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Weak convergence of discrete time non-markovian processes related to selection models in population genetics

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Abstract

We consider discrete time stochastic processes defined by solutions to some non-linear difference equations whose coefficients are autocorrelated random sequences. It is proved that these processes converge weakly in D[0, T] to diffusion processes, under the assumption that the random sequences satisfy some mixing condition. Diffusion approximation for stochastic selection models in population genetics is discussed, as the application of this limit theorem.

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References

  1. Billingsley, P.: Convergence of probability measures. New York: Wiley 1968

    Google Scholar 

  2. Cook, R. D., Hartl, D. L.: Uncorrelated random environments and their effects on gene frequency. Evolution 28, 265–274 (1974)

    Google Scholar 

  3. Crow, J. F., Kimura, M.: An introduction to population genetics theory. New York: Harper and Row 1970

    Google Scholar 

  4. Ewens, W. J.: Mathematical population genetics. New York: Springer 1979

    Google Scholar 

  5. Feller, W.: Diffusion processes in genetics. Second Berkeley Symp. Math. Stat. Prob., pp. 227–246. Berkeley and Los Angeles: University of California Press 1951

    Google Scholar 

  6. Gikhman, I. I.: Convergence to Markov processes. Ukrainian Math. Jour. 21, 263–270 (1969)

    Google Scholar 

  7. Gillespie, J. H.: Natural selection with varying selection coefficients — A haploid model. Genet. Res., Camb. 21, 115–120 (1973a)

    Google Scholar 

  8. Gillespie, J. H.: Polymorphism in random environments. Theoret. Population Biology 4, 193–195 (1973b)

    Google Scholar 

  9. Gillespie, J. H.: A general model to account for enzyme variation in natural populations, V. The SAS-CFF model. Theoret. Population Biology 14, 1–45 (1978)

    Google Scholar 

  10. Gillespie, J. H., Guess, H. A.: The effects of environmental autocorrelations on the progress of selection in a random environment. Amer. Natur. 112, 897–909 (1978)

    Google Scholar 

  11. Guess, H. A.: On the weak convergence of Wright-Fisher models. Stoch. Processes Appl. 1, 287–306 (1973)

    Google Scholar 

  12. Guess, H. A., Gillespie, J. H.: Diffusion approximations to linear stochastic difference equations with stationary coefficients. J. Appl. Prob. 14, 58–74 (1977)

    Google Scholar 

  13. Hartl, D. L.: Mutation — selection balance with stochastic selection. Genetics 86, 687–696 (1977)

    Google Scholar 

  14. Hartl, D. L., Cook, R. D.: Balanced polymorphisms of quasineutral alleles. Theoret. Population Biology 4, 163–172 (1973)

    Google Scholar 

  15. Hartl, D. L., Cook, R. D.: Autocorrelated random environments and their effects on gene frequency. Evolution 28, 275–280 (1974)

    Google Scholar 

  16. Ito, K.: Lectures on stochastic processes. Bombay: Tata Institute of Fundamental Research 1961

    Google Scholar 

  17. Jensen, L., Pollak, E.: Random selective advantages of a gene in a finite population. J. Appl. Prob. 6, 19–37 (1969)

    Google Scholar 

  18. Karlin, S., Levikson, B.: Temporal fluctuations in selection intensities: Case of small population size. Theoret. Population Biology 6, 383–412 (1974)

    Google Scholar 

  19. Karlin, S., Lieberman, U.: Random temporal variation in selection intensities: Case of large population size. Theoret. Population Biology 6, 355–382 (1974)

    Google Scholar 

  20. Kimura, M.: Stochastic processes and distribution of gene frequencies under natural selection. Cold Spring Harbor Symp. 20, 33–53 (1955)

    Google Scholar 

  21. Kimura, M.: Diffusion models in population genetics. J. Appl. Prob. 1, 177–232 (1964)

    Google Scholar 

  22. Kimura, M., Ohta, T.: Theoretical aspects of population genetics. Princeton: Princeton University Press 1971

    Google Scholar 

  23. Kurtz, T. G.: Semigroup of conditioned shifts and approximation of Markov processes. Ann. Probab. 3, 618–642 (1975)

    Google Scholar 

  24. Kushner, H. J., Huang, H.: On the weak convergence of a sequence of general stochastic difference equations to a diffusion. SIAM J. Appl. Math. 40, 528–541 (1981)

    Google Scholar 

  25. Levikson, B., Karlin, S.: Random temporal variation in selection intensities acting on infinite diploid populations: Diffusion method analysis. Theoret. Population Biology 8, 292–300 (1975)

    Google Scholar 

  26. Li, W.-H. (ed.): Stochastic models in population genetics. Pennsylvania: Dowden, Hutchinson and Ross, Inc., 1977

    Google Scholar 

  27. Maruyama, T.: Stochastic problems in population genetics. New York: Springer 1977

    Google Scholar 

  28. Matsuda, H., Gojobori, T.: Protein polymorphism and fluctuation of environments. Adv. Biophys. 12, 53–99 (1979)

    Google Scholar 

  29. Norman, M. F.: Diffusion approximation of non-Markovian processes. Ann. Probab. 3, 358–364 (1975)

    Google Scholar 

  30. Okada, N.: On convergence to diffusion processes of Markov chains related to population genetics. Adv. Appl. Prob. 11, 673–700 (1979)

    Google Scholar 

  31. Papanicolaou, G. C., Kohler, W.: Asymptotic theory of mixing stochastic ordinary differential equations. Comm. Pure Appl. Math. 27, 641–668 (1974)

    Google Scholar 

  32. Sato, K.: Diffusion processes and a class of Markov chains related to population genetics. Osaka J. Math. 13, 631–659 (1976a)

    Google Scholar 

  33. Sato, K.: A class of Markov chains related to selection in population genetics. J. Math. Soc. Japan 28, 621–637 (1976b)

    Google Scholar 

  34. Sato, K.: Convergence of a class of Markov chains to multi-dimensional degenerate diffusion processes. Proc. Intern. Symp. Stoch. Diff. Eq. Kyoto 367–383 (1976c)

  35. Sato, K.: Convergence to a diffusion of a multiallelic model in population genetics. Adv. Appl. Prob. 10, 538–562 (1978)

    Google Scholar 

  36. Serfling, R. J.: Moment inequalities for the maximum cumulative sum. Ann. Math. Statist. 41, 1227–1234 (1970)

    Google Scholar 

  37. Stroock, D. W., Varadhan, S. R. S.: Multidimensional diffusion processes. Berlin: Springer 1979

    Google Scholar 

  38. Takahata, N., Ishii, K., Matsuda, H.: Effect of temporal fluctuation of selection coefficient on gene frequency in a population. Proc. Nat. Acad. Sci. USA 72, 4541–4545 (1975)

    Google Scholar 

  39. Trotter, H. F.: Approximation of semi-groups of operators. Pacific J. Math. 8, 887–919 (1958)

    Google Scholar 

  40. Watterson, G. A.: Some theoretical aspects of diffusion theory in population genetics. Ann. Math. Statist. 33, 939–957 (1962)

    Google Scholar 

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Iizuka, M., Matsuda, H. Weak convergence of discrete time non-markovian processes related to selection models in population genetics. J. Math. Biology 15, 107–127 (1982). https://doi.org/10.1007/BF00275792

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  • DOI: https://doi.org/10.1007/BF00275792

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