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Computer model of the maintenance and selection of genetic heterogeneity in polygamous helminths

Published online by Cambridge University Press:  06 April 2009

A. Saul
Affiliation:
Tropical Health Program, Queensland Institute of Medical Research, Royal Brisbane Hospital, Brisbane, Queensland, Australia4029

Summary

A stochastic simulation model of the transmission and maintenance of genetic heterogeneity in the absence and presence of external selection pressures is presented for polygamous intestinal helminths such as Ascaris. The model assumes that the density distribution of the adult parasites is highly aggregated and that density-dependent effects on fecundity are important. The model gives rise to stable infection rates in the host. Where the parasite population contains genetic heterogeneity, with the exception of stochastic fluctuations which models genetic drift, the ratio of the different alleles remained constant over extended periods of time. This result contrasts with that of an earlier analytical model (Anderson, R. M., May, M. R. & Gutpa S. (1989) Parasitology 99, S59–S79), in which uneven mating probabilities for the different combinations of worm possible in a host was postulated to inevitably lead to fixation of the most abundant allele. New results suggest that in spite of the restricted choice of mating available to a worm in the confines of a host, selection pressure always leads to enrichment of the parasites carrying resistant alleles.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1995

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References

REFERENCES

Anderson, R. M., May, R. M. & Gupta, s. (1989). Nonlinear phenomenon in host-parasite interactions. Parasitology 99, S59–S79.CrossRefGoogle Scholar
Bliss, C. I. & Fisher, R. A. (1953). Fitting the negative binomial distribution to biological data. Biometrics 9, 176200.CrossRefGoogle Scholar
Croll, N. A., Anderson, R. M., Gyorkos, T. W. & Ghadirian, E. (1982). The population biology and control of Ascaris lumbricoides in rural community in Iran. Transactions of the Royal Society of Tropical Medicine and Hygiene 76, 187–97.CrossRefGoogle ScholarPubMed
Dixon, W. J. (1973). BMD07R Nonlinear Regression. In BMD Biomedical Computer Programs, (ed. Dixon, W. J.), pp. 387396. Berkeley: University of California Press.Google Scholar
Haswell-Elkins, M., Elkins, D. & Anderson, R. M. (1989). The influence of individual, social group and household factors on the distribution of Ascaris lumbricoides within a community and implications for control strategies. Parasitology 98, 125–34.CrossRefGoogle ScholarPubMed
May, R. M. (1977). Togetherness among schistosomes: its effects on the dynamics of the infection. Mathematical Biosciences 35, 301–43.CrossRefGoogle Scholar
Pacala, S. W. & Dobson, A. P. (1988). The relationship between the number of parasites/host and host age: population dynamic causes and maximum likelihood estimation. Parasitology 96, 197210.CrossRefGoogle Scholar
Prociv, P. (1989). Observations on egg production by Toxocara pteropodis. International Journal for Parasitology 19, 441–3.CrossRefGoogle ScholarPubMed