R.H., Frazier, B.L., Dew, K., Paabo, S.: Extensive Mitochondria Diversity within a Single Amerindian Tribe. Proc. of the Nat. Academy of Science 88, 8720–8724 (1991)
Watterson, G.A.: On the number of segregating sites in genetical models without recombination. Theoretical Population Biology 7, 256–276 (1975)
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Wu, Y. (2009). Exact Computation of Coalescent Likelihood under the Infinite Sites Model. In: Măndoiu, I., Narasimhan, G., Zhang, Y. (eds) Bioinformatics Research and Applications. ISBRA 2009. Lecture Notes in Computer Science(), vol 5542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01551-9_21
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DOI: https://doi.org/10.1007/978-3-642-01551-9_21
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