Inferring Strain Mixture within Clinical Plasmodium falciparum Isolates from Genomic Sequence Data |
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| Authors: | John D O’Brien Zamin Iqbal Jason Wendler Lucas Amenga-Etego |
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| Institution: | 1Mathematics Department, Bowdoin College, Brunswick, Maine, United States of America;2Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom;3Pacific Northwest National Laboratory, Richland, Washington, United States of America;4Navrongo Health Research Centre, Navrongo, Upper East Region, Ghana;Temple University, UNITED STATES |
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| Abstract: | We present a rigorous statistical model that infers the structure of P. falciparum mixtures—including the number of strains present, their proportion within the samples, and the amount of unexplained mixture—using whole genome sequence (WGS) data. Applied to simulation data, artificial laboratory mixtures, and field samples, the model provides reasonable inference with as few as 10 reads or 50 SNPs and works efficiently even with much larger data sets. Source code and example data for the model are provided in an open source fashion. We discuss the possible uses of this model as a window into within-host selection for clinical and epidemiological studies. |
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