Parasitology Research

, Volume 116, Issue 7, pp 1855–1861 | Cite as

Evolutionary processes in populations of Cryptosporidium inferred from gp60 sequence data

  • Juan C. Garcia-REmail author
  • David T. S. Hayman
Original Paper


Cryptosporidiosis is one of the most common human infectious diseases globally. The gp60 gene has been adopted as a key marker for molecular epidemiological investigations into this protozoan disease because of the capability to characterize genotypes and detect variants within Cryptosporidium species infecting humans. However, we know relatively little about the potential spatial and temporal variation in population demography that can be inferred from this gene beyond that it is recognized to be under selective pressure. Here, we analyzed the genetic variation in time and space within two putative populations of Cryptosporidium in New Zealand to infer the processes behind the patterns of sequence polymorphism. Analyses using Tajima’s D, Fu, and Li’s D* and F* tests show significant departures from neutrality in some populations and indicate the selective maintenance of alleles within some populations. Demographic analyses showed distortions in the pattern of the genetic variability caused by high recombination rates and population expansion, which was observed in case notification data. Our results showed that processes acting on populations that have similar effects can be distinguished from one another and multiple processes can be detected acting at the same time. These results are significant for prediction of the parasite dynamics and potential mechanisms of long-term changes in the risk of cryptosporidiosis in humans.


Demography Expansion New Zealand Recombination Selection 



The first author (JCGR) thanks the New Zealand Ministry of Health for support. m EpiLab members provided useful discussions on different stages of the study.

Supplementary material

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Table S1 (DOCX 13 kb)
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Figure S1 Mismatch distribution analyses showing the frequencies of pairwise differences of C. hominis in Auckland 2012 (A), Auckland 2013 (B), Auckland 2014 (C), Auckland 2015 (D), and Canterbury 2014 (E). The observed distributions (red dotted line) are compared to the expected distribution (black solid line) under a model of sudden expansion. X-axis: number of pairwise differences, Y-axis: frequency. (JPEG 3507 kb)
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Figure S2 Mismatch distribution analyses showing the frequencies of pairwise differences of C. parvum in Auckland 2010 (A), Auckland 2011 (B), Auckland 2012 (C), Auckland 2013 (D), Auckland 2014 (E), Auckland 2015 (F), Canterbury 2010 (G), Canterbury 2013 (H), Canterbury 2014 (I) and Canterbury 2015 (J). The observed distributions (red dotted line) are compared to the expected distribution (black solid line) under a model of sudden expansion. X-axis: number of pairwise differences, Y-axis: frequency. (JPEG 5661 kb)
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ESM 1 (NEX 113 kb)
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ESM 2 (NEX 44 kb)
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ESM 4 (NEX 61 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Molecular Epidemiology and Public Health Laboratory, Hopkirk Research InstituteMassey UniversityPalmerston NorthNew Zealand

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