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An Expectation Maximization Approach to Estimate Malaria Haplotype Frequencies in Multiply Infected Children

Author

Listed:
  • Li Xiaohong

    (University of Massachusetts, Amherst)

  • Foulkes Andrea S

    (University of Massachusetts, Amherst)

  • Yucel Recai M.

    (University of Massachusetts, Amherst)

  • Rich Stephen M.

    (University of Massachusetts, Amherst)

Abstract

Characterizing genetic variability in the human pathogenic Plasmodium species, the group of parasites that cause Malaria, may have broad global health implications. Specifically, discerning the combinations of mutations that lead to viable parasites and the population level frequencies of these clonal sequences will allow for targeted vaccine development and individualized treatment choices. This presents an analytical challenge, however, since haplotypic phase (i.e. the alignment of bases on a single DNA strand) is generally unobservable in multiply infected individuals. This manuscript describes an expectation maximization (EM) approach to maximum likelihood estimation of haplotype frequencies in this missing data setting. The approach is applied to a cohort of N=341 malaria infected children in Uganda, Cameroon and Sudan to characterize regional differences. A simulation study is also presented to characterize method performance and assess sensitivity to distributional assumptions.

Suggested Citation

  • Li Xiaohong & Foulkes Andrea S & Yucel Recai M. & Rich Stephen M., 2007. "An Expectation Maximization Approach to Estimate Malaria Haplotype Frequencies in Multiply Infected Children," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-19, November.
    • Handle: RePEc:bpj:sagmbi:v:6:y:2007:i:1:n:33
    DOI: 10.2202/1544-6115.1321
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    Cited by:

    1. X. Li & B. N. Thomas & S. M. Rich & D. Ecker & J. K. Tumwine & A. S. Foulkes, 2009. "Estimating and testing haplotype–trait associations in non‐diploid populations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 663-678, December.
    2. Amanda Ross & Cristian Koepfli & Xiaohong Li & Sonja Schoepflin & Peter Siba & Ivo Mueller & Ingrid Felger & Thomas Smith, 2012. "Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-7, August.

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