Comparing likelihood and Bayesian coalescent estimation of population parameters

Genetics

Volumn 175, Issue 1, 2007, Pages 155-165

  Kuhner, Mary K ^a   Smith, Lucian P ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA;

ALGORITHM; ARTICLE; BAYES THEOREM; COMPUTER PROGRAM; GENEALOGY; GROWTH RATE; METHODOLOGY; MODEL; MONTE CARLO METHOD; MUTATION; PARAMETER; POPULATION RESEARCH; PRIORITY JOURNAL; PROBABILITY; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; TECHNIQUE;

BAYES THEOREM; CHROMOSOME MAPPING; COMPUTER SIMULATION; GENETICS, POPULATION; LIKELIHOOD FUNCTIONS; MODELS, GENETIC; PROBABILITY; RECOMBINATION, GENETIC; VARIATION (GENETICS);

EID: 33846515096     PISSN: 00166731     EISSN: 00166731     Source Type: Journal    
DOI: 10.1534/genetics.106.056457     Document Type: Article

References (27)

1. ALFARO, M. E., S. ZOLLER and F. LUTZONI, 2003 Bayes or bootstrap? A simulation study comparing the performance of Bayesian Markov chain Monte Carlo sampling and bootstrapping in assessing phylogenetic confidence. Mol. Biol. Evol. 20: 255-266.
2. BAHLO, M., and R. C. GRIFFITHS, 2000 Inference from gene trees in a subdivided population. Theor. Popul. Biol. 57: 79-95.
3. BEERLI, P., 2006 Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics 22: 341-345.
4. BEERLI, P., and J. FELSENSTEIN, 1999 Maximum likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach. Genetics 152: 763-773.
5. BEERLI, P., and J. FELSENSTEIN, 2001 Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc. Natl. Acad. Sci. USA 98: 4563-4568.
6. DRUMMOND, A. J., G. K. NICHOLLS, A. G. RODRIGO and W. SOLOMON, 2002 Estimating mutation parameters, population history, and genealogy simultaneously from temporally spaced sequence data. Genetics 161: 1307-1320.
7. GEYER, C. J., 1991a Markov chain Monte Carlo maximum likelihood, pp. 156-163 in Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, edited by E. M. KERAMIDAS. Interface Foundation, Fairfax Station, VA.
8. GEYER, C. J., 1991b Estimating normalizing constants and reweighting mixtures in Markov chain Monte Carlo. Technical Report No. 568. School of Statistics, University of Minnesota, Minneapolis.
9. GRIFFITHS, R. C., and P. MARJORAM, 1996 Ancestral inference from samples of DNA sequences with recombination. J. Comput. Biol. 3: 479-502.
10. GRIFFITHS, R. C., and S. TAVARÉ, 1993 Sampling theory for neutral alleles in a varying environment. Proc. R. Soc. Lond. Ser. B 344: 403-410.
11. HASTINGS, W. K., 1970 Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57: 97-109.
12. HUDSON, R. R., 2002 Generating samples under a Wright-Fisher neutral model of genetic variations. Bioinformatics 18: 337-338.
13. KAPLAN, N., R. R. HUDSON and M. IIZUKA, 1991 The coalescent process in models with selection, recombination, and geographic subdivision. Genet. Res. 57: 83-91.
14. KIMURA, M., 1980 A simple model for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16: 111-120.
15. KINGMAN, J. F. C., 1982a The coalescent. Stoch. Proc. Appl. 13: 235-248.
16. KINGMAN, J. F. C., 1982b On the genealogy of large populations. J. Appl. Probab. 19A: 27-43.
17. KUHNER, M. K., and J. FELSENSTEIN, 2000 Sampling among haplotype resolutions in a coalescent-based genealogy sampler. Genet. Epidemiol. 19(Suppl. 1): S15-S21.
18. KUHNER, M. K., J. YAMATO and J. FELSENSTEIN, 1995 Estimating effective population size and mutation rate from sequence data using Metropolis-Hastings sampling. Genetics 140: 1421-1430.
19. KUHNER, M. K., J. YAMATO and J. FELSENSTEIN, 1998 Maximum likelihood estimates of population growth rates based on the coalescent. Genetics 149: 429-434.
20. KUHNER, M. K., J. YAMATO and J. FELSENSTEIN, 2000 Maximum likelihood estimation of recombination rates from population data. Genetics 156: 1393-1401.
21. METROPOLIS, N., A. W. ROSENBLUTH, M. N. ROSENBLUTH, A. H. TELLER and E. TELLER, 1953 Equations of state calculations by fast computing machines. J. Chem. Phys. 21: 1087-1092.
22. NIELSEN, R., and J. WAKELEY, 2001 Distinguishing migration from isolation: a Markov chain Monte Carlo approach. Genetics 158: 885-896.
23. PRESS, W. H., S. A. TEUKOLSKY, W. T. VETTERLING and B. P. FLANNERY, 2002 Numerical Recipes in C++: The Art of Scientific Computing, Ed. 2. Cambridge University Press, New York.
24. RAMBAUT, A., and A. J. DRUMMOND, 2003 Tracer v1.3 (http://evolve. zoo.ox.ac.uk/).
25. SILVERMAN, B. W., 1986 Density Estimation for Statistics and Data Analysis (Monographs on Statistics and Applied Probability, Vol. 26). Chapman Hall, London.
26. STEPHENS, M., 1999 Problems with computational methods in population genetics. Bulletin of the 52nd Session of the International Statistical Institute, Helsinki.
27. WATTERSON, G. A., 1975 On the number of segregating sites in genetical models without recombination. Theor. Popul. Biol. 7: 256-276.